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Bots have become critical for managing online communities on platforms, especially to match the increasing technical sophistication of online harms. However, community leaders often adoptthird-party bots, creating room for misalignment in their assumptions, expectations, and understandings (i.e., their technological frames) about them. On platforms where sharing bots can be extremely valuable, how community leaders can revise their frames about bots to more effectively adopt them is unclear. In this work, we conducted a qualitative interview study with 16 community leaders on Discord examining how they adopt third-party bots. We found that participants addressed challenges stemming from uncertainties about a bot's security, reliability, and fit through emergent social ecosystems. Formal and informal opportunities to discuss bots with others across communities enabled participants to revise their technological frames over time, closing gaps in bot-specific skills and knowledge. This social process of learning shifted participants' perspectives of the labor of bot adoption into something that was satisfying and fun, underscoring the value of collaborative and communal approaches to adopting bots. Finally, by shaping participants' mental models of the nature, value, and use of bots, social ecosystems also raise some practical tensions in how they support user creativity and customization in third-party bot use. Together, the social nature of adopting third-party bots in our interviews offers insight into how we can better support the sharing of valuable user-facing tools across online communities.
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The Case for Establishing a Collective Perspective to Address the Harms of Platform Personalization
Personalization on digital platforms drives a broad range of harms, including misinformation, manipulation, social polarization, subversion of autonomy, and discrimination. In recent years, policy makers, civil society advocates, and researchers have proposed a wide range of interventions to address these challenges. This Article argues that the emerging toolkit reflects an individualistic view of both personal data and data-driven harms that will likely be inadequate to address growing harms in the global data ecosystem. It maintains that interventions must be grounded in an understanding of the fundamentally collective nature of data, wherein platforms leverage complex patterns of behaviors and characteristics observed across a large population to draw inferences and make predictions about individuals. Using the lens of the collective nature of data, this Article evaluates various approaches to addressing personalization-driven harms under current consideration. It also frames concrete guidance for future legislation in this space and for meaningful transparency that goes far beyond current transparency proposals. It offers a roadmap for what meaningful transparency must constitute: a collective perspective providing a third party with ongoing insight into the information gathered and observed about individuals and how it correlates with any personalized content they receive across a large, representative population. These insights would enable the third party to understand, identify, quantify, and address cases of personalization-driven harms. This Article discusses how such transparency can be achieved without sacrificing privacy and provides guidelines for legislation to support the development of such transparency.
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- Award ID(s):
- 2217680
- PAR ID:
- 10466982
- Publisher / Repository:
- Vanderbilt Journal of Entertainment and Technology Law, Social Science Research Network
- Date Published:
- Journal Name:
- Vanderbilt Journal of Entertainment and Technology Law
- ISSN:
- 1942-6771
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
