Twitinfo: aggregating and visualizing microblogs for event exploration

By Adam Marcus, Michael S. Bernstein, Osama Badar, David R Karger, Samuel Madden, Rob Miller. International Conference on Human Factors in Computing Systems, 2011

640 citations recorded by the source corpus.

Microblogs are a tremendous repository of user-generated content about world events. However, for people trying to understand events by querying services like Twitter, a chronological log of posts makes it very difficult to get a detailed understanding of an event. In this paper, we present TwitInfo, a system for visualizing and summarizing events on Twitter. TwitInfo allows users to browse a large collection of tweets using a timeline-based display that highlights peaks of high tweet activity. A novel streaming algorithm automatically discovers these peaks and labels them meaningfully using text from the tweets. Users can drill down to subevents, and explore further via geolocation, sentiment, and popular URLs. We contribute a recall-normalized aggregate sentiment visualization to produce more honest sentiment overviews. An evaluation of the system revealed that users were able to reconstruct meaningful summaries of events in a small amount of time. An interview with a Pulitzer Prize-winning journalist suggested that the system would be especially useful for understanding a long-running event and for identifying eyewitnesses. Quantitatively, our system can identify 80-100% of manually labeled peaks, facilitating a relatively complete view of each event studied.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1cf2f8804eaa3050b6e8da58ed241eb490143f27

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Citations in the corpus

Detection and Resolution of Rumours in Social Media (2017, background): “…of social media platforms, researchers have looked into the development of tools for news gathering [Zubiaga et al. 2013; Diakopoulos et al. 2012; Marcus et al. 2011], analysed the use of user-generated content (UGC) for news reporting [Hermida and Thurman 2008; Tolmie et al. 2017a], and…”
Understanding in-video dropouts and interaction peaks inonline lecture videos (2014, methodology): “We tested with different parameters in the algorithm to fit the time scale of our analysis, which is much shorter (the order of seconds and minutes) than what TwitInfo dealt with (hours and days).”
Visual Analysis of Topic Competition on Social Media (2013, background): “2 Social Media Visualization Researchers have developed various visualization methods to help users understand social media data by providing aggregated information [10, 11, 26].”
Towards Supporting Search over Trending Events with Social Media (2013, methodology): “Automatic identification of trends in web and text data is an interesting and challenging problem (Gabrilovich, Dumais, and Horvitz 2004; Kleinberg 2006; Marcus et al. 2011; Vlachos et al. 2004).”
An Uncertainty-Aware Approach for Exploratory Microblog Retrieval (2015, methodology): “The methods applied include event detection [30], topic extraction and analysis [25, 40, 50], information diffusion [8, 52], sentiment analysis [47, 48], and revenue/stock prediction [28, 34].”
EvoRiver: Visual Analysis of Topic Coopetition on Social Media (2014, background): “Social media allow millions of users to consume, produce, and disseminate huge volumes of highly diverse information on social networks [20, 27].”
Processing Social Media Messages in Mass Emergency (2014, background): “Twitter Monitor burst detection open domain yes open no no [Mathioudakis and Koudas 2010] TwitInfo burst detection earthquakes+ yes kw spatial yes [Marcus et al. 2011] Twevent burst segment detection open domain yes open no no [Li et al.”