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This is particularly relevant in social media platforms (e.g., Facebook, Twitter, Snapchat), due to their intrinsic uncontrolled publishing mechanisms. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies: early detection of fake news is crucial. Unfortunately, the availability of information about news propagation is limited. In this paper, we provided a benchmark framework in order to analyze and discuss the most widely used and promising machine\/deep learning techniques for fake news detection, also exploiting different features combinations w.r.t. the ones proposed in the literature. Experiments conducted on well-known and widely used real-world datasets show advantages and drawbacks in terms of accuracy and efficiency for the considered approaches, even in the case of limited content information.<\/jats:p>","DOI":"10.1007\/s10844-021-00646-9","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T09:02:45Z","timestamp":1647853365000},"page":"237-261","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["A comprehensive Benchmark for fake news detection"],"prefix":"10.1007","volume":"59","author":[{"given":"Antonio","family":"Galli","sequence":"first","affiliation":[]},{"given":"Elio","family":"Masciari","sequence":"additional","affiliation":[]},{"given":"Vincenzo","family":"Moscato","sequence":"additional","affiliation":[]},{"given":"Giancarlo","family":"Sperl\u00ed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"646_CR1","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D.G., Steiner, B., Tucker, P.A., Vasudevan, V., Warden, P., ..., Zheng, X. 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