TY - GEN
T1 - Contextual Target-Specific Stance Detection on Twitter
T2 - 23rd IEEE International Conference on Data Mining, ICDM 2023
AU - Li, Yupeng
AU - Wen, Dacheng
AU - He, Haorui
AU - Guo, Jianxiong
AU - Ning, Xuan
AU - Lau, Francis C.M.
N1 - This work is supported by Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515011583 and No. 2023A1515011562), One-off Tier 2 Start-up Grant (2020/2021) of Hong Kong Baptist University (Ref. RCOFSGT2/ 20-21/COMM/002), Startup Grant (Tier 1) for New Academics AY2020/21 of Hong Kong Baptist University, the AI-Info Communication Study (AIS) Scheme 2021/22 (Ref. AIS 21-22/06), UIC Start-up Fund (No. UICR0700026-22), National Natural Science Foundation of China (No. 62202402 and No. 62202055), Germany/Hong Kong Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the German Academic Exchange Service of Germany (No. G-HKBU203/22), and Hong Kong RGC Early Career Scheme (No. 22202423).
Publisher Copyright:
© 2023 IEEE.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - 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.
AB - 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.
KW - Conversation Context
KW - Dataset
KW - Target-Specific Stance Detection
UR - http://www.scopus.com/inward/record.url?scp=85185404825&partnerID=8YFLogxK
U2 - 10.1109/ICDM58522.2023.00045
DO - 10.1109/ICDM58522.2023.00045
M3 - Conference proceeding
AN - SCOPUS:85185404825
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 359
EP - 367
BT - Proceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023
A2 - Chen, Guihai
A2 - Khan, Latifur
A2 - Gao, Xiaofeng
A2 - Qiu, Meikang
A2 - Pedrycz, Witold
A2 - Wu, Xindong
PB - IEEE
Y2 - 1 December 2023 through 4 December 2023
ER -
