Eylon Caplan

Nishanth Sridhar Nakshatri, Eylon Caplan, Rajkumar Pujari et al.

Health influencers play a growing role in shaping public beliefs, yet their content is often conveyed through conversational narratives and rhetorical strategies rather than explicit factual claims. As a result, claim-centric verification methods struggle to capture the pragmatic meaning of influencer discourse. In this paper, we propose TAIGR (Takeaway Argumentation Inference with Grounded References), a structured framework designed to analyze influencer discourse, which operates in three stages: (1) identifying the core influencer recommendation--takeaway; (2) constructing an argumentation graph that captures influencer justification for the takeaway; (3) performing factor graph-based probabilistic inference to validate the takeaway. We evaluate TAIGR on a content validation task over influencer video transcripts on health, showing that accurate validation requires modeling the discourse's pragmatic and argumentative structure rather than treating transcripts as flat collections of claims.

Tania Chakraborty, Eylon Caplan, Dan Goldwasser

Understanding human social behavior such as recognizing emotions and the social dynamics causing them is an important and challenging problem. While LLMs have made remarkable advances, they are limited to the textual domain and cannot account for the major role that non-verbal cues play in understanding social situations. Vision Language Models (VLMs) can potentially account for this gap, however their ability to make correct inferences over such social cues has received little attention. In this paper, we explore the capabilities of VLMs at social reasoning. We identify a previously overlooked limitation in VLMs: the Visual Social-Pragmatic Inference gap. To target this gap, we propose a new task for VLMs: Visual Social-Pragmatic Inference. We construct a high quality dataset to test the abilities of a VLM for this task and benchmark the performance of several VLMs on it.

Eylon Caplan, Dan Goldwasser

Finding evidence for human opinion and behavior at scale is a challenging task, often requiring an understanding of sophisticated thought patterns among vast online communities found on social media. For example, studying how gun ownership is related to the perception of Freedom, requires a retrieval system that can operate at scale over social media posts, while dealing with two key challenges: (1) identifying abstract concept instances, (2) which can be instantiated differently across different communities. To address these, we introduce ConceptCarve, an evidence retrieval framework that utilizes traditional retrievers and LLMs to dynamically characterize the search space during retrieval. Our experiments show that ConceptCarve surpasses traditional retrieval systems in finding evidence within a social media community. It also produces an interpretable representation of the evidence for that community, which we use to qualitatively analyze complex thought patterns that manifest differently across the communities.

Eylon Caplan, Tania Chakraborty, Dan Goldwasser

Variation in language use, shaped by speakers' sociocultural background and specific context of use, offers a rich lens into cultural perspectives, values, and opinions. However, the computational study of these Sociocultural Linguistic Phenomena (SLP) has often been limited to bespoke analyses of specific groups or topics, hindering the pace of scientific discovery. To address this, we introduce Splits!, a 9.7 million-post dataset from Reddit designed for systematic and flexible research. The dataset contains posts from over 53,000 users across 6 demographic groups, organized into 89 discussion topics to enable comparative analysis. We validate Splits! via self-identification and by successfully replicating several known SLPs from existing literature. We complement this dataset with a framework that leverages efficient retrieval methods to rapidly validate potential SLPs (PSLPs) by automatically evaluating whether a given hypothesis is supported by our data. Crucially, to distinguish between novel and obvious insights, the framework incorporates a human-validated measure of a hypothesis's ``unexpectedness.'' We demonstrate that the two-stage process reduces the number of statistically significant findings requiring manual inspection by a factor of 1.5-1.8x, streamlining the discovery of promising phenomena for further investigation.