Event Discovery in Social Media Feeds

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 389–398, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Event Discovery in Social Media Feeds Edward Benson, Aria Haghighi, and Regina Barzilay Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology {eob, aria42, regina}@csail.mit.edu Abstract We present a novel method for record extrac- tion from social streams such as Twitter. Un- like typical extraction setups, these environ- ments are characterized by short, one sentence messages with heavily colloquial speech. To further complicate matters, individual mes- sages may not express the full relation to be uncovered, as is often assumed in extraction tasks. We develop a graphical model that ad- dresses these problems by learning a latent set of records and a record-message alignment si- multaneously; the output of our model is a set of canonical records, the values of which are consistent with aligned messages. We demonstrate that our approach is able to accu- rately induce event records from Twitter mes- sages, evaluated against events from a local city guide. Our method achieves significant error reduction over baseline methods.1 1 Introduction We propose a method for discovering event records from social media feeds such as Twitter. The task of extracting event properties has been well studied in the context of formal media (e.g., newswire), but data sources such as Twitter pose new challenges. Social media messages are often short, make heavy use of colloquial language, and require situational context for interpretation (see examples in Figure 1). Not all properties of an event may be expressed in a single message, and the mapping between mes- sages and canonical event records is not obvious. 1Data and code available at http://groups.csail. mit.edu/rbg/code/twitter Carnegie Hall Artist Venue Craig Ferguson DJ Pauly D Terminal 5 Seated at @carnegiehall waiting for @CraigyFerg’s show to begin RT @leerader : getting REALLY stoked for #CraigyAtCarnegie sat night. Craig, , want to join us for dinner at the pub across the street? 5pm, be there! @DJPaulyD absolutely killed it at Terminal 5 last night. @DJPaulyD : DJ Pauly D Terminal 5 NYC Insanity ! #ohyeah @keadour @kellaferr24 Craig, nice seeing you at #noelnight this weekend @becksdavis! Twitter Messages Records Figure 1: Examples of Twitter messages, along with automatically extracted records. These properties of social media streams make exist- ing extraction techniques significantly less effective. Despite these challenges, this data exhibits an im- portant property that makes learning amenable: the multitude of messages referencing the same event. Our goal is to induce a comprehensive set of event records given a seed set of example records, such as a city event calendar table. While such resources are widely available online, they are typically high precision, but low recall. Social media is a natural place to discover new events missed by curation, but mentioned online by someone planning to attend. We formulate our approach as a structured graphi- cal model which simultaneously analyzes individual messages, clusters them according to event, and in- duces a canonical value …

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