text: This article provides a comprehensive investigation on the relations between virality of news articles and the emotions they are found to evoke. Virality, in our view, is a phenomenon with many facets, i.e. under this generic term several different effects of persuasive communication are comprised. By exploiting a high-coverage and bilingual corpus of documents containing metrics of their spread on social networks as well as a massive affective annotation provided by readers, we present a thorough analysis of the interplay between evoked emotions and viral facets. We highlight and discuss our findings in light of a cross-lingual approach: while we discover differences in evoked emotions and corresponding viral effects, we provide preliminary evidence of a generalized explanatory model rooted in the deep structure of emotions: the Valence-Arousal-Dominance (VAD) circumplex. We find that viral facets appear to be consistently affected by particular VAD configurations, and these configurations indicate a clear connection with distinct phenomena underlying persuasive communication.
  @cite_18: Social media is playing an increasingly vital role in information dissemination. But with dissemination being more distributed, content often makes multiple hops, and consequently has opportunity to change. In this paper we focus on content that should be changing the least, namely quoted text. We find changes to be frequent, with their likelihood depending on the authority of the copied source and the type of site that is copying. We uncover patterns in the rate of appearance of new variants, their length, and popularity, and develop a simple model that is able to capture them. These patterns are distinct from ones produced when all copies are made from the same source, suggesting that information is evolving as it is being processed collectively in online social media.
  @cite_6: Understanding the ways in which information achieves widespread public awareness is a research question of significant interest. We consider whether, and how, the way in which the information is phrased --- the choice of words and sentence structure --- can affect this process. To this end, we develop an analysis framework and build a corpus of movie quotes, annotated with memorability information, in which we are able to control for both the speaker and the setting of the quotes. We find that there are significant differences between memorable and non-memorable quotes in several key dimensions, even after controlling for situational and contextual factors. One is lexical distinctiveness: in aggregate, memorable quotes use less common word choices, but at the same time are built upon a scaffolding of common syntactic patterns. Another is that memorable quotes tend to be more general in ways that make them easy to apply in new contexts --- that is, more portable. We also show how the concept of "memorable language" can be extended across domains.
  @cite_3: 7 Chapter 1: Introduction 8 1.1. Motivation 9 1.2. Problem Hypothesis 11 1.3. Contributions 11 1.4. Thesis Outline 11 Chapter 2 : Background 13 2.1. Social Networks 13 2.2. Implicit versus Explicit Social Network Formations 14 2.3. Social Network Analysis versus Dynamic Network Analysis 15 2.4. Related Research 16 2.5. Social Bookmarking and Digg Network 18 Chapter 3: User Characterization 21 3.1. Network Description and Statistics 21 3.2. Degree Distribution 22 3.3. Egonet Analysis 23 3.4. User Membership Analysis 27 Chapter 4: Comparative Analysis of Network Formations 31 4.
  @cite_2: Great writing is rare and highly admired. Readers seek out articles that are beautifully written, informative and entertaining. Yet information-access technologies lack capabilities for predicting article quality at this level. In this paper we present first experiments on article quality prediction in the science journalism domain. We introduce a corpus of great pieces of science journalism, along with typical articles from the genre. We implement features to capture aspects of great writing, including surprising, visual and emotional content, as well as general features related to discourse organization and sentence structure. We show that the distinction between great and typical articles can be detected fairly accurately, and that the entire spectrum of our features contribute to the distinction.
summary: Recently, the relation between content characteristics and virality has begun to be investigated, especially with regard to textual content. @cite_3 , for instance, features derived from sentiment analysis of comments are used to predict the popularity of stories. The relevant work in @cite_6 measures a different form of content spreading, by analyzing which are the features of a movie quote that make it memorable" online. Another approach to content virality, somehow complementary to the previous one, is presented in @cite_18 , where the authors investigate which modification dynamics make a meme spread from one person to another (as compared with movie quotes which spread remaining exactly the same). Louis and Nenkova @cite_2 focused on influential scientific articles in newspapers, considering characteristics such as readability, description vividness, use of unusual words and affective content, comparing high quality articles (NYT articles appearing in The Best American Science Writing" anthology) against typical NYT articles.