Abstract
Rumor Detection has attracted a lot of attention from the research communities ever since there has been a boom in social networking sites like Twitter, Facebook, etc., for spreading information and news at a very fast pace. We aim to analyze the various machine learning algorithms as well as deep learning algorithms on PHEME dataset which is a benchmark dataset and then propose an avant-garde method for detecting rumors based on deep learning architecture. In our work a detailed comparison of shallow classifiers as well as deep classifiers on the basis of performance parameters (accuracy, precision, and recall) is done which provides a deeper insight into the field of rumor detection. Same goes for the comparison between the deep learning techniques. On application of machine learning algorithms on PHEME dataset, maximum accuracy of 78.54% is achieved using conventional statistical TF-IDF weighing. However, on application of deep learning algorithms using word embeddings, results improve drastically, with Bidirectional LSTM yielding the highest accuracy of 91.1%.
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Kumar, A., Singh, V., Ali, T., Pal, S., Singh, J. (2020). Empirical Evaluation of Shallow and Deep Classifiers for Rumor Detection. In: Sharma, H., Govindan, K., Poonia, R., Kumar, S., El-Medany, W. (eds) Advances in Computing and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0222-4_21
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DOI: https://doi.org/10.1007/978-981-15-0222-4_21
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