Abstract
Community detection on social media has attracted considerable attention for many years. However, existing methods do not reveal the relations between communities. Communities can form alliances or engage in antagonisms due to various factors, e.g., shared or conflicting goals and values. Uncovering such relations can provide better insights to understand communities and the structure of social media. According to social science findings, the attitudes that members from different communities express towards each other are largely shaped by their community membership. Hence, we hypothesize that intercommunity attitudes expressed among users in social media have the potential to reflect their inter-community relations. Therefore, we first validate this hypothesis in the context of social media. Then, inspired by the hypothesis, we develop a framework to detect communities and their relations by jointly modeling users' attitudes and social interactions. We present experimental results using three real-world social media datasets to demonstrate the efficacy of our framework.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 |
| Editors | Andrea Tagarelli, Chandan Reddy, Ulrik Brandes |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 99-106 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781538660515 |
| DOIs | |
| State | Published - Oct 24 2018 |
| Event | 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain Duration: Aug 28 2018 → Aug 31 2018 |
Other
| Other | 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 |
|---|---|
| Country | Spain |
| City | Barcelona |
| Period | 8/28/18 → 8/31/18 |
Fingerprint
ASJC Scopus subject areas
- Sociology and Political Science
- Communication
- Computer Networks and Communications
- Information Systems and Management
Cite this
Detecting antagonistic and allied communities on social media. / Salehi, Amin; Davulcu, Hasan.
Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. ed. / Andrea Tagarelli; Chandan Reddy; Ulrik Brandes. Institute of Electrical and Electronics Engineers Inc., 2018. p. 99-106 8508297.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Detecting antagonistic and allied communities on social media
AU - Salehi, Amin
AU - Davulcu, Hasan
PY - 2018/10/24
Y1 - 2018/10/24
N2 - Community detection on social media has attracted considerable attention for many years. However, existing methods do not reveal the relations between communities. Communities can form alliances or engage in antagonisms due to various factors, e.g., shared or conflicting goals and values. Uncovering such relations can provide better insights to understand communities and the structure of social media. According to social science findings, the attitudes that members from different communities express towards each other are largely shaped by their community membership. Hence, we hypothesize that intercommunity attitudes expressed among users in social media have the potential to reflect their inter-community relations. Therefore, we first validate this hypothesis in the context of social media. Then, inspired by the hypothesis, we develop a framework to detect communities and their relations by jointly modeling users' attitudes and social interactions. We present experimental results using three real-world social media datasets to demonstrate the efficacy of our framework.
AB - Community detection on social media has attracted considerable attention for many years. However, existing methods do not reveal the relations between communities. Communities can form alliances or engage in antagonisms due to various factors, e.g., shared or conflicting goals and values. Uncovering such relations can provide better insights to understand communities and the structure of social media. According to social science findings, the attitudes that members from different communities express towards each other are largely shaped by their community membership. Hence, we hypothesize that intercommunity attitudes expressed among users in social media have the potential to reflect their inter-community relations. Therefore, we first validate this hypothesis in the context of social media. Then, inspired by the hypothesis, we develop a framework to detect communities and their relations by jointly modeling users' attitudes and social interactions. We present experimental results using three real-world social media datasets to demonstrate the efficacy of our framework.
UR - http://www.scopus.com/inward/record.url?scp=85057297857&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057297857&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2018.8508297
DO - 10.1109/ASONAM.2018.8508297
M3 - Conference contribution
AN - SCOPUS:85057297857
SP - 99
EP - 106
BT - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
A2 - Tagarelli, Andrea
A2 - Reddy, Chandan
A2 - Brandes, Ulrik
PB - Institute of Electrical and Electronics Engineers Inc.
ER -