﻿ The SAGE Handbook of Social Media Research Methods | Luke Sloan, Anabel Quan-Haase | download
Principal The SAGE Handbook of Social Media Research Methods

The SAGE Handbook of Social Media Research Methods

,
The SAGE Handbook of Social Media Research Methods offers a step-by-step guide to overcoming the challenges inherent in research projects that deal with "big and broad data," from the formulation of research questions through to the interpretation of findings. This handbook includes chapters on specific social media platforms such as Twitter, Sina Weibo, and Instagram, as well as a series of critical chapters.

The holistic approach is organized into the following sections:
  • Conceptualizing & Designing Social Media Research
  • Collection & Storage
  • Qualitative Approaches to Social Media Data
  • Quantitative Approaches to Social Media Data
  • Diverse Approaches to Social Media Data
  • Analytical Tools
  • Social Media Platforms
The SAGE Handbook of Social Media Research Methods is the single most comprehensive resource for any scholar or graduate student embarking on a social media project.
Categories: Computers\\Media
Année: 2017
Langue: english
Pages: 708
ISBN 10: 1473916321
ISBN 13: 9781473916326
File: PDF, 55.11 MB

You may be interested in

 

Most frequetly terms

 
 
You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.
1

Will Eisner: um sonhador nos quadrinhos

Year: 2013
Language: portuguese
File: EPUB, 6.08 MB
2

Master Builders of the Middle Ages

Year: 2016
Language: english
File: EPUB, 19.75 MB
The SAGE Handbook of

Social Media
Research Methods

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 1

08/12/16 3:52 PM

SAGE was founded in 1965 by Sara Miller McCune to support
the dissemination of usable knowledge by publishing innovative
and high-quality research and teaching content. Today, we
publish over 900 journals, including those of more than 400
learned societies, more than 800 new books per year, and a
growing range of library products including archives, data, case
studies, reports, and video. SAGE remains majority-owned by
our founder, and after Sara’s lifetime will become owned by
a charitable trust that secures our continued independence.
Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 2

08/12/16 3:52 PM

The SAGE Handbook of

Social Media
Research Methods

Edited by

Luke Sloan and
Anabel Quan-Haase

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 3

08/12/16 3:52 PM

SAGE Publications Ltd
1 Oliver’s Yard
55 City Road
London EC1Y 1SP
SAGE Publications Inc.
2455 Teller Road
Thousand Oaks, California 91320
SAGE Publications India Pvt Ltd
B1/I1 Mohan Cooperative Industrial Area
Mathura Road
New Delhi 110044
SAGE Publications Asia-Pacific Pte Ltd
3 Church Street
#10-04 Samsung Hub
Singapore 049483

Editor: Mila Steele
Assistant Editor: Matthew Oldfield
Production Editor: Sushant Nailwal
Copyeditor: Cenveo Publisher Services
Proofreader: Cenveo Publisher Services
Indexer: Cenveo Publisher Services
Marketing Manager: Sally Ransom
Cover design: Wendy Scott
Typeset by Cenveo Publisher Services
Printed in the UK

Introduction, Conclusion and editorial arrangement © Luke Sloan
and Anabel Quan-Haase 2017
Chapter 2 © Lori McCay-Peet and
Anabel Quan-Haase 2017
Chapter 3 © Rob Kitchin 2017
Chapter 4 © Anabel Quan-Haase
and Lori McCay-Peet 2017
Chapter 5 © Kelsey Beninger
2017
Chapter 6 © Shuzhe Yang,
Anabel Quan-Haase, Andrew D.
Nevin and Yimin Chen 2017
Chapter 7 © Luke Sloan 2017
Chapter 8 © Philipp Mayr and
Katrin Weller 2017
Chapter 9 © David M. Brown,
Adriana Soto-Corominas, Juan
Luis Suárez and Javier de la
Rosa 2017
Chapter 10 © Dietmar Janetzko
2017
Chapter 11 © Alex Voss, Ilia Lvov
and Sara Day Thomson 2017
Chapter 12 © Janet Salmons
2017
Chapter 13 © Guillaume
Latzko-Toth, Claudine Bonneau
and Mélanie Millette 2017
Chapter 14 © Martin Hand 2017
Chapter 15 © Diane Rasmussen
Pennington 2017
Chapter 16 © Bonnie Stewart
2017
Chapter 17 © Alexandra
Georgakopoulou 2017
Chapter 18 © Olga Buchel and
Diane Rasmussen Pennington
2017
Chapter 19 © Shadi GhajarKhosravi and Mark Chignell 2017

Chapter 20 © Niels Buus Lassen,
Lisbeth La Cour and Ravi Vatrapu
2017
Chapter 21 © Victoria L. Rubin
2017
Chapter 22 © Nadav Hochman
2017
Chapter 23 © Frauke Zeller 2017
Chapter 24 © Jack Jamieson and
Jeffrey Boase 2017
Chapter 25 © Bernhard Klein and
Ulf-Dietrich Reips 2017
Chapter 26 © Jeffrey Morgan
2017
Chapter 27 © Ulf-Dietrich Reips
and Pablo Garaizar 2017
Chapter 28 © Simon Hegelich
2017
Chapter 29 © Kalina Bontcheva
2017
Chapter 30 © Anatoliy Gruzd,
Philip Mai and Andrea Kampen
2017
Chapter 31 © Daniel Angus 2017
Chapter 32 © Mike Thelwall 2017
Chapter 33 © Dhiraj Murthy 2017
Chapter 34 © Linnea Laestadius
2017
Chapter 35 © Xiao Hu, Chen
Qiao and King-wa Fu 2017
Chapter 36 © Matthew J. Williams
and Martin Chorley 2017
Chapter 37 © Jessica Vitak
2017
Chapter 38 © Anatoliy Gruzd and
Ben O’Bright 2017
Chapter 39 © Luke Sloan and
Anabel Quan-Haase 2017

Apart from any fair dealing for the purposes of research or private
study, or criticism or review, as permitted under the Copyright,
Designs and Patents Act, 1988, this publication may be reproduced,
stored or transmitted in any form, or by any means, only with the prior
permission in writing of the publishers, or in the case of reprographic
reproduction, in accordance with the terms of licences issued by
the Copyright Licensing Agency. Enquiries concerning reproduction
outside those terms should be sent to the publishers.
At SAGE we take sustainability seriously.
Most of our products are printed in the UK
using FSC papers and boards. When we
print overseas we ensure sustainable papers are used as measured by the PREPS
grading system. We undertake an annual
audit to monitor our sustainability.

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 4

Library of Congress Control Number: 2016940832
British Library Cataloguing in Publication data
A catalogue record for this book is available from the British Library
ISBN 978-1-4739-1632-6

08/12/16 3:52 PM

Contents
List of Figuresix
List of Tablesxv
Notes on the Editors and Contributorsxvii
Acknowledgementsxxviii
1

Introduction to the Handbook of Social Media Research Methods: Goals,
Challenges and Innovations
Anabel Quan-Haase and Luke Sloan

PART I
2

CONCEPTUALISING & DESIGNING SOCIAL MEDIA RESEARCH

What is Social Media and What Questions Can Social Media Research
Help Us Answer?
Lori McCay-Peet and Anabel Quan-Haase

3

Big Data – Hype or Revolution?
Rob Kitchin

4

Building Interdisciplinary Social Media Research Teams: Motivations,
Challenges, and Policy Frameworks
Anabel Quan-Haase and Lori McCay-Peet

5

Social Media Users’ Views on the Ethics of Social Media Research
Kelsey Beninger

6

The Role of Online Reputation Management, Trolling, and
Personality Traits in the Crafting of the Virtual Self on Social Media
Shuzhe Yang, Anabel Quan-Haase, Andrew D. Nevin and Yimin Chen

7

Social Science ‘lite’? Deriving demographic proxies from Twitter
Luke Sloan

PART II
8

9

COLLECTION & STORAGE

Think Before You Collect: Setting Up a Data Collection Approach for
Social Media Studies
Philipp Mayr and Katrin Weller
Overview – The Social Media Data Processing Pipeline
David M. Brown, Adriana Soto-Corominas, Juan Luis Suárez and
Javier de la Rosa

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 5

1

11

13

27

40

57

74

90

105

107

125

08/12/16 3:52 PM

vi

The SAGE Handbook of Social Media Research Methods

10

The Role of APIs in Data Sampling from Social Media
Dietmar Janetzko

146

11

Data Storage, Curation and Preservation
Alex Voss, Ilia Lvov and Sara Day Thomson

161

12

Using Social Media in Data Collection: Designing Studies with the
Qualitative E-Research Framework
Janet Salmons

PART III
13

QUALITATIVE APPROACHES TO SOCIAL MEDIA DATA

Small Data, Thick Data: Thickening Strategies for Trace-based
Social Media Research
Guillaume Latzko-Toth, Claudine Bonneau and Mélanie Millette

177

197

199

14

Visuality in Social Media: Researching Images, Circulations and Practices
Martin Hand

215

15

Coding of non-text data
Diane Rasmussen Pennington

232

16

Twitter as method: Using Twitter as a tool to conduct research
Bonnie Stewart

251

17

Small Stories Research: A Narrative Paradigm for the Analysis of Social Media
Alexandra Georgakopoulou

266

PART IV

QUANTITATIVE APPROACHES TO SOCIAL MEDIA DATA

283

18

Geospatial Analysis
Olga Buchel and Diane Rasmussen Pennington

285

19

Pragmatics of Network Centrality
Shadi Ghajar-Khosravi and Mark Chignell

309

20

Predictive Analytics with Social Media Data
Niels Buus Lassen, Lisbeth la Cour and Ravi Vatrapu

328

21

Deception Detection and Rumor Debunking for Social Media
Victoria L. Rubin

342

PART V
22

DIVERSE APPROACHES TO SOCIAL MEDIA DATA

365

From Site-specificity to Hyper-locality: Performances of Place in
Social Media
Nadav Hochman

367

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 6

08/12/16 3:52 PM

Contents

23

Analyzing Social Media Data and Other Data Sources:
A Methodological Overview
Frauke Zeller

vii

386

24	Listening to Social Rhythms: Exploring Logged Interactional Data
through sonification
Jack Jamieson and Jeffrey Boase

405

25

421

Innovative Social Location-aware Services for Mobile Phones
Bernhard Klein and Ulf-Dietrich Reips

PART VI

Research and Analytical Tools	

439

26

COSMOS: The Collaborative On-line Social Media Observatory
Jeffrey Morgan

441

27

Social Lab: An ‘Open Source Facebook’
Ulf-Dietrich Reips and Pablo Garaizar

475

28

R for Social Media Analysis
Simon Hegelich

486

29

GATE: An Open-source NLP Toolkit for Mining Social Media
Kalina Bontcheva

499

30

A how-to for using Netlytic to collect and analyze social media data:
A case study of the use of Twitter during the 2014 Euromaidan
Revolution in Ukraine
Anatoliy Gruzd, Philip Mai and Andrea Kampen

513

31

Theme Detection in Social Media
Daniel Angus

530

32

Sentiment Analysis
Mike Thelwall

545

PART VII

SOCIAL MEDIA PLATFORMS

557

33

The Ontology of Tweets: Mixed-Method Approaches to the Study of Twitter
Dhiraj Murthy

