"Algorithms ruin everything": #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media

By Michael Ann DeVito, Darren Gergle. International Conference on Human Factors in Computing Systems, 2017

308 citations recorded by the source corpus.

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.

Subfields: Computer Science Applications, Safety Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/991103a6da2517c0108b221ae986194d68e95b67

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In Computer Science Applications

In Safety Research

In Sociology and Political Science

Citations in the corpus

Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “This could represent a feeling of violated expectations, which tends to decrease satisfaction and could result in users perceiving that their goals are not being met and that the system is unfair [11, 17].”
The Illusion of Control: Placebo Effects of Control Settings (2018, background): “Many researchers have explored sensemaking around social media and news feeds [7, 12, 18, 20, 48].”
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018, background): “But in the absence of transparency from platform providers, end-users may develop folk theories of algorithms, leading to a diverse range of strategies and countermeasures with varying degrees of effectiveness [22, 14].”
Addressing Age-Related Bias in Sentiment Analysis (2018, background): “Researchers have examined how people may attempt to reverse engineer systems based on their understanding of algorithms [5], how altering the design of system features affects an individual’s understanding of algorithms [21,24], and how understanding algorithms affects user behavior [25].”
Managing visibility on YouTube through algorithmic gossip (2019, background): “They highlight how functional theories on algorithmic processes are built despite users’ ostensive lack of technical knowledge (e.g. Bucher, 2017; DeVito et al., 2017; Eslami et al., 2016; Myers West, 2018).”
Algorithmic Folk Theories and Identity: How TikTok Users Co-Produce Knowledge of Identity and Engage in Algorithmic Resistance (2021, background): “The development of algorithmic folk theories, unocial theories a user holds to explain how a technological system operates and generates various outputs [27], is a powerful way for users of online platforms to make sense of what they see and experience on these platforms [26, 30, 32].”