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Computer Science > Social and Information Networks

arXiv:1605.05134 (cs)
[Submitted on 17 May 2016]

Title:A Semi-automatic Method for Efficient Detection of Stories on Social Media

Authors:Soroush Vosoughi, Deb Roy
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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.
Comments: ICWSM'16, May 17-20, Cologne, Germany. In Proceedings of the 10th International AAAI Conference on Weblogs and Social Media (ICWSM 2016). Cologne, Germany
Subjects: Social and Information Networks (cs.SI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as: arXiv:1605.05134 [cs.SI]
  (or arXiv:1605.05134v1 [cs.SI] for this version)
  https://doi.org/10.48550/arXiv.1605.05134

Submission history

From: Soroush Vosoughi Dr [view email]
[v1] Tue, 17 May 2016 12:33:24 UTC (107 KB)
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