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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2506.05373 |
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| _version_ | 1866916781273120768 |
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| author | Khadka, Arjan |
| author_facet | Khadka, Arjan |
| contents | Social media platforms are ecosystems in which many decisions are constantly made for the benefit of the creators in order to maximize engagement, which leads to a maximization of income. The decisions, ranging from collaboration to public conflict or ``beefing,'' are heavily influenced by social media algorithms, viewer preferences, and sponsor risk. This paper models this interaction as a Stackelberg game in which the algorithm is the leader, setting exposure and reward rules, and the content creators are the followers, who optimize their content to maximize engagement. It focuses on two influencer strategies of collaborating and beefing. Viewer preferences are modeled indirectly through the algorithm's utility function, which rewards engagement metrics like click-through rate and watch time. Our simplified game-theoretic model demonstrates how different algorithmic priorities can shift creator strategies and provides insight into the equilibrium dynamics of social media influence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05373 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Game Theory in Social Media: A Stackelberg Model of Collaboration, Conflict, and Algorithmic Incentives Khadka, Arjan Computer Science and Game Theory Social media platforms are ecosystems in which many decisions are constantly made for the benefit of the creators in order to maximize engagement, which leads to a maximization of income. The decisions, ranging from collaboration to public conflict or ``beefing,'' are heavily influenced by social media algorithms, viewer preferences, and sponsor risk. This paper models this interaction as a Stackelberg game in which the algorithm is the leader, setting exposure and reward rules, and the content creators are the followers, who optimize their content to maximize engagement. It focuses on two influencer strategies of collaborating and beefing. Viewer preferences are modeled indirectly through the algorithm's utility function, which rewards engagement metrics like click-through rate and watch time. Our simplified game-theoretic model demonstrates how different algorithmic priorities can shift creator strategies and provides insight into the equilibrium dynamics of social media influence. |
| title | Game Theory in Social Media: A Stackelberg Model of Collaboration, Conflict, and Algorithmic Incentives |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2506.05373 |