Leveraging data analytics for detection and impact evaluation of fake news and deepfakes in social networks

Tony Mathew Abraham, Tao Wen, Ting Wu and Yu-wang Chen ()
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Tony Mathew Abraham: The University of Manchester
Tao Wen: The University of Manchester
Ting Wu: Manchester Metropolitan University Business School
Yu-wang Chen: The University of Manchester

Palgrave Communications, 2025, vol. 12, issue 1, 1-13

Abstract: Abstract The past decade has seen a rapid and vast adoption of social media globally and over sixty percent of people were connected online through various social media platforms as of the start of 2024. Despite many advantages social media offers, one of the most significant challenges is the rapid rise of fake news and AI-generated deepfakes across these social networks. The spread of fake news and deepfakes can lead to a series of negative impacts, such as social trust, economic consequences, public health and safety crises, as demonstrated during the COVID-19 pandemic. Hence, it is more important now than ever to develop solutions to identify such fake news and deepfakes, and curb their spread. This paper begins with a review of the literature on the definitions of fake news and deepfakes, their different types and major differences, and the ways they spread. Building on this literature research, this paper aims to analyse how fake news can be identified using machine learning models, and understand how data analytics can be leveraged to evaluate the impact of such fake news on public behaviour and trust. A fake news detection framework is developed, where TF-IDF vectorization and bag of n-grams methods are implemented to extract text features, and six typical machine learning models are used to detect fake news, with the XGBoost classifier achieving the highest accuracy using both feature extraction methods. Additionally, a convolutional neural network model is designed to detect deepfake images with two distinct architectures, namely, ResNet50 and DenseNet121. To analyse the emotional impact of fake news on public behaviour and trust, a trained natural language toolkit called VADER lexicon is used to assign sentiment polarity and emotion strength to articles. The rampant rise of deepfake technology poses huge risks to social trust and privacy issues, which impacts both individuals and society at large, and leveraging the effective use of data analytics, machine learning and AI techniques can help prevent irreparable damage and mitigate the negative impacts of deepfakes in social networks. Finally, the paper discusses some practical solutions to mitigate the negative impacts of fake news and deepfakes.

Date: 2025
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DOI: 10.1057/s41599-025-05389-4

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