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Social Media Veracity Detection System Using Calibrate Classifier

Abstract : In the last decade, social media has grown extremely fast and captured tens of millions of users are online at any time. Social media is a powerful tool to share information in the form of articles, images, URLs and, videos online. Concurrently it also spreads the rumors. To fight against the rumors, media users need a verification tool to verify the fake post on Twitter. The main motivation of this research work is to find out which classification model helps to detecting the rumor messages. The proposed system adopts three feature extraction techniques namely Term Frequency-Inverse Document Frequency, Count-Vectorizer and Hashing-Vectorizer. The authors proposed a Calibrate Classifier model to detect the rumor messages in twitter and this model has been tested on real-time event#gaja tweets. The proposed calibrate model shows better results for rumor detection than the other ensemble models.
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Submitted on : Thursday, November 18, 2021 - 2:20:26 PM
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P. Suthanthiradevi, S. Karthika. Social Media Veracity Detection System Using Calibrate Classifier. 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.85-98, ⟨10.1007/978-3-030-63467-4_7⟩. ⟨hal-03434781⟩



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