Fear frames: mapping AI anxiety in Persian social media discourse
Public discourse about artificial intelligence is a critical site for examining how risk and affect are collectively organized through social media. This study investigates the thematic and affective configurations through which Persian-language users on X engage with AI-related concerns, contributing one of the first computational analyses of AI discourse in a Persian-language public sphere and, to our knowledge, the first to integrate LDA topic modeling, multi-label keyword operationalization,
Record details
Published: 8 August 2026
Source: AI & Society
Category: Research
Topics: Finance, VC & PE
Retrieved: 9 August 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
From token probabilities to calibrated confidence: An empirical study of mathematical question answering
arXiv cs.LG · 8 August 2026
Who Built This Model? Tracing LLM Lineage via Spectral Fingerprints in Weight Space
arXiv cs.LG · 7 August 2026
NeuPAT: Neuron-aware Plasticity Allocation Tuning for Language-Preserving MLLMs
HuggingFace Daily Papers · 7 August 2026
Diffusion LLMs as Targets and Adversaries: Mechanistic Safety Exploits
arXiv · 7 August 2026
People Are Not Just Their Countries. Disentangling Social Determinants of LLM Value Alignment Across Europe
arXiv · 7 August 2026
Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks
arXiv cs.AI · 7 August 2026
How to cite this record
ethics.ai (8 August 2026), “Fear frames: mapping AI anxiety in Persian social media discourse,” evidence record 17703, https://ethics.ai/record/17703 (originally published by AI & Society).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.