👥 Social Computing

💬 ACL2026 · 9 paper notes

Among Us: Language of Conspiracy Theorists on Mainstream Reddit

Analyzing 500 million Reddit comments over 10 years of longitudinal data, this study finds that users active in conspiracy theory communities exhibit detectable unique language patterns in mainstream communities (average 87% classification accuracy), but these patterns are highly context-dependent, with community-specific models outperforming global models by up to 17 percentage points.

Explain the Flag: Contextualizing Hate Speech Beyond Censorship

This paper proposes a hybrid approach combining LLMs with human-curated lexicons in three languages (English/French/Greek) to detect and explain hate speech—the term-based pipeline uses lexicon matching + LLM semantic disambiguation to detect inherently derogatory terms, the term-free pipeline uses LLMs to detect group-targeted content, and both are fused to generate evidence-based explanations.

How Language Models Conflate Logical Validity with Plausibility: A Representational Analysis of Content Effects

Through representational analysis, this work reveals that the concepts of "logical validity" and "plausibility" are highly aligned in LLM hidden layer spaces, causing models to conflate plausibility with validity (content effects). The paper constructs debiasing steering vectors that effectively decouple these two concepts, reducing content effects while improving reasoning accuracy.

Is this chart lying to me? Automating the detection of misleading visualizations

Proposes Misviz (2,604 real-world misleading visualizations) and Misviz-synth (57,665 synthetic visualizations) benchmarks covering 12 misleading types, systematically evaluating MLLMs, rule-based checkers, and image classifiers for misleading chart detection, revealing the task remains highly challenging.

On the Step Length Confounding in LLM Reasoning Data Selection

This paper discovers that naturalness-based LLM reasoning data selection methods suffer from "step length confounding"—systematically preferring samples with longer per-step tokens rather than higher-quality ones. The root cause is that the low probability of reasoning steps' first tokens gets diluted by long steps. Two correction methods are proposed: Aslec-drop (dropping first-token probabilities) and Aslec-casl (causal regression debiasing), improving average accuracy by 6–9%.

Persona-E2: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events

Constructs the first large-scale dataset Persona-E2 linking personality traits (MBTI + Big Five) with reader emotional responses, containing 3,111 events × 36 annotators totaling 112K annotations, revealing that LLMs suffer from "personality illusion" when simulating personality-shaped emotional responses, and that Big Five features mitigate this more effectively than MBTI.

SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models

This paper proposes SPAGBias, a framework that systematically evaluates gender bias in LLMs within urban micro-spatial contexts through three diagnostic layers—explicit, probabilistic, and constructive bias—revealing structured spatial-gender association patterns and tracing how bias is embedded and amplified throughout the model development pipeline.

ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection

ToxiTrace proposes an explainable Chinese toxicity detection method for BERT-class encoders, combining CuSA (LLM-guided weak annotation), GCLoss (gradient-constrained loss), and ARCL (adversarial reasoning contrastive learning) to achieve both high sentence-level classification accuracy and contiguous toxic span extraction while maintaining efficient encoder inference.

ToxReason: A Benchmark for Mechanistic Chemical Toxicity Reasoning via Adverse Outcome Pathway

ToxReason proposes an AOP-based chemical toxicity mechanistic reasoning benchmark that integrates drug-target experimental data with toxicity labels, requiring models to reason from molecular initiating events to organ-level adverse outcomes; a 4B model trained with GRPO reinforcement learning surpasses GPT-5 and other large models in both toxicity prediction (F1 71.4%) and reasoning quality.