A Semi-Automatic Method for Efficient Detection of Stories on Social Media
Author(s)Vosoughi, Soroush; Roy, Deb K.
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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.
DepartmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)
Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM 2016)
Association for the Advancement of Artificial Intelligence (AAAI)
Vosoughi, Soroush, and Deb Kay. "A Semi-Automatic Method for Efficient Detection of Stories on Social Media." Tenth International AAAI Conference on Web and Social Media (ICWSM-16), Cologne, Germany, 17-20 May 2016, AAAI, pp.707-710.
Author's final manuscript