text: Multiple studies in the past have analyzed the role and dynamics of the Twitter social network during real world events. However, little work has explored the content of other social media services, or compared content across two networks during real world events. We believe that social media platforms like Facebook also play a vital role in disseminating information on the Internet during real world events. In this work, we study and characterize the content posted on the world's biggest social network, Facebook, and present a comparative analysis of Facebook and Twitter content posted during 16 real world events. Contrary to existing notion that Facebook is used mostly as a private network, our findings reveal that more than 30 of public content that was present on Facebook during these events, was also present on Twitter. We then performed qualitative analysis on the content spread by the most active users during these events, and found that over 10 of the most active users on both networks post spam content. We used stylometric features from Facebook posts and tweet text to classify this spam content, and were able to achieve an accuracy of over 99 for Facebook, and over 98 for Twitter. This work is aimed at providing researchers with an overview of Facebook content during real world events, and serve as basis for more in-depth exploration of its various aspects like information quality, and credibility during real world events.
  @cite_18: Following the 12 May 2008 Wenchuan, China, earthquake, discussion circulated on the Internet describing how the U.S. Geological Survey's earthquake notification lagged behind firsthand accounts sent through Twitter, a popular Internet-based service for sending and receiving short text messages, referred to as “tweets.” A prominent technology blogger, Robert Scoble (http: scobleizer.com), is generally credited for being the first to aggregate and redistribute tweets from people in China who directly experienced and reported the shaking resulting from the Wenchuan earthquake. @PARASPLIT Subsequent earthquakes generated volumes of earthquake-related tweets, and numerous accounts are on the Web. For example, Ian O'Neill discusses Twitter activity following a magnitude 3.3 Los Angeles earthquake on 24 January 2009 in his blog http: astroengine.com. He showed a remarkable increase in frequency of tweets containing the word “earthquake” after the event and discussed the possibility of a Twitter-based earthquake detector. Similarly, following the 30 March 2009, Morgan Hill, California, magnitude 4.3 earthquake, Michal Migursk (http: mike.teczno.com) noted an increase in earthquake-related tweets. @PARASPLIT Access to firsthand accounts of earthquake shaking within seconds of an earthquake is intriguing, but is there any reliable information that can be gleaned from the Twitter messages? To explore this question, we provide a quick review of Twitter and its capabilities and investigate the possibility of using the tweets to detect seismic events and produce rapid maps of the felt area. In this exploratory study, we examine the tweets that followed the 30 March 2009 Morgan Hill earthquake. @PARASPLIT Twitter is a service that allows anyone to send and receive 140-character text messages (tweets) via any Internet-enabled device. Tweets can be sent and received through a Web page, mobile device, or third-party Twitter applications. Tweets can be sent publicly or privately to a specified user. All users who opt to “follow” a Twitter user will receive that user's tweets …
  @cite_14: 
  @cite_8: The emergence of innovative web applications, often labelled as Web 2.0, has permitted an unprecedented increase of content created by non-specialist users. In particular, Location-based Social Networks (LBSN) are designed as platforms allowing the creation, storage and retrieval of vast amounts of georeferenced and user-generated contents. LBSN can thus be seen by Geographic Information specialists as a timely and cost-effective source of spatio-temporal information for many fields of application, provided that they can set up workflows to retrieve, validate and organise such information. This paper aims to improve the understanding on how LBSN can be used as a reliable source of spatio-temporal information, by analysing the temporal, spatial and social dynamics of Twitter activity during a major forest fire event in the South of France in July 2009.
  @cite_9: We analyze microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service. We focus on communications broadcast by people who were "on the ground" during the Oklahoma Grassfires of April 2009 and the Red River Floods that occurred in March and April 2009, and identify information that may contribute to enhancing situational awareness (SA). This work aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques.
  @cite_17: 
  @cite_12: Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96 of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.
  @cite_11: This paper offers a descriptive account of Twitter (a microblogging service) across four high-profile, mass convergence events - two emergency and two national security. We statistically examine how Twitter is being used surrounding these events, and compare and contrast how that behaviour is different from more general Twitter use. Our findings suggest that Twitter messages sent during these types of events contain more displays of information broadcasting and brokerage, and we observe that general Twitter use seems to have evolved over time to offer more of an information-sharing purpose. We also provide preliminary evidence that Twitter users who join during and in apparent relation to a mass convergence or emergency event are more likely to become long-term adopters of the technology.
summary: Twitter has been used widely during emergency situations, such as wildfires @cite_8 , hurricanes @cite_11 , floods @cite_9 and earthquakes @cite_18 @cite_14 @cite_12 . Journalists have hailed the immediacy of the service which allowed to report breaking news quickly - in many cases, more rapidly than most mainstream media outlets'' @cite_17 . @cite_12 explored the potential of the real-time nature of Twitter and proposed an algorithm to detect occurrence of earthquakes by simply monitoring a stream of tweets in real-time. Here, the authors took advantage of the fact that users tweet about events like earthquakes as soon as they take place in the real world, and were able to detect 96