Abstract
Social Media Platforms (SMPs) in general and messaging platforms, namely WhatsApp, have changed how people connect. Unfortunately, SMPs are often used to spread fake information. We focus on images shared on the WhatsApp platform; our goal is to detect whether the image is fake. Our main contribution is in terms of feature engineering. Given an image and meta-data, we compute three features: (1) image content-based features, (2) temporal features using the timestamps at which images were shared, and (3) social context features based on the users who shared images. We provide these features into machine learning models to predict whether the input is fake or not. We evaluate our approach on a fact-checked WhatsApp image dataset released in 2020 gathered during 2.5 months containing 810K and 34K images shared on WhatsApp by 63K and 17K WhatsApp users in India and Brazil. We observed that temporal and social contextual features are essential predictors for fake image detection. Counter-intuitively, we found that image content features derived by CNNs using raw images are not giving promising results in comparison with socio-temporal features, but they are better than random prediction. Our best model uses ensemble learning which fuses the outcomes of Support vector machines, Random Forest, and Logistic Regression using socio-temporal features.
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Notes
We shall share code and dataset used in this work with fellow researchers upon request.
https://opensource.google/projects/tesseract
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Kaur, M., Daryani, P., Varshney, M. et al. Detection of fake images on whatsApp using socio-temporal features. Soc. Netw. Anal. Min. 12, 58 (2022). https://doi.org/10.1007/s13278-022-00883-y
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DOI: https://doi.org/10.1007/s13278-022-00883-y