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• W2996635575 abstract "With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First we detect rumors as a type of misinformation propagation and next we go beyond detection to perform the task of rumor classification. WE explore the problem using a standard data set. We devise novel features and study their impact on the task. We experiment with various levels of preprocessing as a precursor of the classification as well as grouping of features. We achieve and f-measure of over 0.82 in RDC task in mixed rumors data set and 84 percent in a single rumor data set using a two-step classification approach." @default.
• W2996635575 created "2019-12-26" @default.
• W2996635575 creator A5038581447 @default.
• W2996635575 creator A5091271513 @default.
• W2996635575 date "2019-11-25" @default.
• W2996635575 modified "2023-09-27" @default.
• W2996635575 title "Rumor Detection and Classification for Twitter Data" @default.
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• W2996635575 hasPublicationYear "2019" @default.
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