Profiling Fake News Spreaders on Twitter: A Clickbait and Linguistic Feature Based Scheme

Agarwal, Raksha; Gupta, Sharut; Chatterjee, Niladri

doi:10.1007/978-3-031-08473-7_32

Cited by 4 publications

(1 citation statement)

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“…Thakur does other research about clickbait detection by using deep learning [8], [9] which suggests the recurrent CNN overcomes the heavy feature engineering in clickbait detection. This method has been tested and turns out that the accuracy is better than the other clickbait-detection algorithms such as LSTM [10]- [12], CNN [8], [13], [14], BERT [15], [16], or conventional machine learning algorithms [17]- [21].…”

Section: Recent Workmentioning

confidence: 99%

Identifying clickbait in online news using deep learning

2023

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Several industries use clickbait techniques as their strategy to increase the number of readers for their news. Some news companies implement catchy headlines and images in their news article links, with the expectation that the readers will be interested in reading the news and click the provided link. The majority of the news is not hoax news. However, the content might not be as grand as the catchy headlines and images provided to the readers. This research aims to explore the classification model using machine learning to identify if the headlines are classified as clickbait in online news. This research explores several machine learning techniques to classify clickbait in online news and comprehensively explain the results. Several popular machine learning techniques were implemented and explored in this research. The results demonstrate that the model trained with fast large margin provides the best accuracy and classification error (90% and 10%, respectively). Moreover, to improve the performance, bidirectional encoder representations from transformers architecture was used to model clickbait in online news. The best BERT model achieved 98.86% in the test accuracy. BERT model requires more time to train (0.9 hour) compared to machine learning (0.4 hour).

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