559

34

Instagram
Linnea Laestadius

573

35

Weibo
Xiao Hu, Chen Qiao and King-wa Fu

593

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 7

08/12/16 3:52 PM

viii

The SAGE Handbook of Social Media Research Methods

36

Foursquare
Matthew J. Williams and Martin Chorley

610

37

Facebook as a Research Tool in the Social and Computer Sciences
Jessica Vitak

627

38

Big Data and Political Science: The Case of VKontakte and the 2014
Euromaidan Revolution in Ukraine
Anatoliy Gruzd and Ben O’Bright

645

A Retrospective on State of the Art Social Media Research Methods:
Ethical decisions, big-small data rivalries and the spectre of the 6Vs
Luke Sloan and Anabel Quan-Haase

662

39

Index673

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 8

08/12/16 3:52 PM

List of Figures
2.1	A model of social media engagement in context
7.1	The geographic distribution of geocoded tweets in the UK
and Ireland from a sample of 13 million
7.2	Comparison of age profile of UK Twitter users against the 2011 Census
7.3	Comparison of NS-SEC breakdown for Twitter users and the 2011 Census
8.1	Schematic of the technical architecture of SODATO
8.2	Schematic of the technical architecture of a Twitter TweetObserver
9.1
Basic Twitter entities and their attributes
9.2
A simple conceptual model of the Twitterverse
9.3
The final conceptual model of the Twitterverse
9.4
SylvaDB’s type creation form
9.5
SylvaDB’s property graph schema creation interface
9.6	The Twitterverse conceptual model mapped to a property
graph schema with SylvaDB
9.7
OpenRefine’s custom facet creation tool
10.1	Major access control schemes in social media
10.2	Basic HTTP authentication (RFC 2617)
10.3	Authorization flow of OAuth 1.0a (RFC 5849)
10.4	Authorization flow of via OAuth 2.0 (RFC 6749)
10.5	Twitter’s public APIs and endpoints
11.1	The Digital Curation Centre’s Data Curation Model
12.1	The Qualitative E-Research Framework
12.2	Ethical issues in Qualitative E-Research Design
12.3	Level of structure
15.1	The author’s ‘happy’ dog
15.2	Glencoe, Scotland
18.1	Geospatial Analysis pipeline
18.2	A visualization of three different methods for measuring
flu prevalence in the US
18.3	Example of a map as a retrieval tool
18.4	Health data over time as a linear plot graph and as a cyclic spiral
18.5	Distribution of counts of images linked to different locations
18.6	Classification control
18.7	Map showing Craigslist market territories
18.8	Standard Deviational Ellipse
18.9	Real time tweet map
18.10	Sample GeoTime story snapshot
18.11 Network topology represented on a map
19.1	An example ego network surrounding the ego node. Alter nodes
or neighbors are in dark grey and the rest of the network outside of the ego
network is shaded in light grey

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 9

22
93
96
101
118
119
128
129
130
131
132
133
137
150
151
152
153
154
164
180
186
187
235
241
289
291
293
294
295
295
297
298
299
300
301
314

08/12/16 3:52 PM

x

The SAGE Handbook of Social Media Research Methods

19.2	List of variables for the structural analysis of social networks
19.3	Network topology variables before and after the factor analysis
19.4	The three clusters with their members labelled by their names
19.5	Three samples of networks from Cluster 2 and Cluster 3. The first row
presents the visualized graphs of the six networks. The second row presents
their clustering coefficient distribution histogram. The third row presents the
scatter plots of Page Rank centrality vs. Eigenvector centrality
20.1	Predictive model of iPhone sales from Twitter data
20.2	Predictive model of H&M revenues from Facebook data
21.1	Summary of Zhou et al’s (2004) Linguistic Features for Deception Detection.
Twenty seven linguistic-based features, amenable to automation, were
grouped into nine linguistic constructs: quantity, complexity, uncertainty,
nonimmediacy, expressivity, diversity, informality, specificity, and affect.
All the linguistic features are defined in terms of their measurable dependent
variables
21.2	Conceptualization of the Components of Big Data Veracity. Veracity – as the 4th
V in addition to Volume, Velocity and Variety – portrayed across three primary
orthogonal dimensions in the conceptual space – objectivity, truthfulness,
credibility. The dimensions intersect in the center and the nebula represents
a certain degree of variability within the phenomena that together constitute
the big data veracity. Secondary dimensions of lesser concern in textual data
are presented in dotted lines. The tree main components of veracity index
are normalized to the (0,1) interval with 1 indicating maximum objectivity,
truthfulness and credibility, and 0, otherwise. Then, the big data veracity index
is calculated as an average of the three, assuming that each of the dimensions
equally contributes to veracity establishment. The three primary dimensions
reduce ‘noise’ and potential errors in subsequent inferences from the textual big
data due to minimization of bias, intentional misinformation, and implausibility
21.3	Verification feature for rumor debunking on Twitter (Liu et al., 2015). The
six proposed categories of verification features largely based on insights from
journalists
21.4	Rumor busting features for Sina Weibo Microblogs (Yang, 2012).
Grouped into five broad types (content-based, client-based, account-based,
propagation-based, and location-based), rumor detection features were
extracted on Sina Weibo, the Chinese leading micro-blogging platform, for
the binary classification purposes (rumor or not). Each predictive feature is
described in terms of its implementation
21.5	Examples of clickbait via Twitter (Chen, Conroy and Rubin, 2015
presentation). Two examples of clickbaits or online content that primarily
aims to attract attention and encourage visitors to click on a link to a
particular web page. The claims made in headlines of clickbaits and the
associated imagery are often outrageous and/or misleading, as the reader
finds out from reading the full message
21.6	Dashboard-Style Interface of the BotOrNot System (Davis et al., 2015). The
system evaluates the extent to which a Twitter account exhibits similarity to
the known characteristics of social bots
22.1	31 Instagram photos taken by Banksy documenting his month long artistic
residency in the streets of New York City during October 2013. The photos

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 10

319
320
321

323
337
338

347

350
352

353

357
359

08/12/16 3:52 PM

List of Figures

are sorted by their upload date from 1–31 October 2013 (left to right,
top to bottom)
22.2	Elmgreen & Dragset (2014) The ‘Named Series’. Each frame consists of the
color layer of a wall from a number of prominent art institutions’ white cube
exhibition spaces (i.e. Centre Pompidou, Guggenheim, Tate Liverpool and
others). The removed layers are mounted on canvas and framed in a black
waxed oak frame. Photo by Anders Sune Berg (Installation view as part of
the exhibition Biography, Astrup Fearnley Museum, Oslo)
22.3	Robert Smithson (1970) Spiral Jetty. Sculpture. Rozel Point,
Great Salt Lake, Utah
22.4	The nomadic is turn into native: A comparison of Instagram photos taken
at Tate Modern, London (left) and the Museum of Modern Art (MoMA),
NYC. Each radial visualization contains 10,000 photos actively tagged to the
location of the museum by its users. The photos are sorted by saturation mean
and brightness mean
22.5	Instagram photos of 7 of Banksy’s artworks used in our case study (selected
from the larger set of photos for each artwork). Top: original photo posted by
Banksy. Bottom: a montage of 4 photos taken by other users
22.6	Montage visualization of all photos from each cluster sorted by time from
no.1–7 (top to bottom). Each cluster includes the following number of photos:
Cluster 1: 575 photos. Cluster 2: 704 photos. Cluster 3: 783 photos. Cluster 4:
638 photos. Cluster 5: 267 photos. Cluster 6: 1,142 photos. Cluster 7: 449 photos
22.7	Number of photos annotated with the hashtags #banksy and #banksyny (a
total of 28,419 photos) for each day from October 1st to November 20th 2013
22.8	A temporal plot of each cluster organized by time (X) and volume (Y)
22.9	A global spread of all geo-tagged images with the tags #banksyny or #banksy.
16,164 images are in NYC, and 2,571 images are outside of NYC area
22.10	A map of locations of all photos from our 7 clusters
(Only NYC area is shown)
22.11	Radial visualization of 16,164 Instagram photos geo-tagged to NYC area
between October 1st and November 20th, 2013. The photos are organized by
location (perimeter) and upload date and time (angle)
22.12	A matrix image plot visualization of 6 clusters. In each cluster, (X) –
brightness mean, (Y) – hue mean. A black square represents the original
photo of an artwork posted to Instagram by Banksy himself. Top row from
the left: cluster 1, cluster 2, cluster 3. Bottom row from the left: cluster 4,
cluster 6 and cluster 7
22.13	Visualization of cluster 2 (left) and cluster 1 (middle), sorted by hue mean
(X) and date (Y). Right panel shows a close up of cluster 1. The visualization
is rotated 90 degrees
23.1	The extended empirical research process in social media research
23.2	Extract from word frequencies calculation on Greenpeace Corpus, June 2012
23.3	Auto coding dialogue window in Atlas.ti
24.1	Screenshot of E-Rhythms Data Sonifier
25.1	MUGGES Application interfaces – a) muggesNote, b) muggesNote photo
view, c) muggesJournal, d) muggesTrail
25.2	Mugglet Creation Wizard
25.3	Study design overview

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 11

xi

368

371
372

374
375

376
377
377
378
378
379

380
381
390
399
400
414
424
425
426

08/12/16 3:52 PM

xii

The SAGE Handbook of Social Media Research Methods

25.4	a) Personal phone features b) Monthly spending on personal phone
25.5	a) Home locations b) Daily travel distance
25.6	Technical infrastructure
25.7	Comparison of first and long-term impression
25.8	Creation and editing hotspots in Bilbao. The circle size corresponds with the
number of similar events in the same location
25.9	a) Applied mugglet templates b) Observed mashup size
25.10	a) Measured provision duration b) Reasons to stop provision
25.11	Community size of correlated mugglets
25.12	a) Applied search techniques and b) preferred search topics
26.1	COSMOS aims to help social scientists move towards the top-right corner of
the spectrum of data and research-question-definition needs
26.2	The COSMOS software application is a tool that fits into broader socialscientific workflows
26.3	The COSMOS user interface is composed of three main areas: the Data Set
Library (1) the Data View Library (2) and the Workspace (3) Users build
visualizations by dragging and dropping data sets and data views onto the
Workspace, as shown by the arrows
26.4	COSMOS users engage in an iterative workflow of querying, visualizing and
refining data to explore their research questions
26.5	The COSMOS Start New Twitter Collection dialog enables users to collect
tweets from the one percent Twitter stream by either randomly sampling
tweets or by filtering tweets using keywords, hashtags and user accounts
26.6	The COSMOS collection snapshot controls enable users to monitor the
progress of an ongoing Twitter collection without stopping the collection.
Automated snapshots enable users to set up an event monitoring system
26.7	The COSMOS query dialog enables users to query Twitter data sets by
content, demographics, sentiment, time and geography
26.8	The COSMOS Choose Sampling Method dialog enables COSMOS to handle
large data sets with user-defined data sampling strategies
26.9	The COSMOS temporal filtering controls enable users to filter data sets by
time and replay events like a data movie
26.10	COSMOS provides a consistent method of creating selection data sets. Users
select data in a data view – here data is selected by clicking a segment in the
pie chart data view (1) – and dragging from the data-selection control (2) onto
the Data Set Library. The selection is added as a new data set under the data
set from which it was taken (3)
26.11	The COSMOS Export Data dialog supports the use of other software
by providing extensive facilities for exporting data in a wide variety of
commonly used formats including CSV, Microsoft Excel and JSON	
26.12	A combination of multiple-coordinated visualizations enables users to
understand data sets more deeply than they could with any single
visualization. Here, the map, word cloud and frequency data views combine to
help users understand a data set concerning the 2013 bombing of the Boston
Marathon
26.13	The table data view presents a data set as a multi-column table. Binding the
font size and the foreground and background colours of each row enables the
table to visualize data

