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Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision
The problem of automatic detection of fake news in social media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded as a straight-forward, binary classification problem, the major challenge is the collection of large enough training corpora, since manual annotation of tweets as fake or non-fake news is an expensive and tedious endeavor, and recent approaches utilizing distributional semantics require large training corpora. In thisdoi:10.3390/fi13050114 fatcat:jtipjffedbeubo5kti33l5vtry