Clickbait detection using deep learning Techniques

dc.accession.numberT00841
dc.classification.ddc006.32 PAR
dc.contributor.advisorMajumder, Prasenjit
dc.contributor.authorParikh, Apurva Ketanbhai
dc.date.accessioned2020-09-22T12:52:42Z
dc.date.accessioned2025-06-28T10:27:47Z
dc.date.available2023-02-16T12:52:42Z
dc.date.issued2020
dc.degreeM. Tech
dc.description.abstractWith the growing shift towards news consumption primarily through social media sites like Twitter, Facebook etc., most of the news agencies are prompting their stories on social media platform. These news agencies are publishing fake news on social media to generate revenue by enticing users to click on their articles. To increase the number of readers agencies use eye-catchy headlines accompanied with article link, which attract the reader to read the article. These attractive headlines are called Clickbaits. Usually, clickbait article does not meet the expectation of the user. In this work we try to develop an end-to-end clickbait detection system using Transformer based model Bidirectional Encoder Representations from Transformers (BERT). We also found few clickbait specific features which we hypothesised can be utilised along with BERT model to develop a better classifier. Our proposed approach using BERT significantly outperformed baseline paper which utilised BiLSTM.
dc.identifier.citationParikh, Apurva Ketanbhai (2020). Clickbait detection using deep learning Techniques. Dhirubhai Ambani Institute of Information and Communication Technology. vii, 37 p. (Acc.No: T00841)
dc.identifier.urihttp://drsr.daiict.ac.in/handle/123456789/919
dc.student.id201811009
dc.subjectNatural language processing
dc.subjectClickbait detection
dc.subjectFake news
dc.subjectDeep learning
dc.subjectTransformer
dc.titleClickbait detection using deep learning Techniques
dc.typeDissertation

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