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 12

428
429
429
431
433
433
434
435
435
443
444

445
447
450
451
452
454
455

456
456

459
459

08/12/16 3:52 PM

List of Figures

26.14	The text data view presents a plain-text view of a single data column. Here, a
text data view supports the map data view by enabling users to click on a map
marker that represents a geocoded tweet to reveal the text of the tweet
26.15	The word cloud data view sizes the words in a text column based on their
frequency across all the rows in the table
26.16	The frequency analysis data view provides interactive frequency views at
three levels of temporal granularity: days, hours and minutes
26.17	The network analysis data view presents a social network graph created from
retweets or mentions. The network is laid out automatically with a forcedirected layout algorithm
26.18	The map data view plots geocoded data as markers on a map provided by
OpenStreetMap
26.19	The shapefile data view plots geocoded data as markers on shapefiles of the
UK and its constituent territories: England, Wales, Scotland, Northern Ireland
and the London boroughs
26.20	The polygons in the shapefile data view are shaded by 2011 UK Census data
retrieved from the Office of National Statistics via the ONS API
26.21	The pie chart data view segments data into two columns: one column
provides the data that sizes the angles of each segment; the other column
provides the labels for the segments
26.22	The line chart data view plots the values in the numeric data columns against
the temporal values in the timestamp column
26.23	COSMOS data view plugin developers have three opportunities to integrate
new data views into the COSMOS application framework: when users drag
a data set onto the new data view (1); when users select data in another data
view (2); and when users select data in the new data view (3)
26.24	The COSMOS high-performance computing prototype uses the MongoDB
database and Hadoop’s distributed Map/Reduce algorithm to investigate how
COSMOS can scale to meet the challenges of big data
27.1	Analysis of searches for the terms ‘Web 2.0’ and ‘social media’ over time in
Google Trends
27.2	Main display of Social Lab
27.3	Simple social bot for Social Lab
27.4	Setting up triggers and routines for a social bot in Social Lab’s backend
application
27.5	Three slightly different social bots to gather participants for
an online promotion
27.6	Messages can be deployed by researchers or social bots or to provoke user
actions
28.1	Diagnostic plots
28.2	Tweets in California
28.3	Spacial distribution of hashtags
29.1
The GATE developer interface
29.2
The TwitIE information extraction pipeline
29.3
Classication interface: sense disambiguation example
29.4
Sequential selection interface: named entity recognition example
29.5
Mimir dynamic co-occurrence matrix
30.1	Frequently used words in the three datasets (after removing ‘stopwords’)

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 13

xiii

460
461
461
462
463
464
465
466
467

468
470
476
478
479
480
481
482
494
496
497
501
502
506
507
508
519

08/12/16 3:52 PM

xiv

The SAGE Handbook of Social Media Research Methods

30.2	Frequently used word in the three datasets displayed over time (after
removing ‘stopwords’)
30.3	Name vs chain ties derived from sample Tweets
30.4	Communication networks showing who replies to whom on Twitter
(chain network)
30.5	‘Communities’ automatically detected by netlytic in the ‘english’ dataset
30.6	Viewing direct connections of a selected node (marked as #2)
30.7	Top ten most connected users based on in-degree centrality
31.1	The Leximancer graphical user interface
31.2	Leximancer concept map. The map highlights the theme groups using the
large coloured circles, and the individual nodes represent the concepts. Node
proximity implies conceptual relatedness
31.3	This figure highlights the interactive nature of Leximancer and how an
analyst can select a concept to interrogate and reveal the raw data that aligns
with that concept
31.4	This figure highlights the nature of the evidence terms that invoke a specific
concept. The term weightings can be seen here as they relate to the concept
‘citizenship’
31.5	The Streamgraph reveals the prominence of themes on Twitter over time
31.6	The Gephi plot reveals networks of conceptual overlap between Tweeters,
and can be useful in locating groups of topically aligned Tweeters
33.1	Theory building
33.2	Data collection and relationship model
33.3	Continuous open coding Twitter data model applied to #accidentalracist
33.4	Operationalization of Twitter coding model
35.1	Screenshot of Weibo interface
35.2	Screenshot of Weibo search
35.3	Workflow of applying the Open API for Weibo data collection
35.4	Data flow of a Sina Weibo crawler
35.5	Screenshot of the online Weibo data crawler
35.6	Fragment of a coding scheme and results
35.7	A chinese text string on Weibo (translated as ‘AlphaGo defeated
Lee Se-dol, but artificial intelligence has not conquered human being’)
35.8	Segmented words and part-of-speech tagging (by the ICTCLAS tool)
35.9	The process of a classification task with three classifiers
35.10	Weibo topic dynamics with identified significant points (reprinted from Fan
et al., 2014)
35.11	User network constructed with co-mentioned relations on keywords
36.1	A conceptual overview of Foursquare
36.2	The Foursquare mobile app interface (2016)
36.3	The Swarm mobile app interface (2016)
38.1	Country-to-Country networks

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 14

520
522
523
524
525
526
535
536
539
540
541
542
564
566
566
567
594
595
596
597
598
599
600
600
601
602
603
612
614
614
650

08/12/16 3:52 PM

List of Tables
2.1	Selection of social media definitions from the research literature
organised by themes
2.2	Types of social media
3.1	Comparing small and big data
8.1	Selected attributes of tweets available in JSON format
10.1	Use of Facebook’s graph API via R
10.2	Access levels of Twitter’s streaming endpoint
10.3	Use of Twitter’s REST API via twitter
10.4	Use of Twitter’s streaming API via streamR
11.1	Data management and curation guidance
12.1	Selecting research types, communication features and social media sites
12.2	Characteristics of qualitative exemplars
15.1	Overview of research methods for non-text social media data
19.1	A summary of generic measures for 62 social networks
19.2	Means of clusters in homogenous subsets are displayed. Cluster 1 & 2 make
one homogenous subset together for the ClsCff.PgRk and ClsCff.EigV
variables
20.1	Categorization of research publications on predictive analytics with social
media data
20.2	Overview of dataset
21.1	Deception research disciplines and associated data type examples. Various
contemporary disciplines that study deception for the purpose of its detection
are listed with corresponding typical data types that have been studied in
the recent past. The data type distinctions are made based on the mode of
obtaining data and the predominant discourse variety in the data type. Both
columns are non-exhaustive
21.2	Blog credibility assessment factors with additional details added
from the associated presentation
23.1	Methods overview
23.2	Social media monitoring and analytics in commercial market analysis
23.3	Case study corpus overview
24.1	Mean and median response times among sonified sample
24.2	Hypotheses generated through sonification compared to results of statistical
analysis
25.1	Comparison of analysis tools
25.2	Questionnaire items with underlying dimensions and indicators
30.1	Case study datasets
30.2	Definitions of common network-level SNA measures
31.1	Thematic overview of the Twitter corpus. All examples provided here
originally contained the #qanda hashtag which has been omitted for brevity
33.1
Three sample cancer related Twitter topic clusters

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 15

16
18
28
120
154
156
157
158
163
182
192
235
318
322
334
337

349
355
392
394
397
416
423
432
524
537
538
619
569

08/12/16 3:52 PM

xvi

The SAGE Handbook of Social Media Research Methods

38.1	Study dataset description
38.2	Top ranked countries based on the number of group members
38.3	Top ranked countries based on the degree centrality
38.4	Immigration receptor states

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 16

649
649
651
652

08/12/16 3:52 PM

Notes on the Editors
and Contributors
THE EDITORS
Luke Sloan is a Senior Lecturer in Quantitative Methods and Deputy Director of the Social
Data Science Lab (http://socialdatalab.net/) at the School of Social Sciences, Cardiff University, UK. Luke has worked on a range of projects investigating the use of Twitter data for
understanding social phenomena covering topics such as election prediction, tracking (mis)
information propagation during food scares and ‘crime-sensing’. His research focuses on
the development of demographic proxies for Twitter data to further understand who uses the
platform and increase the utility of such data for the social sciences. He sits as an expert
member on the Social Media Analytics Review and Information Group (SMARIG) which
brings together academics and government agencies. Luke’s most recent work is focused on
exploring the possibility gaining informed consent for linking Twitter content to survey
data.
Anabel Quan-Haase is an Associate Professor and holds a joint appointment at the Faculty of
Information and Media Studies and the Department of Sociology, the University of Western
Ontario. She is the director of the SocioDigital Lab and her research interests focus on how
people integrate social media into their everyday lives and work settings. Her particular focus
is on user engagement and the role of social context in how individuals use and make sense of
messages and interactions on social media. Dr Quan-Haase is the author of Technology and
Society (2015, 2nd ed. with Oxford University Press) and Information Brokering in the HighTech Industry (2009 with Lambert). She is the past president of the Canadian Association for
Information Science and a Council Member of the CITAMS section of the American Sociological Association.

THE CONTRIBUTORS
Daniel Angus is a lecturer in Computational Social Science at The University of Queensland,
and leads the Discursis computational speech and language analysis project funded by the
Australian Research Council’s Centre of Excellence for the Dynamics of Language. His
research focuses on the development of information visualization and analysis methods for
communication data, with a specific focus on conversation data.
Kelsey Beninger is a Associate Director in the Qualitative Research team at the independent
social research agency, TNS-BMRB, based in London, England. Previously with NatCen
Social Research, her research interests include the ethical implications of social media and

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 17

08/12/16 3:52 PM

xviii

The SAGE Handbook of Social Media Research Methods

online research on researcher practice and participants. Kelsey is also a contributing member
to the network of methodological innovation, New Social Media, New Social Science.
Jeffrey Boase is an Associate Professor in the Institute of Communication, Culture, Information and Technology and the Faculty of Information at the University of Toronto. His research
focuses on the relationship between communication technology and personal networks. He is
particularly interested in how emerging technologies such as smartphones and social media
platforms may enable or hinder the transfer of information and support within personal networks. In recent years he has incorporated digital trace data (so-called ‘Big Data’) into his
project designs, merging it with more traditional survey and interview data.
Claudine Bonneau is Associate Professor in the Department of Management and Technology
at Université du Québec à Montréal (UQAM), where she is a member of the Laboratory on
Computer-Mediated Communication (LabCMO) and teaches in graduate and undergraduate
programs in Information Technology. Her current work focuses on social media uses and online
collaboration practices at work. She is also interested in methodological issues related to qualitative research and online ethnography. Besides her contributions to edited books, her work has
been published in the International Journal of Project Management (2014), tic&société (2013)
and other French-language publications.
Kalina Bontcheva (University of Sheffield) is leading a research team of nine RAs and five
PhD students, working on five projects on Information Extraction for social media, including
an EPSRC career acceleration fellowship (EP/I004327/1) and the SoBigData and OpenMinTeD H2020 eInfrastructure projects. Her research lies at the intersection of Natural Language Processing (NLP), web science, and computational social media analysis. Prof.
Bontcheva is also one of the leading researchers and developers behind the world leading
GATE infrastructure. GATE has had over 44,000 downloads in 2014 alone, nine annual
summer schools for researchers and companies, and over 3,800 citations of the eight key GATE
papers on Google Scholar. GATE is used in many products and services, including: Financial
Times, the BBC, Fizzback (now NICE); the UK National Archives; the Press Association;
Nesta, the British Library, Text Mining Solutions, and Ontotext.
David M. Brown is a PhD candidate in the Department of Modern Languages and Literatures
at Western University. He received his BA from the University of Oregon, where he graduated
Summa Cum Laude. His dissertation focuses on data-driven approaches to understanding book
history in the Spanish Empire. As an RA at the CulturePlex Lab, he is part of the core data
analytics team, specializing in network analysis, software development, and graph databases.
He is also an active Python programming language community developer, and maintains several projects designed to interact with graph database servers.
Olga Buchel is an interdisciplinary researcher specializing in geovisualization, information visualization, visual analytics, and geographic information retrieval. She holds a PhD
in Library and Information Science from Western University. In the past she has worked at
the Alexandria Digital Library in Santa Barbara, CA, the forerunner of Google Maps.
Olga’s most recent publications are about geospatial analysis of user interactions with
Flickr image collections, and the role of interactive exploratory visualizations in research
and knowledge discovery in public health and international business. She has presented her
research at a number of international conferences, including GEOMED, iConference,

