Abstract
Recent political, pandemic, and social turmoil events have led to an increase in the popularity and spread of misinformation. As demonstrated by the widespread effects of the large onset of fake news, humans are inconsistent if not outright poor detectors of fake news. Thereby, many efforts are being made to automate the process of fake news detection. The most popular of these approaches include blacklisting sources and authors that are unreliable. While these tools are useful, in order to create a more complete end to end solution, we need to account for more difficult cases where reliable sources and authors release fake news. As such, the goal of this paper is to propose an approach for detecting the language and behavioral patterns that characterize fake and real news through the use of social network analysis and natural language processing techniques. We have built a model that catches many intuitive indications of real and fake news using users and submissions attributes, and thus laid the foundation for an approach that concatenates multiple embeddings for better fake news detection on social media.
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Hebroune, O., Benhiba, L. (2022). User-Enriched Embedding for Fake News Detection on Social Media. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_44
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DOI: https://doi.org/10.1007/978-3-031-07969-6_44
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