social media content moderators

Abstract: Generative AI is changing what can happen after a source artifact reaches its audience. A viewer's interpretation can now be externalized into a derivative artifact, allowing private sensemaking to become part of subsequent communication. Once such a derivative artifact circulates, it can enter subsequent viewers' information environments and shape the conditions under which their later sensemaking occurs. In this paper, we examine how this shift changes visual information communication. We first consider the viewer's immediate interaction with a source artifact and generative AI. We then examine what becomes consequential when the viewer's sensemaking takes communicative form, including communicative commitment, the legibility of transformations and source relationships, and the literacy required to interpret already-mediated information. Finally, we broaden the unit of analysis to consider how repeated and distributed AI mediation may accumulate over time, shaping what subsequent viewers notice, consider plausible, trust, and carry into subsequent sensemaking. We argue that understanding these longer-term forms of influence is a research direction for AI-mediated visual communication.

Abstract: Topic models are widely used to analyze public health-related social media short texts, yet their evaluation remains dominated by metrics that focus entirely on generated topics alone. There is a lack of metrics that quantitatively assess whether assigned topics meaningfully represent the corresponding short-text posts. We propose Document-Topic Alignment metrics (DoTA), an assignment-aware evaluation framework comprising metrics that measure semantic alignment between documents (posts) and their assigned topics. We also introduce margin-based and discriminative variants that capture topic assignment confidence and distinguishability. We evaluate DoTA across five topic models on three public health-related social media datasets from X and compare DoTA metrics with conventional topic-based metrics. Results show that DoTA provides complementary evaluation cues and aligns meaningfully with human evaluations. These findings establish the need for assignment-aware evaluation and demonstrate that the addition of DoTA enables a more comprehensive and practically meaningful evaluation for assessing short-text topic modeling performance.

Abstract: Vision Language Models have achieved strong performance on multimodal benchmarks, yet their ability to reason about culturally grounded and metaphor-rich content remains insufficiently studied. Internet memes present a challenging setting where meaning emerges from implicit interactions between image, overlaid text, sarcasm, and shared socio-cultural knowledge rather than literal visual recognition. This challenge is amplified in low-resource languages such as Bangla, where code-mixing, stylized scripts, and culturally specific symbolism introduce substantial distribution shift. In this work, we introduce BanglaMemeX, a culturally grounded multimodal benchmark comprising 3,000 Bangla memes annotated with multi-dimensional labels (humor, sarcasm, offensiveness, motivational intent, and overall sentiment) and human-written explanations that explicitly describe textual and visual metaphors. We systematically evaluate modern VLMs on both classification and explanation generation, revealing that current models struggle to interpret implicit cultural cues despite reasonable surface-level accuracy. Our results highlight the need for culturally-aware multimodal systems capable of grounded reasoning under linguistic and cultural distribution shift.