Automatic Detection and Categorization of Election-Related Tweets


  • Prashanth Vijayaraghavan Massachusetts Institute of Technology
  • Soroush Vosoughi Massachusetts Institute of Technology
  • Deb Roy Massachusetts Institute of Technology



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.




How to Cite

Vijayaraghavan, P., Vosoughi, S., & Roy, D. (2021). Automatic Detection and Categorization of Election-Related Tweets. Proceedings of the International AAAI Conference on Web and Social Media, 10(1), 703-706.