Predicting Stances in Twitter Conversations for Detecting Veracity of Rumors: A Neural Approach
| Autor: | Mong Li Lee, Wynne Hsu, Shruti Subramaniyam, Lahari Poddar |
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| Rok vydání: | 2018 |
| Předmět: |
Structure (mathematical logic)
Computer science media_common.quotation_subject ComputerApplications_COMPUTERSINOTHERSYSTEMS 020206 networking & telecommunications 02 engineering and technology Rumor Data science Task (project management) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Social media Conversation Transfer of learning media_common |
| Zdroj: | ICTAI |
| DOI: | 10.1109/ictai.2018.00021 |
| Popis: | Detecting rumors is a crucial task requiring significant time and manual effort in forms of investigative journalism. In social media such as Twitter, unverified information can get disseminated rapidly making early detection of potentially false rumors critical. We observe that the early reactions of people towards an emerging claim can be predictive of its veracity. We propose a novel neural network architecture using the stances of people engaging in a conversation on Twitter about a rumor for detecting its veracity. Our proposed solution comprises two key steps. We first detect the stance of each individual tweet, by considering the textual content of the tweet, its timestamp, as well as the sequential conversation structure leading up to the target tweet. Then we use the predicted stances of all tweets in a conversation tree to determine the veracity of the original rumor. We evaluate our model on the SemEval2017 rumor detection dataset and demonstrate that our solution outperforms the state-of-the-art approaches for both stance prediction and rumor veracity prediction tasks. |
| Databáze: | OpenAIRE |
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