Fake News Detection - Social Article Fusion

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Presentation transcript:

Fake News Detection - Social Article Fusion FakeNewsTracker: A Tool for Fake News Collection, Detection, and Visualization Kai Shu, Deepak Mahudeswaran, Huan Liu Arizona State University {kai.shu, dmahudes, huan.liu}@asu.edu Introduction Fake News Detection is an important problem because of its social impacts. It is challenging because of Lack of adequate labeled data Changing topics of fake news in social media Proposed Framework Contributions: An end-to-end framework for the fake news collection, detection and visualization An ability to collect fake news pieces in streaming manner A detection mechanism using temporal social engagements and news content Fake News Detection - Social Article Fusion Detect using news context and social context Learn News representation using Autoencoder Learn Social temporal engagements using RNN Jointly optimize the output of both networks Experimental Results Proposed model performs better when both news content and social engagements are used. Visualization Web interface for tweet and feature visualization Data Exploration including trends, geo location, social network and topics Visualizations to compare the feature significance and model performance Future Work Extend FakeNewsTracker to establish a Fake-News Repository Extend FakeNewsTracker to collect data from different sources or platforms This material is based upon work supported by, or in part by, the ONR grant N00014-16-1-2257. Data Mining and Machine Learning Lab