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• W3136728770 abstract "The past decade has witnessed a rapid growth in the use of online social networks, such as Twitter, by individuals as well as communities for fast dissemination of information. However, as useful as this information might be, it also prospects the rapid spread of rumors. This phenomenon of rumor proliferation has persuaded researchers to work in the field of rumor detection by using the temporal, textual and author related features. From a sociological perspective, rumors are generally seen to be targeted at some important entities such as well-known politicians, actors, places etc. Some rumors are aimed at creating social unrest by spreading information about eye-catching events like highjacking, bomb attack etc. The existing works in rumor detection do not seem to harness this peculiar characteristic of rumor.This research proposes a novel approach to rumor detection by using entity recognition on the post text along with other features that imply reliability and consistency. Our hypothesis defines four different scores namely, i) Famousness score, ii) Rareness score, iii) Reliability score and iv) Consistency score. Experimental results show that the proposed features not only increase the performance of our model, but also out-perform the baseline approaches in terms of F1 score." @default.
• W3136728770 created "2021-03-29" @default.
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• W3136728770 date "2020-12-10" @default.
• W3136728770 modified "2023-10-16" @default.
• W3136728770 title "Rumor Detection on Social Networks: A Sociological Approach" @default.
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• W3136728770 doi "https://doi.org/10.1109/bigdata50022.2020.9378149" @default.
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