A Semi-Automatic Method for Efficient Detection of Stories on Social Media

Authors

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

Abstract

Twitter has become one of the main sources of news for many people. As real-world events and emergencies unfold, Twitter is abuzz with hundreds of thousands of stories about the events. Some of these stories are harmless, while others could potentially be life-saving or sources of malicious rumors. Thus, it is critically important to be able to efficiently track stories that spread on Twitter during these events. In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter. We ran a user study with 25 participants, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can track stories about real-world events.

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Published

2021-08-04

How to Cite

Vosoughi, S., & Roy, D. (2021). A Semi-Automatic Method for Efficient Detection of Stories on Social Media. Proceedings of the International AAAI Conference on Web and Social Media, 10(1), 707-710. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14809