Exploring Algorithmic Resistance: Responses to Social Media Censorship in Activism
Bibliographic record
Abstract
Social media platforms have become a double-edged sword for human rights activism, simultaneously offering a stage and facilitating wide-reaching communication and connection, while also imposing censorship through stringent and opaque content governance. This study focuses on the over enforcement of content moderation on social media platforms, affecting activists who tried to engage online publics with issues of forced evictions and displacements in Sheikh Jarrah (SJ) and Silwan, in occupied East Jerusalem in May 2021 --- a critical juncture in the Palestinian-Israeli conflict. By analyzing responses from a survey of 201 users who reported experiencing censorship, and interviews with 14 political influencers and activists, we uncover how these individuals navigate the intricate landscape of social media censorship. The findings reveal a continuum of different ways of responding to censorship, from self-censorship to proactive advocacy of policy change, that highlight the ingenuity activists can employ to bypass content restrictions. This research not only contributes to our understanding of the interaction between social media's technical affordances and activist responses but also discusses broader implications for the design and governance of digital platforms in supporting democratic discourse and human rights activism in conflict zones. This study enriches the ongoing dialogue about social media's dual role as both a facilitator and a controller of public discourse, emphasizing the need for platforms to consider the profound impacts of their technical and policy decisions on global activism.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".