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 18

08/12/16 3:52 PM

Notes on the Editors and Contributors

xix

Annual Meetings of the Association for Information Science and Technology, visualization
workshop at the American Academy of Management, workshop on Provenance of Sensemaking at IEEE VIS, and other conferences. She currently works with Big Data at SiTechnologyGroup, Inc., where she develops health applications and visualizations from clinical
health records.
Yimin Chen is a PhD candidate in the Faculty of Information and Media Studies at the University of Western Ontario and holds an MLIS from the same institution. Driven by a longstanding fascination with the internet and its cultures, Yimin’s PhD work revolves around
understanding how internet trolling is defined, portrayed, and discussed in print and online. His
other research interests include news and journalism, deception detection, information literacy,
and humour.
Mark Chignell is a Professor of Mechanical and Industrial Engineering, and a Professor in the
Faculty of Information, at the University of Toronto, where he has been on the faculty since
1990. Prior to that, he was an Assistant Professor in Industrial and Systems Engineering at the
University of Southern California from 1984 to 1990. He has a PhD in Psychology (specializing in mathematical psychology) from the University of Canterbury (New Zealand, 1981),
and an MS in Industrial and Systems Engineering (Ohio State, 1984). In addition to being
Director of the Interactive Media Lab, he is President of Vocalage Inc., a University of Toronto
spinoff company and a visiting scientist at both the IBM Centre for Advanced Studies and Keio
University in Japan. He is currently the director of the Knowledge Media Design Institute, and
BUL Chair in Human-Computer Interaction, at the University of Toronto.
Martin J. Chorley is a lecturer at the School of Computer Science & Informatics at Cardiff
University. He completed an MSc in High End Computing at Edinburgh University in 2007 and
received a PhD degree in computer science from Cardiff University in 2012. His research interests include mobile and social computing, and computational journalism.
Ian Colville is Professor of Organization and Management Studies at the University of Bath,
UK. His research interests lie theoretically in the areas of organizing and sensemaking combined with an abiding concern with their relevance to practice. Most recently he has been
studying organizing and sensemaking in complex and fast moving contexts, e.g. Counter Terrorism and Environmental Flooding, which perforce require a process perspective. His work
is published in journals such as Accounting, Organization and Society, Public Administration,
Human Relations, Management Learning, Organization Studies and Organization. He is on the
editorial board of Organization Studies and Human Relations.
Sara Day Thomson works for the Digital Preservation Coalition (dpconline.org) where she
researches new methods and technologies to support member organisations in ensuring longterm access to their digital data. She has also helped coordinate community-building and sustainability for the EU-funded project TIMBUS, which aligns digital preservation with business
continuity solutions. More recently, she has conducted a 15-month study into preserving forms
of Big Data in collaboration with the UK Data Service. She is the author of ‘Preserving Social
Media’, a DPC Technology Watch report that articulates the challenges of capturing and preserving user-generated content on web-based platforms. Her main interests include the preservation of web and social media content, community uptake of new methods and tools, and
practical case studies for implementing digital preservation.

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 19

08/12/16 3:52 PM

xx

The SAGE Handbook of Social Media Research Methods

Javier de la Rosa is a Postdoctoral fellow at the CulturePlex Lab, where he also works as Tech
Lead and Chief Developer. He holds a PhD in Hispanic Studies by the University of Western
Ontario (Canada), and both MSc in Artificial Intelligence and BSc in Computer Engineering
by the University of Seville (Spain). As a humanist and computer scientist, he combines his
knowledge in machine learning and data science with humanistic inquiries, focusing his
research on topics such as graph and network theory, computer vision, digital art history, sentiment analysis, and authorship attribution. Javier also has experience in the industry, where he
worked for over five years writing open source software, and doing web development and
research.
King-wa Fu is an Associate Professor at the Journalism and Media Studies Centre (JMSC),
The University of Hong Kong. His research interest includes political participation and media
use, computational media studies, mental health/suicide and the media, health communication,
and young people’s Internet use. He has a PhD from the JMSC, a MA in Social Sciences and
an MPhil in Engineering from the Hong Kong University of Science and Technology. He
obtained an undergraduate degree in Engineering from HKU. He was a journalist at the Hong
Kong Economic Journal.
Pablo Garaizar is a lecturer and researcher at the University of Deusto, Bilbao, Spain. He
holds a PhD in Computer Engineering and a BSc in Psychology. As a lecturer, he is specialized
in GNU/Linux, Computer Networks, Web Development and Computer Security. As a
researcher, he explores new ways of using Information and Communication Technologies to
enhance Learning. He is also actively involved in research projects regarding associative learning processes, biases in education, and Internet-based research.
Alexandra Georgakopoulou is Professor of Discourse Analysis & Sociolinguistics, King’s
College London. Drawing on sociocultural linguistic and interactional perspectives, she has
researched the role of everyday life stories (both face-to-face and on new/social media) in the
(re)formation of social relations. She has specifically investigated the links of narrative communication practices with youth and gender identities in the context of late modernity. She has
(co)-authored nine books which include Analyzing narrative: Discourse and sociolinguistic
perspectives (with Anna De Fina, 2012, Cambridge University Press) and she has (co)-edited
several Special Issues and edited volumes, including The Handbook of Narrative Analysis
(with Anna De Fina, 2015, Wiley-Blackwell) and The Routledge Handbook of Language &
Digital Communication (with Tereza Spilioti, 2016).
Shadi Ghajar-Khosravi is currently an NSERC visiting fellow at the Socio-Cognitive Systems section of DRDC-Toronto (Defence Research and Development Canada in Toronto). She
has a PhD in Mechanical and Industrial Engineering (human factors) from the University of
Toronto, and an MISt from the Faculty of Information at the University of Toronto. She was
an HCTP fellow from 2011 to 2013 and an IBM CAS research assistant from 2011 to 2012.
Her research interests include social network analysis, information systems design and analysis, and the user-centered design and evaluation of user interfaces.
Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate
Professor and Director of the Social Media Lab in the Ted Rogers School of Management at
Ryerson University (Toronto, Canada). He is also a co-editor of a multidisciplinary journal on
Big Data & Society. Dr Gruzd’s research initiatives explore how the advent of social media

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 20

08/12/16 3:52 PM

Notes on the Editors and Contributors

xxi

and the growing availability of social big data are changing the ways in which people communicate, collaborate and disseminate information and how these changes impact the social,
economic and political norms and structures of modern society.
Martin Hand is an Associate Professor of Sociology at Queen’s University, Canada. He is the
author of Ubiquitous Photography (Polity, 2012), Making Digital Cultures (Ashgate, 2008),
co-author of The Design of Everyday Life (Berg, 2007) and co-editor of Big Data? Qualitative
Approaches to Digital Research (Emerald, 2014). His current research interests are in the range
of temporal expectations and experiences emerging with increased use of smartphone and
wearable technologies.
Simon Hegelich is Professor for Political Data Science at the Technical University of Munich,
Bavarian School of Public Policy. He is focused in his research on the connection between political science and data science. Simon Hegelich is interested in the political dimension of the ongoing digital revolution as well as in implementing new methods in political science like machine
learning, data mining, computer vision and simulations. He has published – amongst others – in
Policy Studies Journal, Swiss Political Science Review and European Policy Analysis. He is
writing about political data science on his blog https://politicaldatascience.blogspot.de.
Xiao Hu is an Assistant Professor in the Division of Information and Technology Studies in
the Faculty of Education of the University of Hong Kong. Her research interests include
applied data/text mining, information retrieval, and social media for learning. Dr Hu holds a
PhD degree in Library and Information Science and a Master’s degree in Computer Science,
both from the University of Illinois at Urbana-Champaign. She also holds a Master’s degree in
Electronic Engineering from Beijing University of Posts and Telecommunications, China and
a Bachelor’s degree in Electronics and Information Systems from Wuhan University, China.
Jack Jamieson is a PhD student at the University of Toronto’s Faculty of Information. His
research examines how emerging communication technologies affect and are affected by cultural and social activities. In particular, his work examines the influence of social, cultural, and
political values on web development technologies. Other interests include user-generated
mobile literature and exploratory analysis using data sonification.
Dietmar Janetzko studied Psychology, Philosophy and Computer Science and holds a PhD both
in psychology and education. He is a Professor of business computing and business process management at Cologne Business School in Germany. His research interests focus on statistics, data
mining, and quantitative analysis of data sourced from the Internet and social media.
Andrea Kampen is a Research Collaborator with the Ryerson University Social Media Lab
and the Manager of Conference Publications of the International Conference on Social Media
& Society. She has graduated from the School of Information Management with a Masters in
Library and Information Management degree from Dalhousie University, Canada. Through her
work with the Social Media Lab, Andrea has developed expertise in social media analysis and
how to use various analytical tools like Netlytic to study what people are sharing and how they
are interacting with information online.
Bernhard Klein received his diploma degree in Computer Science from the Technical
University of Munich and a doctoral degree at the University of Vienna in Economic and

