Abstract:
To understand different aspects of online human behaviors, e.g., the public stances toward various social and political issues, contextual target-specific stance detectio...Show MoreMetadata
Abstract:
To understand different aspects of online human behaviors, e.g., the public stances toward various social and political issues, contextual target-specific stance detection has become one of the most important studies on social media. Considering the lack of appropriate data for the studies of contextual target-specific stance detection on Twitter, which is one of the most popular online social platforms worldwide, we introduce CTSDT, a new dataset that consists of a large number of annotated target-specific conversations collected from Twitter. Furthermore, we propose a new contextual target-specific stance detection model called ConMulAttn, which is the first method that can learn both the contents of the posts and the concrete relationships between the posts in a conversation. We conduct extensive evaluation using CTSDT as well as another two popular datasets, CreateDebate and ConvinceMe, for contextual target-specific stance detection. The evaluation results validate the necessity of introducing our dataset CTSDT. Besides, according to the evaluation results, our proposed model ConMulAttn can outperform the state-of-the-art contextual target-specific stance detection method by up to 25% in F1 score, indicating the effectiveness and superiority of our solution. Our study has the potential to assist policymakers in utilizing conversation data from online social platforms to efficiently gain real-time insights into public stances on target topics, such as vaccination.
Published in: 2023 IEEE International Conference on Data Mining (ICDM)
Date of Conference: 01-04 December 2023
Date Added to IEEE Xplore: 05 February 2024
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- IEEE Keywords
- Index Terms
- Twitter ,
- Stance Detection ,
- Social Media ,
- F1 Score ,
- Detection Studies ,
- Popular Datasets ,
- Post Content ,
- Different Aspects Of Behavior ,
- Online Social Platforms ,
- Aspects Of Human Behavior ,
- Contextual Information ,
- Cohen’s Kappa ,
- Types Of Relationships ,
- Social Media Platforms ,
- COVID-19 Vaccine ,
- Attention Mechanism ,
- Annotation Data ,
- Feed-forward Network ,
- Annotation Process ,
- Positional Encoding ,
- Context Encoder ,
- Attention Scores ,
- Monthly Active Users ,
- Hashtags ,
- Manual Labeling ,
- Conversational Context ,
- Relevant Datasets ,
- Popular Social Media Platforms ,
- Human Labor
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Twitter ,
- Stance Detection ,
- Social Media ,
- F1 Score ,
- Detection Studies ,
- Popular Datasets ,
- Post Content ,
- Different Aspects Of Behavior ,
- Online Social Platforms ,
- Aspects Of Human Behavior ,
- Contextual Information ,
- Cohen’s Kappa ,
- Types Of Relationships ,
- Social Media Platforms ,
- COVID-19 Vaccine ,
- Attention Mechanism ,
- Annotation Data ,
- Feed-forward Network ,
- Annotation Process ,
- Positional Encoding ,
- Context Encoder ,
- Attention Scores ,
- Monthly Active Users ,
- Hashtags ,
- Manual Labeling ,
- Conversational Context ,
- Relevant Datasets ,
- Popular Social Media Platforms ,
- Human Labor
- Author Keywords