Details
| Original language | English |
|---|---|
| Title of host publication | WWW '23 Companion |
| Subtitle of host publication | Companion Proceedings of the ACM Web Conference 2023 |
| Pages | 1030-1038 |
| Number of pages | 9 |
| ISBN (electronic) | 9781450394161 |
| Publication status | Published - 30 Apr 2023 |
| Event | 2023 World Wide Web Conference, WWW 2023 - Austin, United States Duration: 30 Apr 2023 → 4 May 2023 |
Abstract
Social Media (SM) has become a stage for people to share thoughts, emotions, opinions, and almost every other aspect of their daily lives. This abundance of human interaction makes SM particularly attractive for social sensing. Especially during polarizing events such as political elections or referendums, users post information and encourage others to support their side, using symbols such as hashtags to represent their attitudes. However, many users choose not to attach hashtags to their messages, use a different language, or show their position only indirectly. Thus, automatically identifying their opinions becomes a more challenging task. To uncover these implicit perspectives, we propose a collaborative filtering model based on Graph Convolutional Networks that exploits the textual content in messages and the rich connections between users and topics. Moreover, our approach only requires a small annotation effort compared to state-of-the-art solutions. Nevertheless, the proposed model achieves competitive performance in predicting individuals' stances. We analyze users' attitudes ahead of two constitutional referendums in Chile in 2020 and 2022. Using two large Twitter datasets, our model achieves improvements of 3.4% in recall and 3.6% in accuracy over the baselines.
Keywords
- collaborative filtering, graph convolutional networks, recommendation system, stance prediction
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
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WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023. 2023. p. 1030-1038.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Stance Inference in Twitter through Graph Convolutional Collaborative Filtering Networks with Minimal Supervision
AU - Zhou, Zhiwei
AU - Elejalde, Erick
N1 - Funding Information: This paper is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101021866 (CRiTERIA).
PY - 2023/4/30
Y1 - 2023/4/30
N2 - Social Media (SM) has become a stage for people to share thoughts, emotions, opinions, and almost every other aspect of their daily lives. This abundance of human interaction makes SM particularly attractive for social sensing. Especially during polarizing events such as political elections or referendums, users post information and encourage others to support their side, using symbols such as hashtags to represent their attitudes. However, many users choose not to attach hashtags to their messages, use a different language, or show their position only indirectly. Thus, automatically identifying their opinions becomes a more challenging task. To uncover these implicit perspectives, we propose a collaborative filtering model based on Graph Convolutional Networks that exploits the textual content in messages and the rich connections between users and topics. Moreover, our approach only requires a small annotation effort compared to state-of-the-art solutions. Nevertheless, the proposed model achieves competitive performance in predicting individuals' stances. We analyze users' attitudes ahead of two constitutional referendums in Chile in 2020 and 2022. Using two large Twitter datasets, our model achieves improvements of 3.4% in recall and 3.6% in accuracy over the baselines.
AB - Social Media (SM) has become a stage for people to share thoughts, emotions, opinions, and almost every other aspect of their daily lives. This abundance of human interaction makes SM particularly attractive for social sensing. Especially during polarizing events such as political elections or referendums, users post information and encourage others to support their side, using symbols such as hashtags to represent their attitudes. However, many users choose not to attach hashtags to their messages, use a different language, or show their position only indirectly. Thus, automatically identifying their opinions becomes a more challenging task. To uncover these implicit perspectives, we propose a collaborative filtering model based on Graph Convolutional Networks that exploits the textual content in messages and the rich connections between users and topics. Moreover, our approach only requires a small annotation effort compared to state-of-the-art solutions. Nevertheless, the proposed model achieves competitive performance in predicting individuals' stances. We analyze users' attitudes ahead of two constitutional referendums in Chile in 2020 and 2022. Using two large Twitter datasets, our model achieves improvements of 3.4% in recall and 3.6% in accuracy over the baselines.
KW - collaborative filtering
KW - graph convolutional networks
KW - recommendation system
KW - stance prediction
UR - http://www.scopus.com/inward/record.url?scp=85159558360&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2303.15532
DO - 10.48550/arXiv.2303.15532
M3 - Conference contribution
AN - SCOPUS:85159558360
SP - 1030
EP - 1038
BT - WWW '23 Companion
T2 - 2023 World Wide Web Conference, WWW 2023
Y2 - 30 April 2023 through 4 May 2023
ER -