Combating Islamophobia: Compromise, Community, and Harmony in Mitigating Harmful Online Content
Bibliographic record
Abstract
Despite significant advances in content moderation within HCI, social computing scholarship in this area remains constrained by secular values and Western interpretations of justice. As a result, current literature often overlooks religious and spiritual sensibilities, as well as communal peacebuilding efforts even when the harms originate from and strongly connected to faith sensitivities, such as Islamophobia. This article presents findings from a design and evaluation study on the reporting and moderation of Islamophobic posts on Twitter (currently known as “X”). By utilizing HCI theories and readily available NLP techniques, we developed an online tool for reporting and moderating Islamophobic tweets. We subsequently conducted usability studies, contextual inquiries, and interviews with 32 participants to assess the tool’s effectiveness in addressing Islamophobic content. Our study revealed that factors such as faith-related knowledge practices, fact-checking, communal leadership, social harmony, and the cultural-religious value of “compromise” significantly influence reactions to Islamophobic posts online. Expanding on these findings and drawing from the literature on conflict resolution in theology, legal studies, and justification, we explore how “Sulha,” a community-driven process for mitigating conflict and restoring communal peace, can cater to faith-based sensibilities in reporting and moderating Islamophobic content. Therefore, this article complements existing content moderation literature by recommending the adaptation of faith sensitivities in the design of computing tools and policies to mitigate Islamophobia and similar faith-related online harms.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".