Abstract
The Internet has shaped how people gather knowledge, learn from their surroundings, form their individual opinions, and deal with socially relevant topics. In a time of polarization, when the news that we see is twisted according to one’s view, extremely one-sided views aim to conquer the internet. In such a case, it is of utmost importance to devise an algorithm that can outperform and overcome such biases. We propose to build a convolutional neural network by utilizing sentence embeddings from language representation models like BERT, RoBERTa, DistilBERT, and XLNet, which would be able to classify whether an article displays a hyper-partisan narrative or not. We analyze the writing style of the author rather than depending on fact verification to prove an article’s underlying bias. Our model gives an accuracy up to 88% with BERTweet-base. Such a model can actively prevent the spread of political propaganda through news outlets and can lead to the public consuming unbiased and accurate information.
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Acknowledgements
We are grateful to the Department of Computer Science and Engineering Delhi Technological University for providing us the labs and resources for performing our study. We are thankful to all other faculty members of our department for their guidance, and our parents for their encouragement.
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Kumar, A., Tyagi, U., Grover, T., Ghosh, A. (2022). Fighting Media Hyper-partisanship with Modern Language Representation Models. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 90. Springer, Singapore. https://doi.org/10.1007/978-981-16-6289-8_6
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DOI: https://doi.org/10.1007/978-981-16-6289-8_6
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