Automatic Detection and Categorization of Election-Related Tweets
Name
pv_sv_dr_icwsm2016.pdf
Description
Main article
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644.46 KB
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Author(s)
Vijayaraghavan, Prashanth
Vosoughi, Soroush
Roy, Deb K
Date Issued
May 2016
Journal
Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM 2016)
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Citation
Vijayaraghavan, Prashanth, Soroush Vosoughi, and Deb Roy. "Automatic Detection and Categorization of Election-Related Tweets." Tenth International AAAI Conference on Web and Social Media (ICWSM 2016), Cologne, Germany, 17-20 May 2016, AAAI, pp. 703-706.
Version
Author's final manuscript
Abstract
With the rise in popularity of public social media and micro-blogging services, most notably Twitter, the people have found a venue to hear and be heard by their peers without an intermediary. As a consequence, and aided by the public nature of Twitter, political scientists now potentially have the means to analyse and understand the narratives that organically form, spread and decline among the public in a political campaign.However, the volume and diversity of the conversation on Twitter, combined with its noisy and idiosyncratic nature, make this a hard task. Thus, advanced data mining and language processing techniques are required to process and analyse the data. In this paper, we present and evaluate a technical framework, based on recent advances in deep neural networks, for identifying and analysing election-related conversation on Twitter on a continuous, longitudinal basis. Our models can detect election-related tweets with an F-score of 0.92 and can categorize these tweets into 22 topics with an F-score of 0.90.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/view/13159/12834
