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- W3093750809 abstract "Social media platforms have become an integral part of our lives. We rely on them for entertainment purpose, social interactions and to learn what's happening around the world. One such social media network `Twitter' has become extensively popular around the world, majorly for sharing news and important information. As it is rapidly being used to share news and information, it is also causing a problem which implicitly comes with sharing of news without fact checking in place, which is, spreading of misinformation and rumors. To tackle this challenge, we propose a novel method wherein we leverage the structural and graphical properties of a tweet's propagation and tweet's text which tells us how a news of a specific class spreads, the characteristics of users involved in spreading that news and the linguistic cue of the source of news. This methodology extracts user features (like account verified, user description, followers count etc.) by modeling each user as a node and creating a graphical network of users retweeting the source tweet. We are extracting the textual content of source tweets in form of RoBERTa text's vector representations which is the current state-of-the-art for text embedding. The model is trained on benchmark datasets of Twitter15 and Twitter16 along with scrapped data of users retweeting in their current state. The results have been promising for rumor and fake detection and outperforms current state-of-the-art algorithms." @default.
- W3093750809 created "2020-10-29" @default.
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- W3093750809 date "2020-09-01" @default.
- W3093750809 modified "2023-10-14" @default.
- W3093750809 title "Classification of Propagation Path and Tweets for Rumor Detection using Graphical Convolutional Networks and Transformer based Encodings" @default.
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- W3093750809 doi "https://doi.org/10.1109/bigmm50055.2020.00034" @default.
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