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 21

08/12/16 3:52 PM

xxii

The SAGE Handbook of Social Media Research Methods

Social Sciences. During his PhD studies at the University of Vienna he was a research assistant and lecturer for the Distributed and Multimedia Systems research group. In 2009, he
transferred during a research collaboration with the University of Deusto to Spain, where he
led as a postdoctoral researcher several European projects in the area of smart homes and
smart cities. In February 2014, he moved to the Singapore-ETH Centre (ETH Zuerich), where
he was responsible for the Value Lab Asia and headed research projects for urban modeling,
simulation and visualization. Since September 2015 he has worked as a project coordinator
for the Big Data – Informed Urban Design project. His research interests include big data
analytics, ambient intelligence, complex systems and multi-scale simulation.
Rob Kitchin is professor and ERC Advanced Investigator at the National University of Ireland
Maynooth. He is principal investigator of the Programmable City project, the Dublin Dashboard, the All-Island Research Observatory, and the Digital Repository of Ireland, and the
author of Code/Space: Software and Everyday Life (MIT Press, 2011) and The Data Revolution (Sage, 2014). He was the 2013 recipient of the Royal Irish Academy’s Gold Medal for the
Social Sciences.
Tetsuro Kobayashi (PhD, University of Tokyo) is an Associate Professor of Department of
Media & Communication at City University of Hong Kong. His research focuses on political
communication and mobile communication in East Asian countries.
Lisbeth la Cour is a Professor of Time Series Analysis at the Department of Economics,
Copenhagen Business School. Her current research topics are in the fields of big social data
analytics, FDI spill-over on productivity in European firms, educational economics, statistical
analysis of public procurement data in the EU and time series analysis of economic historical
data. In her research in big social media data analytics she focuses on the use of predictive
modeling and tries to look deeper into the basic time series properties of social media data and
their preprocessing. Also, she works on specification and evaluation of the predictive models
in relation to economic and financial outcomes of firms. Prof. la Cour holds a Doctor of Philosophy (PhD) in Economics from University of Copenhagen and a Master of Science in Economics (MSc) also from University of Copenhagen.
Linnea I. Laestadius is an Assistant Professor of Public Health Policy and Administration at
the Joseph J. Zilber School of Public Health at the University of Wisconsin-Milwaukee. She
holds a PhD from the Johns Hopkins Bloomberg School of Public Health and a Master of
Public Policy from George Washington University. Her research focuses on the intersections
of public health, society, and technology. She is particularly interested in the drivers and
effects of health-related posting behaviors on visual social media platforms, as well as the
ethics and public health implications of corporate solicitation and use of user-generated
content.
Niels Buus Lassen is PhD Fellow at the Department of IT Management of the Copenhagen
Business School, and researcher at the Computational Social Science Laboratory (http://cssl.
cbs.dk). His main research interests are predictive modelling with social media data. He has
built predictive models of sales for iPhone, H&M, Nike and others, based on Twitter and Facebook data, and a predictive model for Net Promoter Score based on Facebook feelings. He
holds a MSc in Economics and BSc in Economics/Mathematics from Copenhagen Business
School, and an MBA from Edinburgh Business School.

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 22

08/12/16 3:52 PM

Notes on the Editors and Contributors

xxiii

Guillaume Latzko-Toth is Associate Professor in the Department of Information and Communication at Université Laval (Quebec City, Canada) and codirector of the Laboratory on
Computer-Mediated Communication (LabCMO, www.labcmo.ca). Rooted in a Science and
Technology Studies (STS) perspective, his research and publications address the role of users
in the development of digital media, the transformations of publics and publicness, and methodological and ethical issues related to Internet research. Besides several contributions to
edited books, his work appeared in the Journal of Community Informatics (2006), the Bulletin
of Science, Technology and Society (2010), and the Canadian Journal of Communication
(2014).
Ilia Lvov is a PhD student in the School of Computer Science at the University of St Andrews
working under supervision of Dr Voss. He studies the issues that are brought into social
research by new forms of data and new research methods. Through collaboration with academics, journalists and NPOs, Ilia has participated in a number of research projects studying social
media data.
Philip Mai MA, JD, is the Manager of Academic Communications at Ryerson University in
Toronto, a member of the Office of Communications, Government and Community Engagement, the Office of the Provost and Vice President Academic. He is also the Research and
Communications Manager at the Ryerson Social Media Lab and a co-founder of the International Conference on Social Media & Society. In his work, Philip focuses on knowledge mobilization, information diffusion and social media analytics with the aim to connect people,
knowledge and ideas.
Philipp Mayr is a postdoctoral researcher and team lead at the GESIS department Knowledge
Technologies for the Social Sciences. He was the computer science project head in the PEP-TF
project mentioned in the abstract (see Kaczmirek et al., 2014) and responsible for the technical
collection infrastructure. Philipp Mayr has published in the areas Informetrics, Information
Retrieval and Digital Libraries.
Lori McCay-Peet is an Assistant Professor in the School of Information Management (SIM)
in the Faculty of Management at Dalhousie University. She has an interdisciplinary PhD
(Management and Computer Science), a Master of Library and Information Studies (MLIS),
and a BA (History). Dr McCay-Peet’s research focuses on people’s perceptions and uses of
digital information environments such as social media and digital libraries, particularly in the
context of knowledge work. She has published and presented her research in several information science and computer science publications and venues including the Journal of the
Association of Information Science and Technology, Information Research, Information Processing and Management, and the SIGCHI Conference on Human Factors in Computing
Systems.
Mélanie Millette is Assistant Professor of Social Media at Université du Québec à Montréal
(UQAM), and a member of the Laboratory on Computer-Mediated Communication (LabCMO).
Her work concerns social, political, and cultural aspects of social media uses, more specifically
how citizens mobilize online platforms to achieve political participation. She won a SSHRCArmand-Bombardier grant and a Trudeau Foundation scholarship for her thesis research which
examines media visibility options offered by online channels such as Twitter for francophone
minorities in Canada. She has co-edited a book on social media (Médias sociaux, PUQ, 2012,

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 23

08/12/16 3:52 PM

xxiv

The SAGE Handbook of Social Media Research Methods

in French). She has recently co-authored a chapter in Hashtag Publics (N. Rambukkana (ed.),
2015, Peter Lang). Her additional research interests include methodology, digital, and feminist
methods. Her work has been published in many French-language publications.
Jeffrey Morgan is a user interface and data visualization designer and developer as well as a
data scientist specializing in social media. His research interests include human–computer
interaction and the design of highly-interactive information retrieval and visualization
systems.
Dhiraj Murthy is an Associate Professor in the School of Journalism and the Department of
Sociology at University of Texas at Austin. His research explores social media, digital research
methods, race/ethnicity, qualitative/mixed methods, big data quantitative analysis, and virtual
organizations. Dr Murthy has authored over 40 articles, book chapters, and papers and the
book Twitter: social communication in the Twitter Age, the first on the medium (2013, Polity
Press). He was funded by the National Science Foundation’s Office of CyberInfrastructure for
pioneering work on social networking technologies in virtual organization breeding grounds.
Dr Murthy’s work also uniquely explores the potential role of social technologies in diversity
and community inclusion.
Andrew D. Nevin is a PhD student in the department of Sociology at the University of Toronto.
He holds a Bachelor of Arts in Criminology and a Masters of Arts in Sociology from the University of Western Ontario. Andrew’s current research addresses topics of online deviance and
how the internet may facilitate shifts in identity and personality expression in ways that support
misconduct in digital environments. His additional research interests include cyber-bullying,
digital piracy, hacktivism, social networks and online communities, internet culture, and digital
inequality.
Diane Rasmussen Pennington is a Lecturer in Information Science in the Department of
Computer and Information Sciences at the University of Strathclyde in Glasgow, Scotland,
where she is a member of the iLab and the Digital Health and Wellness research groups. She
is also the Social Media Manager of the Association for Information Science & Technology
(ASIS&T). Dr Rasmussen Pennington has taught classes on research methods, social media,
knowledge organisation, and a range of information technology topics. Her diverse research
areas encompass non-text information indexing and retrieval, Emotional Information Retrieval
(EmIR), user behaviours on social media, and online health information preferences. She is the
editor of Indexing and Retrieval of Non-Text Information (2012) and Social Media for Academics: A Practical Guide (2012). She is currently editing a book series entitled Computing for
Information Professionals.
Chen Qiao is currently a PhD student in the Division of Information & Technology Studies in
the University of Hong Kong. He is interested in natural language processing (NLP) and
machine learning (especially probabilistic graphical models), and is trying to apply and adapt
these techniques to research questions in the education domain. Before joining the PhD program, he was a software engineer at the NLP group of a company in Shanghai. Mr. Qiao
obtained a Bachelor of Arts degree in Russian Language & Literature, and a Master of Science
degree in Educational Technology from East China Normal University, China. He also has
experience in developing Web projects and Augmented Reality applications for classrooms,
and is interested in viewing social phenomena through the lens of complexity theory.

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 24

08/12/16 3:52 PM

Notes on the Editors and Contributors

xxv

Ulf-Dietrich Reips is the Full Professor for Research Methods, Assessment & iScience at the
University of Konstanz and was recently offered to become the director of the Leibniz Institute
for Psychology Information (http://zpid.de). He received his PhD in 1996 from the University
of Tübingen. His research focuses on Internet-based research methodologies, the psychology
of the Internet, measurement, development, the cognition of causality, personality, privacy,
Social Media, crowdsourcing, and Big Data. In 1994, he founded the Web Experimental Psychology Lab, the first laboratory for conducting real experiments on the World Wide Web. Ulf
was a founder of the German Society for Online Research and was elected the first non-North
American president of the Society for Computers in Psychology. He is the founding editor of
the free open access journal International Journal of Internet Science (http://ijis.net). Ulf and
his team develop and provide free Web tools for researchers, teachers, students, and the public.
They received numerous awards for their Web applications (available from the iScience Server
at http://iscience.eu/) and methodological work serving the research community.
Victoria L. Rubin is an Associate Professor at the Faculty of Information and Media Studies
and the Director of the Language and Information Technologies Research Lab (LiT.RL) at the
University of Western Ontario. She specializes in information retrieval and natural language
processing techniques that enable analyses of texts to identify, extract, and organize structured
knowledge. She studies complex human information behaviors that are, at least partly,
expressed through language such as deception, uncertainty, credibility, and emotions. Her
research on Deception Detection has been published in recent core workshops on the topic and
prominent information science conferences, as well as the Journal of the Association for Information Science and Technology. Her 2015–2018 project entitled Digital Deception Detection:
Identifying Deliberate Misinformation in Online News is funded by the Government of Canada
Social Sciences and Humanities Research Council (SSHRC) Insight Grant. For further information, see http://victoriarubin.fims.uwo.ca/.
Janet Salmons is an independent researcher and consultant through Vision2Lead. She is the
author of five books about online research; the most recent are Doing Qualitative Research
Online (2016) and Qualitative Online Interviews (2015). Dr Salmons is a frequent presenter
for conferences and webinars, and currently serves as the Chair for the Academy of Management Ethics Education Committee. Dr Salmons serves on the PhD faculty in Educational
Technology at Walden University. She previously served on the graduate faculty of the Capella
University School of Business, where was honored with the Harold Abel Distinguished Faculty
Award for 2011–2012 and the Steven Shank Recognition for Teaching in 2012, 2013, 2014 and
2015. Dr Salmons lives and works in Boulder, Colorado.
Adriana Soto-Corominas is a PhD student of Hispanic Linguistics at Western University
(Canada). She received her BA from Universitat Autònoma de Barcelona (Spain) and her specialization in Linguistics from University of California Los Angeles (USA). Her current
research focuses on language acquisition and bilingualism. She has been part of the CulturePlex Lab at Western University since early-2015, where she is currently investigating loanwords and neologisms in Spanish.
Bonnie Stewart is an education researcher and practitioner fascinated by who we are when
we’re online. Coordinator of Adult Teaching and Professional Learning at the University of
Prince Edward Island, where she completed her PhD in Educational Studies, Bonnie leads digital strategy and professional learning initiatives. Her research focuses primarily on

