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

文献类型：期刊论文

作者Li, Yupeng7; He, Haorui5,6,7; Wang, Shaonan3,4; Lau, Francis C. M.2; Song, Yunya1
刊名IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
出版日期2023-10-13
页码12
ISSN号2329-924X
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
DOI10.1109/TCSS.2023.3320723
通讯作者He, Haorui(hehaorui11@gmail.com)
英文摘要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.
WOS关键词ARGUMENTATION ; TWEETS
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]
WOS研究方向Computer Science
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001088288200002
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/54346]  
专题多模态人工智能系统全国重点实验室
模式识别国家重点实验室_自然语言处理
通讯作者He, Haorui
作者单位1.Hong Kong Baptist Univ, Dept Journalism, Hong Kong, Peoples R China
2.Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
5.Chinese Univ Hong Kong, Shenzhen 518172, Peoples R China
6.Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing 210003, Peoples R China
7.Hong Kong Baptist Univ, Dept Interact Media, 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.

入库方式： OAI收割

来源：自动化研究所

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