Beyond Trending Topics: Real-World Event Identification on Twitter

By Hila Becker, Mor Naaman, Luis Gravano. International Conference on Web and Social Media, 2011

840 citations recorded by the source corpus.

User-contributed messages on social media sites such as Twitter have emerged aspowerful, real-time means of information sharing on the Web. These short messages tend to reflect a variety of events in real time, making Twitter particularly well suited as a source of real-time event content. In this paper, we explore approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events andnon-event messages. Our approach relies on a rich family of aggregatestatistics of topically similar message clusters. Large-scale experiments over millions of Twitter messages show the effectiveness of our approach for surfacing real-world event content on Twitter.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/bb7965a9dbeab34cec56e19aae949690f2463f20

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Citations in the corpus

Racism on the Internet: Conceptualization and Recommendations for Research (2018, background): “Sharing, in particular, has become especially influential to society due to the “trending” power of social media (e.g., Twitter; Becker et al., 2011), or the effectiveness in propagating controversial incidents at an exponential rate by the collective sharing efforts among people.”
Sensing Trending Topics in Twitter (2013, background): “Similar approaches based on textual similarity and can be found in the literature [18], [19].”
Real-Time Detection of Traffic From Twitter Stream Analysis (2015, neutral): “traditional news media [55], [56].”
The Geography and Importance of Localness in Geotagged Social Media (2016, background): “Finally, it is important to note that a much larger body of work unintentionally works around the localness assumption by using data from the location fields of social media users’ profiles rather than geotags [3,26,34].”
Open domain event extraction from twitter (2012, neutral): “[3], Popescu et al.”
Processing Social Media Messages in Mass Emergency (2014, background): “To address this problem, Becker et al. [2011] present an approach to classify realworld events from nonevents using Twitter.”
Semantics-driven Event Clustering in Twitter Feeds (2015, background): “Next to the text itself, other characteristic features such as the timestamp of the post, user behavioural patterns and geolocation have been successfully taken into account [1, 4, 15, 17, 18, 22].”
Harvesting ambient geospatial information from social media feeds (2011, background): “Coincident with the growth of VGI and crisis mapping (see Meier 2009; Biewald and Janah 2010; Parry 2011) we have seen the growth of data mining techniques to explore events.”
Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract (2015, neutral): “We compared our algorithm with two baselines: (1) A standard incremental clustering (IC) algorithm [Becker et al. , 2011].”