08/12/2020

Exploiting Microblog Conversation Structures to Detect Rumors

Jiawen Li, Yudianto Sujana, Hung-Yu Kao

Keywords:

Abstract Paper Similar Papers

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

The video of this talk cannot be embedded. You can watch it here:
https://underline.io/lecture/6125-exploiting-microblog-conversation-structures-to-detect-rumors
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at COLING 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 11:32 
 0
 0
 0
 0
 7:12 
 0
 0
 0
 0
 13:00 
 0
 0
 0
 0
 12:00 
 0
 0
 0
 0
 14:57 
 0
 0
 0
 0
 15:58 
 0
 0
 0
 0
 12:16 
 0
 0
 0
 0
 17:22 
 0
 0
 0
 0
 8:57 
 0
 0
 0
 0
 13:42 
 0
 0
 0
 0
 12:07 
 0
 0
 0
 0
 14:17 
 0
 0
 0
 0
 14:17 
 0
 0
 0
 0
 9:01 
 0
 0
 0
 0
 14:11 
 0
 0
 0
 0
 12:13 
 0
 0
 0
 0
 15:07 
 0
 0
 0
 0
 9:59 
 0
 0
 0
 0
 11:43 
 0
 0
 0
 0
 3:03 
 0
 0
 0
 0
 12:38 
 0
 0
 0
 0
 15:57 
 0
 0
 0
 0
 14:03 
 0
 0
 0
 0
 11:58 
 0
 0
 0
 0
 7:16 
 0
 0
 0
 0
 8:15 
 0
 0
 0
 0
 7:23 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 7:57 
 0
 0
 0
 0
 7:06 
 0
 0
 0
 0
 11:12 
 0
 0
 0
 0
 3:47 
 0
 0
 0
 0
 10:47 
 0
 0
 0
 0
 6:19 
 0
 0
 0
 0
 12:32 
 0
 0
 0
 0
 8:11 
 0
 0
 0
 0
 3:01 
 0
 0
 0
 0
 9:59 
 0
 0
 0
 0
 11:57 
 0
 0
 0
 0
 14:56