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MS_Thesis_Submission_V3.pdf (501.42 KB)

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Abstract Header

Forecasting event outcomes from user predictions on Twitter

Swamy, Sandesh
http://orcid.org/0000-0001-6069-580X
http://rave.ohiolink.edu/etdc/view?acc_num=osu1492692142585459

Abstract Details

2017, Master of Science, Ohio State University, Computer Science and Engineering.
Social media websites like Twitter provide an ideal environment for people to express their opinions and beliefs about upcoming events. When an event like the Oscars is around the corner, users on social media websites try to make predictions about potential winners at the event. For example, Leo will finally win the Oscar for Revenant! suggests that the author of the tweet believes that Leonardo DiCaprio will win at the Oscars. Can user predictions on Twitter be an indicator of the actual outcome of these events? This work focuses on exploring the answer to this question by using the concept of veridicality. In social media, we find tweets of varying degrees of veridicality. Before the 2017 Oscars, we see tweets about Moonlight such as Moonlight will win Best Picture! vs. Moonlight will lose to La La Land. and I need Moonlight to win!. The first tweet indicates that the author believes, with certainty, that Moonlight will win. This is positive veridicality towards an outcome. The second tweet indicates the opposite - Moonlight not winning - and is an expression of negative veridicality towards the event. We build a classifier which can automatically detect the veridicality of a given tweet with high precision. Further, by using this classifier to detect tweets with positive veridicality, it can be shown that we can make predictions on outcomes of speculative events much better than sentiment and volume-based predictions made on the same events. Our analysis also allows us to retrospectively predict surprise outcomes in such speculative events. We also show that our method can be used to assess the reliability of accounts which are making predictions on such events.
Alan Ritter (Advisor)
Marie-Catherine de Marneffe (Advisor)
Wei Xu (Committee Member)
29 p.
Computer Engineering; Computer Science; Language; Linguistics

Recommended Citations

Citations

  • Swamy, S. (2017). Forecasting event outcomes from user predictions on Twitter [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492692142585459

    APA Style (7th edition)

  • Swamy, Sandesh. Forecasting event outcomes from user predictions on Twitter. 2017. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1492692142585459.

    MLA Style (8th edition)

  • Swamy, Sandesh. "Forecasting event outcomes from user predictions on Twitter." Master's thesis, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492692142585459

    Chicago Manual of Style (17th edition)

osu1492692142585459
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© 2017, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.