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• W2599770314 abstract "Online social networks such as Twitter are among the most important sources of information in the current era of information overload, restiveness, and uncertainty. Therefore, it is necessary to develop a model for verifying information from Twitter, which is a challenging task. We propose a new credibility assessment model for identifying implausible content on Twitter to prevent the proliferation of false/malicious information. The proposed model consists of six integrated components operating in an algorithmic form to assess the credibility of tweets. We enhanced our classifier by weighting features extracted from tweets according to their relative importance. Further, we applied our model to two different datasets created from 155,794 unique accounts. To evaluate the performance of our model, we trained two naïve Bayes models, M1 (without relative importance algorithm) and M2 (with relative importance algorithm). The results were quite encouraging: M2 achieved accuracies of 82.25 and 85.47% on the two datasets." @default.
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• W2599770314 date "2017-01-01" @default.
• W2599770314 modified "2023-10-11" @default.
• W2599770314 title "A Credibility Assessment Model for Online Social Network Content" @default.
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• W2599770314 doi "https://doi.org/10.1007/978-3-319-51367-6_3" @default.
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