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The spread of fake news on social media seriously jeopardizes the authenticity of online content. Such misinformation has the potential to affect public opinion, government credibility, and more. Therefore, automatic detection of fake news is an important and challenging problem that has not been well solved today. Fake news has three universally recognized characteristics: the text of an article, the user response it receives, and the source users promoting it. Existing work mainly focuses on formulating a solution for a certain feature, which cannot detect fake news more accurately. In this work, we propose a model that combines all three features to achieve more accurate and automated predictions. We propose a model called TSRI, which consists of four modules: Text, Source, Response, and Integrate. The first module is based on news text content, using the BERT+LSTM model, while considering the semantic representation of sentences and the long-distance dependencies between sentences. The second module uses GAT to model the network of replying and retweeting users based on social network features. The third module is based on the user's response to the news, using LDA to find conflicting opinions in the news tweets, and based on the identified tweets' opinions, constructs the credibility of the tweets and spreads the network, and finally obtains the module's value through iterative deduction. result. The last module combines the above three and outputs whether the news is true or false. © 2023 IEEE.
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年份: 2023
页码: 91-95
语种: 英文
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