A Semi-automatic Method for Efficient Detection of Stories on Social Media
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.
Cite
@article{arxiv.1605.05134,
title = {A Semi-automatic Method for Efficient Detection of Stories on Social Media},
author = {Soroush Vosoughi and Deb Roy},
journal= {arXiv preprint arXiv:1605.05134},
year = {2016}
}
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