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 25

08/12/16 3:52 PM

xxvi

The SAGE Handbook of Social Media Research Methods

digital literacies, networked scholarship, and the intersections of knowledge and technologies.
A networked educator who began working in online education in the 1990s, Bonnie was
involved with Massive Open Online Course (MOOC) research in its early Canadian incarnations. Bonnie has published in Salon.com, The Guardian UK, and Inside Higher Ed in addition
to a variety of peer-reviewed venues, and he does her best thinking aloud on Twitter as @
bonstewart.
Juan Luis Suárez is the Director of the CulturePlex Lab at Western University, Canada. He
holds an e-MBA (IE Business School), a PhD in Hispanic Studies (McGill) and a PhD in
Philosophy (Salamanca). His current research focuses on cultural analytics, big data, cultural
networks, and digital humanities. His publications include over 60 articles and chapters, and
four authored books.
Mike Thelwall leads the Statistical Cybermetrics Research Group at the University of Wolverhampton in the UK Midlands. He has developed free software and methods for systematically
gathering and analysing web and social web data. His sentiment analysis program SentiStrength is sold commercially and given away free for research, with thousands of downloads and
hundreds of citations. SentiStrength has also been used for social media driven art installations,
such as on the London Eye during the 2012 Olympic Games and on the Empire State Building
during the 2014 Super Bowl final. Mike sits on four editorial boards, has co-authored hundreds
of refereed journal articles and has written three books.
Ravi Vatrapu is the Director of the Centre for Business Data Analytics (http://bda.cbs.dk),
Professor of Human Computer Interaction at the Department of IT Management, Copenhagen
Business School; and Professor of Applied Computing at the Westerdals Oslo School of Arts
Communication and Technology. His current research focus is on big social data analytics.
Based on the enactive approach to the philosophy of mind and phenomenological approach to
sociology and the mathematics of classical, fuzzy and rough set theories, his current research
program seeks to design, develop and evaluate a new holistic approach to computational social
science, Social Set Analytics (SSA). SSA consists of novel formal models, predictive methods
and visual analytics tools for big social data. Prof. Vatrapu holds a Doctor of Philosophy (PhD)
degree in Communication and Information Sciences from the University of Hawaii at Manoa,
a Master of Science (MSc) in Computer Science and Applications from Virginia Tech, and a
Bachelor of Technology in Computer Science and Systems Engineering from Andhra
University.
Jessica Vitak (PhD, Michigan State University) is an Assistant Professor in the College of
Information Studies and an Affiliate Professor in the Department of Communication at the
University of Maryland. Her research seeks to span social and computational sciences regarding how people interact with new communication technologies – and the social consequences
of technology use. Specifically, her research evaluates ICT users’ mental models around privacy and disclosure in digital spaces by focusing on unpacking the privacy negotiation processes uses engage in on social media platforms and the perceived and actual social
consequences of information disclosure, including benefits like social support and harms like
harassment. More information is available at http://jessicavitak.com.
Timothy Vogus is an Associate Professor of Management at the Vanderbilt University Owen
Graduate School of Management. He studies how organizations create and sustain

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 26

08/12/16 3:52 PM

Notes on the Editors and Contributors

xxvii

highly reliable, nearly error-free performance. His focus has primarily been on the role that the
processes of mindful organizing – a set of behaviors focused on detecting and correcting errors
and unexpected events – play in generating high reliability and the roles that safety culture and
reliability-enhancing work practices play in supporting it. He typically studies this in health
care organizations, but has also investigated child welfare agencies, offshore oil rigs, social
entrepreneurial ventures, and software firms. His research has been published in health policy
(e.g., Health Services Research, Medical Care), industrial relations (ILR Review), and management (e.g., Academy of Management Review)
Alex Voss is a lecturer in Software Engineering in the School of Computer Science at the
University of St Andrews. He works at the intersection of computer science and the social sciences, with an interest in how people make sense of the social world and the implications for
the design of information and communication technologies. His work on social media involves
academic as well as practical uses of social media data.
Katrin Weller is an information scientist and postdoctoral researcher at GESIS – Leibniz
Institute for the Social Sciences, department Computational Social Science. She is co-editor of
Twitter and Society (2014). Her research interests include interdisciplinary methods in social
media studies, altmetrics and web science.
Matthew J. Williams received the BSc degree in computer science from Cardiff University,
UK in 2008. He received the PhD in computer science degree from the same institution in
2013. He is currently a Postdoctoral Research Fellow at the University of Exeter and an Associate Research Fellow at the University of Birmingham. His research interests include computational social science, spatio-temporal complex systems, and human mobility patterns.
Shuzhe Yang is a research and teaching assistant working at the Chair of Mobile Business and
Multilateral Security in the Institute for Business Informatics at the Goethe University Frankfurt, Germany. He received his PhD degree in Information Systems and Master of Science
degree in Information Management and Finance from the same university. Shuzhe’s research
interests include personal online reputation management, privacy on social media, online selftracking, information retrieval and information system design and architectures. Within these
topics, his particular focus is on people’s behaviour on and outside social media platforms and
the factors that influence people’s motivation formation.
Frauke Zeller is Assistant Professor in the School of Professional Communication, at Ryerson
University in Toronto, Canada. Her research interests are in digital communication analysis
methods, human–computer interaction and human–robot interaction.

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 27

08/12/16 3:52 PM

Acknowledgements
We would like to thank the team at SAGE for their incredible support throughout this project
including Katie Metzler, Mila Steele, Judi Burger and Matthew Oldfield. We are also very
grateful to Gareth Morrell and Kandy Woodfield for their expert advice. We started this
Handbook by asking colleagues across the globe for their ideas and contributions and we would
like to thank them all for their enthusiasm and participation. We would also like to thank all the
anonymous reviewers for their insights and expert feedback, this project would not have come
to fruition without their commitment to excellence.
Finally, thank you to Sarah (for the patience and for keeping me on task) and to Tilly (for not
understanding patience and providing the perfect distraction).
Luke Sloan
And thank you to Bruce for his support and love and to Max + Daniella for inspiring me
every day.
Anabel Quan-Haase

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 28

08/12/16 3:52 PM

1
Introduction to the Handbook of
Social Media Research Methods:
Goals, Challenges and Innovations
Anabel Quan-Haase and Luke Sloan

This introductory chapter provides an overview of the most pressing methodological
issues and challenges social media scholars
need to address. Social media is pervasive in
people’s daily lives and provides new platforms for socialization, public debate, and
information exchange which in turn generates data that is potentially of great interest to
social scientists. This has created a growing
need for the development of methodologically sound, transparent and replicable techniques to capture, collate and analyze these
new forms of data. This chapter starts with
an introduction to The SAGE Handbook of
Social Media Research Methods by presenting its goals, key features and merits. It then
provides a succinct definition of social media
and presents an overview of key characteristics of social media data frequently discussed
in the literature. These characteristics present
new challenges to scholars, as they necessitate novel approaches to data collection,
analysis and interpretation. We highlight the

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 1

issues that derive from the data (using the
6 Vs) and direct readers to the chapters in this
edition which provide solutions. The chapter
concludes that the academic community has
risen to the challenge of developing methodologically innovative approaches, techniques
and tools that are specifically tailored to
address the uniqueness of social media
research – the challenge now is to disseminate this information to the wider social science community. It is our sincere hope that
this edition contributes to this ambitious
goal.

Introduction
It is indeed thrilling to write the introduction
to this comprehensive, timely, and cuttingedge Handbook. Since we started working on
this Handbook, several exciting developments
have occurred in the field of social media

08/12/16 3:52 PM

2

The SAGE Handbook of Social Media Research Methods

scholarship. Firstly, a new journal Social
Media + Society was launched in April 2015
dedicated solely to the publication of original
work in the field. This is in addition to numerous other journals that have social media as a
key focus of scholarship such as Big Data
and Society, Social Media and Society,
Information, Communication & Society, New
Media and Society and the Journal of
Computer-Mediated
Communi­c ation.
Secondly, the relevance of social media in
everyday life continues to grow and this relevance is further increased by the move by citizens toward adopting mobile devices (e.g.,
smartphones, phablets, and tablets) that provide flexible, on-the-go capabilities to access
information from social media apps, as well
as to contribute text, images, commentary and
opinion. Finally, new data collection, data
analysis and data visualization tools as well
as web and mobile applications continue to be
developed and existing ones are constantly
updated and refined. These represent a new
toolkit for scholars to embark on social media
projects that allow for the integration of multiple data sources on a large scale. Hence, The
SAGE Handbook of Social Media Research
Methods represents an important step towards
sharing the novel methodologies, tools and
techniques specifically geared toward taking
full advantage of the unique characteristics of
social media data.
The amount, scale and scope of social
media data have created a need for methodological innovations that are uniquely suited
to examine social media data. This is not
only restricted to big data analysis of a quantitative vein, which has perhaps received the
most media and scholarly attention, but also
to new approaches in qualitative methodology (Salmons, Chapter 12, this volume),
from small stories in narrative analysis
(Georgakopoulou, Chapter 17, this volume),
to close reading (Stewart, Chapter 16, this
volume), to thick data description (LatzkoToth, Bonneau and Millete, Chapter 13, this
volume), to methodologies that examine

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 2

non-verbal data such as images, representations and sound (Rasmussen Pennington,
Chapter 15, this volume). Furthermore, the
linking of data at different scales is a major
challenge in social media data requiring
approaches that are qualitatively different
from existing methods, often combining
image, text and interactions across time and
contexts. Perhaps we can assert that we are
observing what Kuhn (1970) described as
‘anomalies’ which lead toward new paradigms in science, after all, as we will discuss
in more detail below, social media scholarship does require novel approaches and new
ways of looking at social phenomena. As
a result it also requires scholars to develop
new skills in order to harvest, analyze and
most importantly, interpret research findings
and place them in context.
The SAGE Handbook of Social Media
Research Methods is the first book to cover
not only the entire research process in social
media scholarship from question formulation to data analysis to the interpretation of
research findings, but also to include designated chapters on how data collection,
analysis, presentation and interpretation
takes place on specific social media platforms
such as Twitter (Murthy, Chapter 33, this
volume), Facebook (Vitak, Chapter 37,
this volume), Weibo (Hu, Qiao and Fu,
Chapter 35, this volume), VKontakte (Gruzd
and O’Bright, Chapter 38, this volume) and
Instagram (Laestadius, Chapter 38, this
­volume). It provides a step-by-step guide to
overcoming the challenges inherent in the
nature of research projects that deal with
‘big and broad data’ and the need to add context to this data to help with result interpretation. To help those interested in acquiring
the skills needed to complete a social media
project, the chapters provide examples and
case studies of a wide range of approaches
to illustrate how to implement these with
real data. The chapters are detailed and
allow scholars who are unfamiliar with specific approaches or techniques to quickly

08/12/16 3:52 PM

Introduction to the Handbook of Social Media Research Methods

grasp the strengths, limitations and key considerations. The aim of the Handbook is for
scholars to have a reference volume that will
allow them to apply and tailor the various
methodologies to their own research questions. The Handbook will be the single most
comprehensive resource for any scholar
or graduate student embarking on a social
media project.
Four key highlights of the Handbook
include:
1 Exploring the foundations for social media
research including the development of interdisciplinary teams (McCay-Peet & Quan-Haase,
Chapter 2, this volume), ethical considerations
(Beninger, Chapter 5, this volume) and the wider
impact of ‘big data’ on the social sciences
(Kitchin, Chapter 3, this volume).
2 Demonstrating how both established and new
qualitative and quantitative methods can be
applied to social media data (Hand, Chapter 14,
this volume).
3 Navigating what tools are available to help
researchers with social media data collection,
analysis, and representation (e.g., visual, sound,
video and textual) and how they can be used
(Rasmussen Pennington, Chapter 15, this volume;
Vitak, Chapter 37, this volume; Zeller, Chapter 23,
this volume).
4 Evaluating the characteristics and applications
of different social media platforms for academic
research purposes (Gruzd and O’Bright, Chapter
38, this volume; Laestadius, Chapter 34, this
volume; Vitak, Chapter 37, this volume; Hu, Qiao
& Fu, Chapter 35, this volume).

