Emotions Unveiled: Detecting COVID-19 Fake News on Social Media

Farhoudinia, B., Ozturkcan, S., & Kasap, N. (2024). Emotions unveiled: Detecting COVID-19 fake news on social media. Humanities and Social Sciences Communications, 11, 640. https://doi.org/10.1057/s41599-024-03083-5

11 Pages Posted: 21 May 2024

See all articles by Bahareh Farhoudinia

Bahareh Farhoudinia

Sabanci University - Sabanci University

Selcen Ozturkcan

School of Business and Economics, Linnaeus University; Sabanci Business School, Sabanci University

Nihat Kasap

Sabanci University

Date Written: May 19, 2024

Abstract

The COVID-19 pandemic has highlighted the pernicious effects of fake news, underscoring the critical need for researchers and practitioners to detect and mitigate its spread. In this paper, we examined the importance of detecting fake news and incorporated sentiment and emotional features to detect this type of news. Specifically, we compared the sentiments and emotions associated with fake and real news using a COVID-19 Twitter dataset with labeled categories. By utilizing different sentiment and emotion lexicons, we extracted sentiments categorized as positive, negative, and neutral and eight basic emotions, anticipation, anger, joy, sadness, surprise, fear, trust, and disgust. Our analysis revealed that fake news tends to elicit more negative emotions than real news. Therefore, we propose that negative emotions could serve as vital features in developing fake news detection models. To test this hypothesis, we compared the performance metrics of three machine learning models: random forest, support vector machine (SVM), and Naïve Bayes. We evaluated the models’ effectiveness with and without emotional features. Our results demonstrated that integrating emotional features into these models substantially improved the detection performance, resulting in a more robust and reliable ability to detect fake news on social media. In this paper, we propose the use of novel features and methods that enhance the field of fake news detection. Our findings underscore the crucial role of emotions in detecting fake news and provide valuable insights into how machine-learning models can be trained to recognize these features.

Keywords: fake news, emotions, social media, machine learning, Twitter, Platform X

JEL Classification: M00

Suggested Citation

Farhoudinia, Bahareh and Ozturkcan, Selcen and Kasap, Nihat, Emotions Unveiled: Detecting COVID-19 Fake News on Social Media (May 19, 2024). Farhoudinia, B., Ozturkcan, S., & Kasap, N. (2024). Emotions unveiled: Detecting COVID-19 fake news on social media. Humanities and Social Sciences Communications, 11, 640. https://doi.org/10.1057/s41599-024-03083-5, Available at SSRN: https://ssrn.com/abstract=4833527

Bahareh Farhoudinia

Sabanci University - Sabanci University ( email )

Orhanli
Estambul, Tuzla 34956
Turkey

Selcen Ozturkcan (Contact Author)

School of Business and Economics, Linnaeus University ( email )

Växjö, 352 52
Sweden
+46 470-70 82 87 (Phone)

HOME PAGE: http://lnu.se/en/staff/selcen.ozturkcan/

Sabanci Business School, Sabanci University ( email )

Istanbul
Turkey

Nihat Kasap

Sabanci University ( email )

Orta Mahalle Üniversite Caddesi 27
Istanbul, Orhanli, 34956 Tuzla 34956
Turkey

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