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        <title>Exploiting Microblog Conversation Structures to Detect Rumors</title>
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        <namePart type="given">Jiawen</namePart>
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    <name type="personal">
        <namePart type="given">Hung-Yu</namePart>
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    <abstract>As one of the most popular social media platforms, Twitter has become a primary source of information for many people. Unfortunately, both valid information and rumors are propagated on Twitter due to the lack of an automatic information verification system. Twitter users communicate by replying to other users’ messages, forming a conversation structure. Using this structure, users can decide whether the information in the source tweet is a rumor by reading the tweet’s replies, which voice other users’ stances on the tweet. The majority of rumor detection researchers process such tweets based on time, ignoring the conversation structure. To reap the benefits of the Twitter conversation structure, we developed a model to detect rumors by modeling conversation structure as a graph. Thus, our model’s improved representation of the conversation structure enhances its rumor detection accuracy. The experimental results on two rumor datasets show that our model outperforms several baseline models, including a state-of-the-art model</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.473</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T Exploiting Microblog Conversation Structures to Detect Rumors
%A Li, Jiawen
%A Sujana, Yudianto
%A Kao, Hung-Yu
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F li-etal-2020-exploiting
%X As one of the most popular social media platforms, Twitter has become a primary source of information for many people. Unfortunately, both valid information and rumors are propagated on Twitter due to the lack of an automatic information verification system. Twitter users communicate by replying to other users’ messages, forming a conversation structure. Using this structure, users can decide whether the information in the source tweet is a rumor by reading the tweet’s replies, which voice other users’ stances on the tweet. The majority of rumor detection researchers process such tweets based on time, ignoring the conversation structure. To reap the benefits of the Twitter conversation structure, we developed a model to detect rumors by modeling conversation structure as a graph. Thus, our model’s improved representation of the conversation structure enhances its rumor detection accuracy. The experimental results on two rumor datasets show that our model outperforms several baseline models, including a state-of-the-art model
%R 10.18653/v1/2020.coling-main.473
%U https://aclanthology.org/2020.coling-main.473
%U https://doi.org/10.18653/v1/2020.coling-main.473
%P 5420-5429

Markdown (Informal)

[Exploiting Microblog Conversation Structures to Detect Rumors](https://aclanthology.org/2020.coling-main.473) (Li et al., COLING 2020)

• Exploiting Microblog Conversation Structures to Detect Rumors (Li et al., COLING 2020)

ACL

• Jiawen Li, Yudianto Sujana, and Hung-Yu Kao. 2020. Exploiting Microblog Conversation Structures to Detect Rumors. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5420–5429, Barcelona, Spain (Online). International Committee on Computational Linguistics.