This introductory chapter seeks to place the
volume in the wider context of the big data
revolution through further defining what
social media is and what it means to develop
a ‘social media methodology’. We then move
on to explore what makes social media
research so different to traditional social scientific endeavour in terms of the generic nonplatform specific characteristics of the data.
Having identified the difficulties and frustrations of using social media data, we conclude
with how the contributions in this book have

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 3

3

established an accessible foundation for
social scientific enquiry in this area.

Social Media for Social
Research?
While some scholars have studied social phenomena on social media as a separate sphere
from ‘real life’, we argue that these applications need to be viewed as integrated into and
as an integral part of society at large. It is
myopic to think that social media data emerge
in a vacuum. Interactions and engagement on
social media are often directly linked, or even
result from, events taking place outside of it.
Moreover, they are produced within a specific
historical, social, political, and economic context. Thus, social media scholarship needs to
take this context into account in any study of
social media. This perspective is critical as it
directly influences a study’s research design
and interpretation of findings. Often additional
information, in the form of maps, historical
events, newspaper articles, demographic information or political upheavals, need to be
included to provide additional context that can
aid in the interpretation of research findings.
Following the interest in the role social
media played in the 2011 London Riots, the
2012 Barak Obama presidential campaign,
the 2014 Ukraine political crises, the recent
announcement in the UK of a multimillion
pound government-funded data science institute, and the increasing disenfranchisement
of social science data at the expense of privately owned transactional datasets (Savage
and Burrows 2007, Hong and Nadler 2012),
the social science community has become
increasingly interested in non-traditional
approaches to research design and data collection. The massive and unprecedented generation of ‘big and broad data’ necessitates
the development of novel and innovative
approaches to make sense of the social world
through social media data, which in itself is

08/12/16 3:52 PM

4

The SAGE Handbook of Social Media Research Methods

often de-contextualized and ‘data light’ with
regards to the demographic staples of social
scientific analysis (Sloan et al. 2013, 2015;
Sloan, Chapter 7, this volume). Social media
data also presents challenges with data preparation not seen to this scale in past data sets.
The identification and handling of outliers is
not new to scholars. In purely quantitative
approaches, outliers are often eliminated as
they introduce undesired ‘noise’ and can bias
analytical findings, for example, in multivariate regression analysis. Qualitative data
has handled outliers very differently, focusing on anomalies in data sets and integrating
them in the interpretation of findings (Bradley,
1993). Social media data confronts several
new types of noise and it remains unclear
as yet as to how to integrate them into the
analysis and interpretation of findings (Yang,
Chen, Nevin and Quan-Haase, Chapter 6).
For instance, misinformation can either be
deceitful or accidental, depending on the
individual’s motivation (Rubin, Chapter 21,
this volume). Either way, scholars need to be
aware of these extraneous factors and handle
data analysis and interpretation accordingly.
This Handbook provides an overview of the
cutting-edge developments in this field that
establish how to tackle these problems and
overcome unique challenges, thus enabling
more researchers to study the digital world
through developments in methodology at the
nexus of the social and computer sciences
and digital humanities.

Defining Social Media
The global proliferation of social media is
unprecedented both in growth of take-up and
content production.1 Duggan, M. (2015)
shows that in 2015, as much as 72%
of American online adults used Facebook,
31% Pinterest, 28% Instagram, 25%
LinkedIn, and 23% Twitter. Most young
people are constantly updating their Facebook
status, retweeting messages and uploading

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 4

pictures to Instagram. Zephoria reports that
as of June 2015, Facebook had 1.49 billion
monthly active users: every 60 seconds these
users provide 293,000 status updates, post
510 comments, and upload 136,000 photos
(Zephoria, 2015). The amount of data generated and stored every minute is unprecedented. In short, social media usage has
become a daily practice for many. For scholars, this revolution in communication provides both opportunities and challenges. The
sheer amount of digitized user-generated
content is a potentially rich source of information about the social world including
interactions, attitudes, opinions and virtual
reactions to real-world events. Yet the computational and analytical challenges are
­significant – how to process the vast amount
of data, how to filter noise, how to democratize access to social media data for the wider
social science community, how to understand
online behaviour, and how to apply traditional social scientific concepts of sampling
and inference, and coding and interpretation
to understand the relationship between online
communities and the wider population.
One key challenge is providing a definition for what social media is. Chapter 2,
co-authored by McCay-Peet and QuanHaase (this volume), provides a review of
how scholars have approached this con­
ceptual challenge and discusses what key
elements are constant across various definitions. Creighton et al. (2013) get at the heart
of the problem by stating that social media is
closely linked to digital technology in general, making it difficult to articulate where
the boundaries lie between various applications, tools and sites. This results from the
heavy emphasis on social features in many
applications, be it mobile, Internet-based or
other platforms. Moreover, most web sites
provide capabilities to seamlessly interact
with social media further blurring the boundaries. It is also increasingly difficult to distinguish social media from digital technology
in general (Creighton et al. 2013) because of
the social elements that are now embedded in

08/12/16 3:52 PM

Introduction to the Handbook of Social Media Research Methods

everything from smartphone applications to
wearable technologies.
What distinguishes social media from traditional media such as print and radio and
from other new media such as web sites and
podcasts? Hogan and Quan-Haase (2010)
suggest that a definition of social media needs
to specifically focus on what is unique about
the applications and tools that are included
and Bruns (2015) points out that the uniqueness of social media is its focus on connecting: ‘All media are social, but only a particular
subset of all media are fundamentally defined
by their sociality, and thus distinguished (for
example) from the mainstream media of print,
radio, and television’ (2015: 1). For the purpose of The SAGE Handbook of Social Media
Research Methods we propose to include
applications that have the following three
characteristics:
1 Have the capability to support user-generated
content in forms such as images, text, videos
and statuses (such as geolocation check ins)
(Blackshaw 2006, Gruzd et al. 2012, Kaplan and
Haenlein 2010, Xiang and Gretzel 2010).
2 Provide a means for users to connect with one
another (through follows or likes on Twitter,
friendship connections on Facebook, or checking in with Foursquare) (Correa, Hinsley, and de
Zúñiga 2010).
3 Support various means for members to engage
with one another in the form of collaboration,
community building, participation, sharing, linking and other means (Bruns 2015, Otieno and
Matoke 2014).

Once these three elements come together, a
medium can be described as falling under the
rubric of social media.

The Methodological Challenges
We Must Respond To
Using social media data for social scientific
analysis requires a reorientation of how we
think about data and its relationship with the

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 5

5

social world. The data exists and proliferates
whether it is observed or not, it is not created
solely for the purpose of research – in this
sense its role in academic work could be
labelled as incidental, yet that cannot detract
from its importance in recording and shedding light on a whole range of social phenomena including attitudes, intentions,
identity, networks, opinions, locations and
representations. Of course, the incidental
nature of the data is not entirely new to the
social sciences (observational studies and
ethnography as examples), but social media
data inherently creates specific challenges
that we must tackle head on. The challenges
are not discipline-specific and can most
poetically be presented as the 6 Vs: volume,
variety, velocity, veracity, virtue and value
(Williams et al. 2016).
Volume refers to the sheer amount of data
being produced on social media platforms.
BIS (2013) estimates that around 90% of
the world’s data was created in two years
prior to 2013 and Twitter reports the creation
of 500 million tweets a day (Twitter, 2015)
with around 15 million Twitter users in the
UK alone (Rose, 2014). Collecting and storing this data raises significant challenges
(Mayr and Weller, Chapter 8, this volume;
Voss, Lvov and Thomson, Chapter 11, this
volume) and sorting the useful data from the
noise can take time and skill.
Variety is related to the multimodal nature
of the data including text (Angus, Chapter 31,
this volume; Georgakopoulou, Chapter 17,
this volume; Thelwall, Chapter 32, this
­volume), images (Hand, Chapter 14, this volume; Laestadius, Chapter 34, this v­ olume),
videos, geospatial check ins (Buchel &
Rasmussen Pennington, Chapter 18, this
­volume; Williams and Chorley, Chapter 36,
this volume; Reips and Garaizar, Chapter
27, this volume) and audio. Also relevant is
the ability of social media platforms to often
facilitate multiple data types. This means that
the ‘big data’ problem is not an issue solely
for quantitative studies and the chapters in
this edition demonstrate the huge potential

08/12/16 3:52 PM

6

The SAGE Handbook of Social Media Research Methods

for analysis of many data types using social
media including mixed methods studies
(Hochman, Chapter 22, this volume).
Velocity refers to both the speed at which
social media data is generated and how
quickly users respond to real world events.
The speed of data generation poses some
very particular problems for data collection
that need computational solutions rather
than manual recording, such as the use of
Application Program Interfaces (APIs)
(Brown, et al., Chapter 9, this volume;
Hegelich, Chapter 28, this volume). This
is a particular problem for the social science community because of the paucity of
computing and coding knowledge amongst
researchers and in response we have covered the topic in this book from several
angles. The speed of response to events
creates a different set of problems around
researchers reacting quickly enough to
commence data collection when an event
occurs and understanding the role of
fine-grained temporality with ‘locomotive’
data (Jamieson and Boase, Chapter 24,
this volume).
Veracity is primarily concerned with the
accuracy, reliability and quality of the data.
Social media data are often lacking important information that we would normally
collect as standard in social research, most
notably the demographic characteristics of
the respondent and/or content producer.
The development of demographic proxies
(Sloan, Chapter 7, this volume) is key to
understanding who is represented on social
media, thus enabling further conversations
around sampling and populations. Concerns
around how (and if) social media data
reflects real world events can be addressed
through data linkage and augmentation
with existing curated and administrative
data sources (Zeller, Chapter 23, this volume), although we must still deal with the
question of how the self is presented and
to what extent the online identity of a user
is crafted (Yang, Quan-Haase, Nevin and
Chen, Chapter 6, this volume).

