Abstract
Nowadays social media is widely used as the source of information because of itslow cost, easy to access nature. However, consuming news from social media is a double-edgedsword because of the wide propagation of fake news, i.e., news with intentionally falseinformation. Fake news is a serious problem because it has negative impacts on individuals aswell as society large. In the social media the information is spread fast and hence detectionmechanism should be able to predict news fast enough to stop the dissemination of fake news.Therefore, detecting fake news on social media is an extremely important and also a technicallychallenging problem. In this paper, we present FakeNewsTracker, a system for fake newsunderstanding and detection. As we will show, FakeNewsTracker can automatically collect datafor news pieces and social context, which benefits further research of understanding andpredicting fake news with effective visualization techniques.
| Original language | English (US) |
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| State | Published - 2018 |
| Event | 2018 International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, BRiMS 2018 - Washington, United States Duration: Jul 10 2018 → Jul 13 2018 |
Conference
| Conference | 2018 International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, BRiMS 2018 |
|---|---|
| Country/Territory | United States |
| City | Washington |
| Period | 7/10/18 → 7/13/18 |
Keywords
- Fake News Detection
- Neural Networks
- Twitter Visualization
ASJC Scopus subject areas
- Human-Computer Interaction
- Modeling and Simulation