Implications of Revenue Models and Technology for Content Moderation Strategies
This paper develops a theoretical model to study the economic incentives for a social media platform to moderate user-generated content. We show that a self-interested platform can use content moderation as an effective marketing tool to expand its installed user base, to increase the utility of its users, and to achieve its positioning as a moderate or extreme content platform. For the purpose of maximizing its own profit, a platform will balance pruning some extreme content, thus losing some users, with gaining new users because of a more moderate content on the platform. This balancing act will play out differently depending on whether users will have to pay to join (subscription vs advertising revenue models) and on whether the technology for content moderation is perfect.We show that when conducting content moderation optimally, a platform under advertising is more likely to moderate its content than one under subscription, but does it less aggressively compared to the latter when it does. This is because a platform under advertising is more concerned about expanding its user base, while a platform under subscription is also concerned with users’ willingness-to-pay. We also show a platform’s optimal content moderation strategy depends on its technical sophistication. Because of imperfect technology, a platform may optimally throw away the moderate content more than the extreme content. Therefore, one cannot judge how extreme a platform is by just looking at its content moderation strategy. Furthermore, we show that a platform under advertising does not necessarily benefit from a better technology for content moderation, but one under subscription does, as the latter can always internalize the benefits of a better technology. This means that platforms under different revenue models can have different incentives to improve their content moderation technology. Finally, we draw managerial and policy implications from our insights
Year of publication: |
[2021]
|
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Authors: | Liu, Yi ; Yildirim, Pinar ; Zhang, Z. John |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Extent: | 1 Online-Ressource (62 p) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 23, 2021 erstellt |
Other identifiers: | 10.2139/ssrn.3969938 [DOI] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10013312697
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