Improved Target-Specific Stance Detection on Social Media Platforms by Delving into Conversation Threads

doi:10.1109/TCSS.2023.3320723

	Improved Target-Specific Stance Detection on Social Media Platforms by Delving into Conversation Threads
	Li, Yupeng 1; He, Haorui 1,2,3; Wang, Shaonan4,5 ; Lau, Francis C. M.6; Song, Yunya 7
发表期刊	IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
ISSN	2329-924X
	2023-10-13
页码	12
通讯作者	He, Haorui(hehaorui11@gmail.com)
摘要	Target-specific stance detection on social media, which aims at classifying a textual data instance such as a post or a comment into a stance class of a target issue, is an emerging opinion mining paradigm of importance. An example application would be to overcome vaccine hesitancy in combating the coronavirus pandemic. Existing stance detection strategies rely merely on the individual instances which cannot always capture the expressed stance of a given target. We address a new task called conversational stance detection (CSD) which is to infer the stance toward a given target (e.g., COVID-19 vaccination) when given a data instance and its corresponding conversation thread. To carry out the task, we first propose a benchmarking CSD dataset with annotations of stances and the structures of conversation threads among the instances, which is based on six major social media platforms in Hong Kong. To infer the desired stances from both data instances and conversation threads, we propose a model called Branch-bidirectional encoder representations from transformers (BERT) that incorporates contextual information in conversation threads. Extensive experiments on our CSD dataset show that our proposed model outperforms all the baseline models that do not make use of contextual information. Specifically, it improves the F1 score by 10.3% compared with the state-of-the-art method in the SemEval-2016 Task 6 competition. This shows the potential of incorporating rich contextual information on detecting target-specific stances on social media platforms and suggests a more practical way to construct future stance detection tasks.
关键词	Message systems Task analysis Oral communication Social networking (online) Vaccines COVID-19 Context modeling Conversation threads opinion mining social media platform target-specific stance detection
DOI	10.1109/TCSS.2023.3320723
关键词[WOS]	ARGUMENTATION ; TWEETS
收录类别	SCI
语种	英语
资助项目	Guangdong Basic and Applied Basic Research Foundation[2022A1515011583] ; Guangdong Basic and Applied Basic Research Foundation[2023A1515011562] ; One-off Tier 2 Start-up Grant of Hong Kong Baptist University[RC-OFSGT2/20-21/COMM/002] ; Startup Grant (Tier 1)for New Academics AY2020/21 of Hong Kong Baptist University ; AI-Info Communication Study (AIS) Scheme 2021/22[AIS21-22/06] ; National Natural Science Foundation of China[62202402] ; Research Grants Council of Hong Kong ; German Academic Exchange Service of Germany[G-HKBU203/22] ; Hong Kong RGC Early Career Scheme[22202423] ; Initiation Grant for Faculty Niche Research Areas of Hong Kong Baptist University[RC-FNRA-IG/21-22/COMF/01]
项目资助者	Guangdong Basic and Applied Basic Research Foundation ; One-off Tier 2 Start-up Grant of Hong Kong Baptist University ; Startup Grant (Tier 1)for New Academics AY2020/21 of Hong Kong Baptist University ; AI-Info Communication Study (AIS) Scheme 2021/22 ; National Natural Science Foundation of China ; Research Grants Council of Hong Kong ; German Academic Exchange Service of Germany ; Hong Kong RGC Early Career Scheme ; Initiation Grant for Faculty Niche Research Areas of Hong Kong Baptist University
WOS研究方向	Computer Science
WOS类目	Computer Science, Cybernetics ; Computer Science, Information Systems
WOS记录号	WOS:001088288200002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54347
专题	多模态人工智能系统全国重点实验室多模态人工智能系统全国重点实验室_自然语言处理
通讯作者	He, Haorui
作者单位	1.Hong Kong Baptist Univ, Dept Interact Media, Hong Kong, Peoples R China 2.Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing 210003, Peoples R China 3.Chinese Univ Hong Kong, Shenzhen 518172, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 6.Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China 7.Hong Kong Baptist Univ, Dept Journalism, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Li, Yupeng,He, Haorui,Wang, Shaonan,et al. Improved Target-Specific Stance Detection on Social Media Platforms by Delving into Conversation Threads[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2023:12.
APA	Li, Yupeng,He, Haorui,Wang, Shaonan,Lau, Francis C. M.,&Song, Yunya.(2023).Improved Target-Specific Stance Detection on Social Media Platforms by Delving into Conversation Threads.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,12.
MLA	Li, Yupeng,et al."Improved Target-Specific Stance Detection on Social Media Platforms by Delving into Conversation Threads".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2023):12.