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.
In recent years, studying Twitter has been the main focus for researchers in context of real world events. @cite_4 showed a big overlap between real-world news headlines and Twitter's trending topics. Authors of this work crawled the entire Twitter network, and showed that the majority (over 85
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Twitter has become exceedingly popular, with hundreds of millions of tweets being posted every day on a wide variety of topics. This has helped make real-time search applications possible with leading search engines routinely displaying relevant tweets in response to user queries. Recent research has shown that a considerable fraction of these tweets are about "events," and the detection of novel events in the tweet-stream has attracted a lot of research interest. However, very little research has focused on properly displaying this real-time information about events. For instance, the leading search engines simply display all tweets matching the queries in reverse chronological order. In this paper we argue that for some highly structured and recurring events, such as sports, it is better to use more sophisticated techniques to summarize the relevant tweets. We formalize the problem of summarizing event-tweets and give a solution based on learning the underlying hidden state representation of the event via Hidden Markov Models. In addition, through extensive experiments on real-world data we show that our model significantly outperforms some intuitive and competitive baselines.