research-article

Discovering Strategic Behaviors for Collaborative Content-Production in Social Networks

Authors:

Hari SundaramAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 2078 - 2088

https://doi.org/10.1145/3366423.3380274

Published: 20 April 2020 Publication History

Abstract

Some social networks provide explicit mechanisms to allocate social rewards such as reputation based on users’ actions, while the mechanism is more opaque in other networks. Nonetheless, there are always individuals who obtain greater rewards and reputation than their peers. An intuitive yet important question to ask is whether these successful users employ strategic behaviors to become influential. It might appear that the influencers ”have gamed the system.” However, it remains difficult to conclude the rationality of their actions due to factors like the combinatorial strategy space, inability to determine payoffs, and resource limitations faced by individuals. The challenging nature of this question has drawn attention from both the theory and data mining communities. Therefore, in this paper, we are motivated to investigate if resource-limited individuals discover strategic behaviors associated with high payoffs when producing collaborative/interactive content in social networks. We propose a novel framework of Dynamic Dual Attention Networks (DDAN) which models individuals’ content production strategies through a generative process, under the influence of social interactions involved in the process. Extensive experimental results illustrate the model’s effectiveness in user behavior modeling. We make three strong empirical findings: (1) Different strategies give rise to different social payoffs; (2) The best performing individuals exhibit stability in their preference over the discovered strategies, which indicates the emergence of strategic behavior; and (3) The stability of a user’s preference is correlated with high payoffs.

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Index Terms

Discovering Strategic Behaviors for Collaborative Content-Production in Social Networks

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Cited By

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https://dl.acm.org/doi/10.1016/j.procs.2023.10.098
Yang JYang LWang HGao YZhao YXie XLu Y(2023)Representation learning for knowledge fusion and reasoning in Cyber–Physical–Social Systems: Survey and perspectivesInformation Fusion10.1016/j.inffus.2022.09.00390(59-73)Online publication date: Feb-2023
https://doi.org/10.1016/j.inffus.2022.09.003
Li WZhang CZhou XJin Q(2023)Dynamic Multi-view Group Preference Learning for group behavior prediction in social networksExpert Systems with Applications10.1016/j.eswa.2023.120553231(120553)Online publication date: Nov-2023
https://doi.org/10.1016/j.eswa.2023.120553
Sato RYamada MKashima HAl Hasan MXiong L(2022)Twin PapersProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557716(4444-4448)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557716
Li YZhao HChen Y(2022)Graphical Evolutionary Game Theoretic Modeling of Strategy Evolution Over Heterogeneous NetworksIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2022.32023088(739-754)Online publication date: 2022
https://doi.org/10.1109/TSIPN.2022.3202308
Yang CXiao YZhang YSun YHan J(2022)Heterogeneous Network Representation Learning: A Unified Framework With Survey and BenchmarkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.304592434:10(4854-4873)Online publication date: 1-Oct-2022
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Jung SSegev A(2022)Semantic Similarity Analysis between Future Topics and Their Neighbors in Topic Networks for Network-based Topic Evolution2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020287(5952-5961)Online publication date: 17-Dec-2022
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