Name
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. |
Year | Venue | DocType |
|---|---|---|
2016 | ICWSM | Conference |
Volume | Citations | PageRank |
abs/1605.05134 | 3 | 0.43 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soroush Vosoughi | 1 | 39 | 4.34 |
| Deb Roy | 2 | 1033 | 92.10 |