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• W2901823655 abstract "Twitter offers social media platform in the form of microblog in which becomes social judgment and updates. The popularity of Twitter shows that there is growth in quantity of information. Not all user posted content can be trusted. This can be a serious impact on users who are using Twitter as a source of information and decision making. Users of Twitter are expected to be independently able to obtain credible information from those who are credible in their expertise field. This research measurement model of user credibility in delivering information based on topic areas of competence and proposed method with machine learning. The features consist of visible feature and tweet activity. Education becomes a feature that affects user identification. The result shows that algorithm of Neural Network using parameter learning rate of 0.35 and momentum of 0.2 resulted in a good performance with an accuracy of 77.46%." @default.
• W2901823655 created "2018-11-29" @default.
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• W2901823655 date "2017-11-01" @default.
• W2901823655 modified "2023-09-24" @default.
• W2901823655 title "Identification of Twitter User Credibility Using Machine Learning" @default.
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• W2901823655 doi "https://doi.org/10.1109/icici-bme.2017.8537765" @default.
• W2901823655 hasPublicationYear "2017" @default.
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