A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media

Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing benchmarks often come from a single domain or focus on a limited set of topics. Additionally, manual annotation for accurate labeling is time-consuming and labor-intensive. To address these challenges, we propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation. Our approach produces a multidomain benchmark comprising 4,498 topical claims and 30,961 arguments from three sources, spanning 21 domains. We benchmark the dataset in fully supervised, zero-shot, and few-shot settings, shedding light on the strengths and limitations of different methodologies. We release the dataset and code in this study at hidden for anonymity.

Moral Judgments in Narratives on Reddit: Investigating Moral Sparks via Social Commonsense and Linguistic Signals

Given the increasing realism of social interactions online, social media offers an unprecedented avenue to evaluate real-life moral scenarios. We examine posts from Reddit, where authors and commenters share their moral judgments on who is blameworthy. We employ computational techniques to investigate factors influencing moral judgments, including (1) events activating social commonsense and (2) linguistic signals. To this end, we focus on excerpt-which we term moral sparks-from original posts that commenters include to indicate what motivates their moral judgments. By examining over 24,672 posts and 175,988 comments, we find that event-related negative personal traits (e.g., immature and rude) attract attention and stimulate blame, implying a dependent relationship between moral sparks and blameworthiness. Moreover, language that impacts commenters' cognitive processes to depict events and characters enhances the probability of an excerpt become a moral spark, while factual and concrete descriptions tend to inhibit this effect.