Oregon State University

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Event Details

MS Final Examination – Zahra Iman

Wednesday, May 25, 2016 10:00 AM - 12:00 PM

Learning Topical Social Media Sensors for Twitter
Social media sources such as Twitter represent a massively distributed social sensor over a kaleidoscope of topics ranging from social and political events to entertainment and sports news. However, due to the overwhelming volume of content, it can be difficult to identify novel and significant content within a broad theme in a timely fashion. To this end, this work proposes a scalable and practical method to automatically construct social sensors for generic topics. Specifically, given minimal supervised training content from a user, we learn to identify topical tweets from millions of features capturing content, user and social interactions on Twitter. On a corpus of over 800 million English Tweets collected from the Twitter streaming API during 2013 and 2014 and learning for 10 diverse themes ranging from social issues to celebrity deaths to the “Iran nuclear deal”, we empirically show that our learned social sensor automatically generalizes to unseen future content with high ranking and precision scores. Furthermore, we provide an extensive analysis of features and feature types across different topics that reveals, for example, that (1) largely independent of topic, simple terms are the most informative feature followed by location features and that (2) the number of unique hashtags and tweets by a user correlates more with their informativeness than their follower or friend count. In summary, this work provides an effective, and efficient way to learn topical social sensors requiring minimal user curation effort and offering strong generalization performance for identifying future topical content.

Major Advisor: Scott Sanner
Committee: Arash Termehchy
Committee: Xiaoli Fern
GCR: John Bolte

Nicole Thompson
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