"Algorithms ruin everything"
Top Cited Papers
- 2 May 2017
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 3163-3174
- https://doi.org/10.1145/3025453.3025659
Abstract
As algorithmically-driven content curation has become an increasingly common feature of social media platforms, user resistance to algorithmic change has become more frequent and visible. These incidents of user backlash point to larger issues such as inaccurate understandings of how algorithmic systems work as well as mismatches between designer and user intent. Using a content analysis of 102,827 tweets from #RIPTwitter, a recent hashtag-based backlash to rumors about introducing algorithmic curation to Twitter's timeline, this study addresses the nature of user resistance in the form of the complaints being expressed, folk theories of the algorithmic system espoused by users, and how these folk theories potentially frame user reactions. We find that resistance to algorithmic change largely revolves around expectation violation, with folk theories acting as frames for reactions such that more detailed folk theories are expressed through more specific reactions to algorithmic change.Keywords
Funding Information
- National Science Foundation (IIS-1217143/003)
This publication has 24 references indexed in Scilit:
- Bias in algorithmic filtering and personalizationEthics and Information Technology, 2013
- Concepts and Folk TheoriesAnnual Review of Anthropology, 2011
- The Feasibility of Folk ScienceCognitive Science, 2010
- Privacy as information access and illusory control: The case of the Facebook News Feed privacy outcryElectronic Commerce Research and Applications, 2009
- The role of task-technology fit as users’ motivation to continue information system useComputers in Human Behavior, 2009
- The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuanceInternational Journal of Human-Computer Studies, 2006
- A Technological Frames Perspective on Information Technology and Organizational ChangeThe Journal of Applied Behavioral Science, 2006
- Understanding Information Systems Continuance: An Expectation-Confirmation ModelMIS Quarterly, 2001
- Technological framesACM Transactions on Information Systems, 1994
- Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information TechnologyMIS Quarterly, 1989