Abstract
Fact checking is an important topic that needs to be studied scientifically to determine how fake news is spread. Previous work in this area has primarily focused on document- level fact checking. In this paper, however, we will focus on individual statements and the relationship between target statements and the overall news text. In larger context, we will compare statements to known facts, which we will tag within the statement. For dual verification, we will compare our findings to forty mainstream news sources as well as the online encyclopedia (Wikipedia). If a news is detected as fake the existing techniques should block it immediately due to its function, as we cannot replace it. However if a news is detected as fake, we need at least an expert opinion or review before blocking that particular news. This process helps third-party fact-checking organizations to solve the issue; but it is also a time-consuming process. We will attempt to solve the problem of automatically identifying factual claims at the sentence level. Despite its importance, this is a relatively under-studied problem. Existing fake news systems are based on predictive models that simply classify whether a news item is fake or not. Some models use source reliability and network structure so the major challenge in these cases is to train the model. But due to the unavailability of corpora, this is impossible to accomplish. We created a new corpus for social media claims, containing statements that have been fact checked by three reputable sources, and then trained a machine learning model to predict the facts of the news. We presented a fact checking system that takes news as input and then produces an output with an aggregation such as fake, non fake or unclear. To the best of our knowledge it is the only system that has such capabilities.
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Ahmed, S., Hinkelmann, K., Corradini, F. (2022). Fact Checking: An Automatic End to End Fact Checking System. In: Lahby, M., Pathan, AS.K., Maleh, Y., Yafooz, W.M.S. (eds) Combating Fake News with Computational Intelligence Techniques. Studies in Computational Intelligence, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-90087-8_17
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