Anindya Mondal

Anindya Mondal, Sauradip Nag, Anjan Dutta

ABACUS is a unified vision-language model that handles object counting, crowd counting, referring-expression counting, and count-faithful image generation without any benchmark-specific training required. Our model is built on existing 3B-parameter unified foundation model and is adapted for object localization tasks using three key innovations: density-aware adaptive zooming with objectness maps for spatial grounding; a boundary-aware count policy via GRPO to eliminate crop-boundary errors; and a cycle-consistent GRPO strategy where the understanding branch self-critiques generated outputs, closing the understanding-generation gap without any external annotations. ABACUS achieves state-of-the-art results across seven benchmarks, outperforming both task-specific specialists and larger generalist models.

Danishjeet Singh, Anindya Mondal, Filippo Menczer

Political discourse on social media has grown increasingly toxic, with electoral periods amplifying partisan hostility and cross-group attacks. Yet it remains unclear whether toxicity in online political speech reflects how partisans communicate within their own circles, or how aggressively they engage with the opposition. Disentangling these dynamics is critical for understanding online political hostility and for designing effective content moderation. We examine this question at scale using a large collection of original posts and replies from X (formerly Twitter), collected during the 2024 U.S. presidential election. Using a human-validated large language model to classify the political alignment of posts and users, and the Perspective API for toxicity scoring, we uncover a striking asymmetry: Republican-leaning posts are significantly more toxic than Democratic-leaning posts, yet Democratic-leaning posts attract significantly more toxic replies. To interpret this finding, we compare the toxicity of same-party and cross-partisan replies. While cross-partisan replies are slightly but significantly more toxic than same-party replies, this is true for both Democratic and Republican posts. However, Republican users account for a large majority of replies to Democratic posts, while Democrats account for a minority of replies to Republican content. Therefore, the elevated toxicity directed at Democratic content is better explained by the volume of Republican cross-partisan replies.

Henrique Ferraz de Arruda, Andreia Sofia Teixeira, Pranay Gundala Reddy et al.

Despite raising concerns about the mental health effects associated with the usage of TikTok, little is known about how related content is framed by creators and received by audiences. We collect the content of 28,341 TikTok videos and 80,130 comments from Mental Health Awareness Month (May) in 2023 and 2024 via the TikTok Research API, and study how the tone of awareness varies across topics and years. We characterize "tone" as the emotional and interpersonal framing of mental health discourse, operationalized through sentiment and toxicity measures. We extract topics from video text using BERTopic and log-odds keywords, then quantify topic-conditioned sentiment (XLM-T) and toxicity (Detoxify) separately for video transcriptions and comments. Sentiment captures the affective valence of content, while toxicity reflects the presence of harmful or abusive language. We find a stable set of recurring themes across years, spanning clinical conditions, emotional disclosure, self-care, and campaign-oriented content, with engagement highly skewed toward a small subset of topics. All sentiment and toxicity analyses are computed separately for video content and comments, allowing us to distinguish between content production and audience reception. Sentiment in videos is often negative for emotionally charged topics, while comments tend to shift toward more mixed or positive polarity, especially for suicide prevention. Toxicity is low in median overall, but exhibits longer-tailed outliers in comments than in videos that are more pronounced in comments and concentrated in specific topics (e.g., "Duet", "Suicide Prevention", and "Psychisch"). Overall, our results provide a topic-level decomposition of mental health discourse on TikTok during awareness-month campaigns.