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Identifying click baits using various machine learning and deep learning techniques

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Abstract

In today’s world, most readers prefer to read news online as they get instant access to what is happening right now. Furthermore, personalized recommendations help in keeping users engaged. Along with these virtues, online news has some vices as well. One such vice is the presence of alluring social media posts (tweets) relating to news articles whose sole purpose is to draw the attention of the users rather than directing them to read the actual content. Such posts are referred to as click baits. The objective of this paper is to develop a system which is capable of predicting how likely are the social media posts (tweets) relating to new articles tend to be click baits. GloVe embeddings [Pennington et al. in: Empirical methods in natural language processing (EMNLP), pp 1532–1543, 2014] have been used to represent text data numerically. Various novel features (like Word mover’s distances (Kusner et al. in: Proceedings of the 32nd international conference on international conference on machine learning, ICML’15, vol 37, pp 957–966, 2015), subjectivity, polarity of the tweets and so on) have been engineered. Several machine learning-based models like Logistic Regression, Random Forest, XG-Boost and Light GBM have been trained for classification. Moreover, we have also implemented a few deep learning-based models like Deep Neural Networks and Long Short Term Memory for developing this predictive system.

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Acknowledgements

We thank Mr Yashu Kant Gupta, Mr Asif Iquebal Ajazi, Mr Uttam Kumar Pandey and Dr Swati Agarwal for their valuable time, guidance and support.

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  1. Artificial Intelligence, Personal Investments, Centre of Excellence Fidelity Investments, Bangalore, India

    Sohom Ghosh

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  1. Sohom Ghosh
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Correspondence to Sohom Ghosh.

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Part of this work has been done when the author was previously associated with Times Internet and BITS, Pilani.

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Ghosh, S. Identifying click baits using various machine learning and deep learning techniques. Int. j. inf. tecnol. 13, 1235–1242 (2021). https://doi.org/10.1007/s41870-020-00473-1

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