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 6

Virtue means ethics. Current ethical guidelines for social research are not fit for purpose when applied to social media data and
much work has been done internationally to
coordinate a response from the social science
community on what such an ethical framework may look like (the ‘New Social Media,
New Social Science’ #NSMNSS scholarly network has been particularly active in
this area and practical guidelines are starting to emerge (see Townsend and Wallace
2016) which provide pragmatic advice to
researchers). To complicate matters further,
general ethical principles such as participant
anonymity are at odds with the legal terms
and conditions of data use for some platforms. Twitter will not allow tweets to be
presented without usernames). This, in turn,
has implications for protecting participants
from harm when presenting data that may be
incendiary (such as tweets containing hate
speech). A starting point for the development
of an ethical framework for social media is
to understand how participants feel about
their data being used for research (Beninger,
Chapter 5, this volume).
Value is an assessment of how social
media data increases our understanding of
the social world by opening hitherto unavailable avenues of research and/or augmenting
existing work through access to new data
(McCay-Peet and Quan-Haase, Chapter 2,
this volume). Certainly there are questions to
be asked about how new ‘big data’ really is
and what role theory can play within the data
deluge (Kitchin, Chapter 3, this volume), but
perhaps an unexpected outcome of the challenge this data has thrown at us has been
an increase in interdisciplinary work across
the social and computing sciences as well
as the humanities from which all sides have
benefitted (Quan-Haase and McCay-Peet,
Chapter 4, this volume).
In response to these challenges a range of
tools have been identified or developed that
account for the complex characteristics of
social media data. Popular and free analytical
packages such as ‘R’ enable users to collect

08/12/16 3:52 PM

Introduction to the Handbook of Social Media Research Methods

Twitter data (Janetzko, Chapter 10, this volume) and analyze it in a variety of ways, whilst
other tools provide more bespoke functionality
in the areas of Natural Language Processing
(NLP) and language analysis (Bontecheva,
Chapter 29, this volume; Rubin, Chapter 21,
this volume; Thelwall, Chapter 32, this volume) and social network analysis (GhajarKhosravi and Chignell, Chapter 19, this
volume; Gruzd, Mai and Kampen, Chapter
30, this volume). Importantly, the development of graphical interface platforms, such as
COSMOS, have democratized access by lowering the level of technical knowledge required
to ascertain, process, filter and explore social
media data (Morgan, Chapter 26, this volume).

Conclusion
The introductory chapter highlights and discusses many of the challenges encountered in
studies of social media ranging from practical
decisions that influence the research design
(i.e., timeline of data collection, what hashtags
to follow and what tools to use to collect data)
to more philosophical questions around the
ethical treatment of human subjects. These
challenges also emerge from the complexity
of social media data and we discussed the
6 Vs as a means of summarizing the key characteristics (volume, variety, velocity, veracity,
virtue and value) that any social media project
needs to come to terms with and the tools that
facilitate working with this data. The contributions to this Handbook demonstrate that the
academic community has responded with
gusto to these challenges with over 40 experts
from around the world from a plethora of
disciplines and a variety of methodological
viewpoints coming together in one place for
the first time. The Handbook covers not only
the entire research process for social media
research, from question formulation to the
interpretation of research findings, but it also
presents numerous examples and case studies
of various approaches to showcase how to

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 7

7

implement various techniques with real data.
The chapters provide methodological detail
and allow scholars who are unfamiliar with
the domain to quickly grasp the strengths,
limitations and key considerations of this type
of data with the aim of:
•• Encouraging skill development: An easy to follow,
step-by-step approach encourages scholars to
immerse themselves in new techniques and
thereby widen their methodological toolkit.
•• Showcasing tool use: Many of the chapters rely
on tools developed specifically for either the collection, analysis or visualization of social media
data. Both pros and cons of various tools are
discussed and the possibilities for analysis presented via case studies and examples.
•• Covering of the entire research process: Scholars
are encouraged to think about the entire research
process from study design to interpretations of
data. This provides a uniquely holistic perspective
of social media research.
•• Promoting ethical considerations: The chapters
highlight how the person, the self, cannot be
separated from the data trace in particular types
of studies. This necessitates novel approaches to
research ethics and the treatment of both big
data and small data.
•• Demonstrating data harvesting and cleaning practices and techniques: Social media data has particular characteristics and these need to be taken
into account when harvesting data either for large
quantitative analysis or analysis at a small scale.
Also misinformation, deception and trolling practices can influence data interpretation and need
to be considered seriously as part of the data set.
•• Highlighting multi-method and multi-data
approaches: The use of multiple methods of data
collection, analysis, visualization and interpretation are critical for developing distinct understandings of social media practice and social
phenomena. Triangulating data and also analyzing social media data in conjunction with other
data sources can provide a fuller picture of social
phenomena.

To tackle the challenges of social media
research we must embrace an interdisciplinary approach (Quan-Haase and McCayPeet, Chapter 4, this volume), drawing on
methodological traditions from across and

08/12/16 3:52 PM

8

The SAGE Handbook of Social Media Research Methods

outside of the social sciences, computer sciences and humanities. It is our sincere hope
that this edition widens the pool of researchers who feel confident and competent when
working with social media data and that the
methodological discussions spread and grow
because, as Bob Dylan almost said, these
methods they are a changin’.

Note
1 	 As McCay-Peet and Quan-Haase (Chapter 2, this
volume) report between 2009 and 2014 there
are almost twice as many references in Scholar’s
Portal (http://www.scholarsportal.info) in peerreviewed articles to the term ‘social media’ in
comparison to ‘social networking sites (SNSs)’. In
recent years, usage of both terms has increased
exponentially, the trend suggests that social
media is more ‘social media’ than ‘social networking site (SNS)’.

References
BIS (2013). Seizing the data opportunity: A
strategy for UK data capability. London:
Department of Business, Innovation and
Skills.
Blackshaw, P. (2006). ‘The consumer-controlled
surveillance culture’, ClickZ. Retrieved October 15, 2015 from http://www.clickz.com/
clickz/column/1706163/the-consumercontrolled-surveillance-culture
Bradley, J. (1993). ‘Methodological issues and
practices in qualitative research’, The Library
Quarterly: Information, Community, Policy,
63(4): 431–449.
Bruns, A. (2015). ‘Making sense of society
through social media’, Social Media + S­ ociety,
1(1): 1–2.
Correa, T., Hinsley, A. W., and de Zúñiga, H. G.
(2010). ‘Who interacts on the Web? The
intersection of users’ personality and social
media use’, Computers in Human Behavior,
26(2): 247–253.
Creighton, J. L., Foster, J. W., Klingsmith, L.,
and Withey, D. K. (2013). ‘I just look it up:

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 8

Undergraduate student perception of
social media use in their academic success’, Journal of Social Media in Society,
2(2): 26–46.
Duggan, M. (2015). Mobile messaging and
social media 2015 | Pew Research Center.
Retrieved September 6, 2016, from http://
w w w. p e w i n t e r n e t . o r g / 2 0 1 5 / 0 8 / 1 9 /
mobile-messaging-and-social-media2015/
Gruzd, A., Staves, K., and Wilk, A. (2012).
‘Connected scholars: Examining the role of
social media in research practices of faculty
using the UTAUT model’, Computers in
Human Behavior, 28(6): 2340–2350.
Hogan, B., and Quan-Haase, A. (2010). ‘Persistence and change in social media’, Bulletin
of Science, Technology & Society, 30(5):
309–315.
Hong, S., and Nadler, D. (2012). ‘Which candidates do the public discuss online in an election campaign? The use of social media by
2012 presidential candidates and its impact
on candidate salience’, Government Information Quarterly, 29(4): 455–461. http://
doi.org/10.1016/j.giq.2012.06.004
Kaplan, A. M., and Haenlein, M. (2010). ‘Users
of the world, unite! The challenges and
opportunities of social media’, Business Horizons, 53(1): 59–68.
Kuhn, T. S. (1970). The structure of scientific
revolutions. Chicago, IL: University of
­Chicago Press.
Otieno, D. O., and Matoke, V. B. (2014). ‘Social
media as tool for conducting academic
research’, International Journal of Advanced
Research in Computer Science and Software
Engineering, 4(1): 962–967.
Rose, K. (2014). ‘The UK Social Media Landscape for 2014’, Available at: http://www.
rosemcgrory.co.uk/2014/01/06/uk-socialmedia-statistics-for-2014/
Savage, M., and Burrows, R. (2007). ‘The
coming crisis of empirical sociology’, Sociology, 41(5): 885–899. http://doi.org/
10.1177/0038038507080443
Sloan, L., Morgan, J., Burnap, P., and Williams, M.
(2015). ‘Who tweets? Deriving the demographic characteristics of age, occupation
and social class from Twitter user meta-data’,
Plos One, 10(3), article number: e0115545
(10.1371/journal.pone.0115545).

08/12/16 3:52 PM

Introduction to the Handbook of Social Media Research Methods

Sloan, L., Morgan, J., Housley, W., Williams,
M., Edwards, A., Burnap, P., and Rana, O.
(2013). ‘Knowing the Tweeters: Deriving
sociologically relevant demographics from
Twitter’, Sociological Research Online, 18(3),
article number: 7. (10.5153/sro.3001).
Townsend, L., and Wallace, C. (2016). ‘Social
Media Research: A Guide to Ethics’. Available at: http://www.dotrural.ac.uk/socialmediaresearchethics.pdf
Twitter (2015). ‘Twitter Usage/Company Facts’,
Available from: https://about.twitter.com/
company.

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 9

9

Williams, M., Burnap, P., and Sloan, L. (2016).
doi: 10.1093/bjc/azw031 ‘Crime sensing
with Big Data: The affordances and limitations of using open source communications
to estimate crime patterns’, British Journal of
Criminology - Special Issue.
Xiang, Z., and Gretzel, U. (2010). ‘Role of social
media in online travel information search’,
Tourism Management, 31(2): 179–188.
Zephoria. (2015). ‘The Top 20 valuable Facebook
statistics – Updated October 2015’, Retrieved 9
November 2015, from https://zephoria.com/
top-15-valuable-facebook-statistics/

08/12/16 3:52 PM

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 10

08/12/16 3:52 PM

PART i

Conceptualising & Designing
Social Media Research

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 11

08/12/16 3:52 PM

BK-SAGE-SLOAN_QUAN-HAASE-160238.indb 12

08/12/16 3:52 PM

2
What is Social Media and What
Questions Can Social Media
Research Help Us Answer?
Lori McCay-Peet and Anabel Quan-Haase

This chapter critically engages with the plurality of meanings given to the term social
media, ranging from mainstream blogging
platforms to niche communication tools. A
brief historical overview is first presented of
how the term has evolved, showing that in
academia it has only gained widespread popularity since the mid-2000s. The chapter then
discusses various categorization frameworks
available in the literature to examine what
applications and platforms are commonly
considered a part of the social media spectrum. The chapter ends
https://fr.b-ok.xyz/book/3497405/1bbf7e
fr.b-ok.xyz
crawl=CC-MAIN-2019-35
154103
application/http; msgtype=response
sha1:VAQEKYABAKIYIIVN27XAEIW2T5UCU7EE
<urn:uuid:08de2eee-0fbf-4361-8046-9b250927eebe>
2019-08-25T15:20:44Z
179.43.147.124
text/html
sha1:MJUI3QLHHMD2MW3I5FEVH4F5VDJSFJ3M
<urn:uuid:241c4d8f-5cf2-40ba-9377-727fcf75008f>
https://fr.b-ok.xyz/book/3497405/1bbf7e
response
<urn:uuid:5d79b911-d29e-4e51-bd13-8b1d7263a5c5>
isPartOf: CC-MAIN-2019-35
publisher: Common Crawl
description: Wide crawl of the web for August 2019
operator: Common Crawl Admin (info@commoncrawl.org)
hostname: ip-10-67-67-214.ec2.internal
software: Apache Nutch 1.15 (modified, https://github.com/commoncrawl/nutch/)
robots: checked via crawler-commons 1.1-SNAPSHOT (https://github.com/crawler-commons/crawler-commons)
format: WARC File Format 1.1
conformsTo: http://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/