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.
Keywords
Social mediaComputer scienceTrack (disk drive)World Wide WebData science
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Publication Info
- Year
- 2021
- Type
- article
- Volume
- 10
- Issue
- 1
- Pages
- 707-710
- Citations
- 11
- Access
- Closed
External Links
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Cite This
Soroush Vosoughi,
Deb Roy
(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.
https://doi.org/10.1609/icwsm.v10i1.14809
Identifiers
- DOI
- 10.1609/icwsm.v10i1.14809
- arXiv
- 1605.05134