Trust and Friction: Negotiating How Information Flows through Decentralized Social Media
Bibliographic record
Abstract
Decentralized social media protocols enable users in independent, user-hosted servers (i.e., instances) to interact with each other while they self-govern. This community-based model of social media governance opens up new opportunities for tailored decision-making about information flows - i.e., what user data is shared to whom and when - and in turn, for protecting user privacy. To better understand how community governance shapes privacy expectations on decentralized social media, we conducted a semi-structured interview with 23 users of the Fediverse, a decentralized social media network. Our findings illustrate important factors that shape a community's understandings of information flows, such as rules and proactive efforts from admins who are perceived as trustworthy. We highlight ''governance frictions'' between communities that raise new privacy risks due to incompatibilities in values, security practices, and software. Our findings point to the unique challenges of decentralized social media, suggest design opportunities to address frictions, and outline the role of participatory decision-making to realize the full potential of decentralization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".