Accelerated Epistemic Harm: Understanding the Role of Social Media Engagement Algorithms in Online Radicalization
Bibliographic record
Abstract
Social media use appears to play a role in radicalizing an increasing number of people.I problematize what I characterize as one popular picture of radicalization, which demands that an agent be socially isolated, have mental health issues, a propensity to violence, and becomes radicalized on a timeline.I argue that what is particularly concerning about online radicalization, versus offline radicalization, is the accelerated epistemic harm that prolonged social media use and exposure to its engagement algorithms can cause to the epistemic capacities of the agent, drawing from Fricker's concept of epistemic injustice.I then argue that social media companies share some responsibility for online radicalization, having fostered an environment that can cause epistemic harm.I conclude by sketching ways we might combat this environment at the level of the agent and community, problematizing the virtue ethics-based approach that is common in such recommendations and instead favouring a legislative approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".