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Forward Context-Aware Clickbait Tweet Identification System

Forward Context-Aware Clickbait Tweet Identification System

Rajesh Kumar Mundotiya, Naina Yadav
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 12
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781799860273|DOI: 10.4018/IJACI.2021040102
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MLA

Mundotiya, Rajesh Kumar, and Naina Yadav. "Forward Context-Aware Clickbait Tweet Identification System." IJACI vol.12, no.2 2021: pp.21-32. http://doi.org/10.4018/IJACI.2021040102

APA

Mundotiya, R. K. & Yadav, N. (2021). Forward Context-Aware Clickbait Tweet Identification System. International Journal of Ambient Computing and Intelligence (IJACI), 12(2), 21-32. http://doi.org/10.4018/IJACI.2021040102

Chicago

Mundotiya, Rajesh Kumar, and Naina Yadav. "Forward Context-Aware Clickbait Tweet Identification System," International Journal of Ambient Computing and Intelligence (IJACI) 12, no.2: 21-32. http://doi.org/10.4018/IJACI.2021040102

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Abstract

Clickbait is an elusive challenge with the prevalence of social media such as Facebook and Twitter that misleads the readers while clicking on headlines. Limited annotated data makes it onerous to design an accurate clickbait identification system. The authors address this problem by purposing deep learning-based architecture with external knowledge which trains on social media post and descriptions. The pre-trained ELMO and BERT model obtains the sentence level contextual feature as knowledge; moreover, the LSTM layer helps to prevail the word level contextual feature. Training has done at different experiments (model with EMLO, model with BERT) with different regularization techniques such as dropout, early stopping, and finetuning. Forward context-aware clickbait tweet identification system (FCCTI) with BERT finetuning and model with ELMO using glove pre-trained embedding is the best model and achieves a clickbait identification accuracy of 0.847, improving on the previous baseline for this task.

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