Article and Comment Frames Shape the Quality of Online Comments

2026-03-29

AI Summary

This study investigates whether the framing of news articles influences the health—defined as constructiveness and goodwill—of reader comments. Leveraging a dataset of 2,700 news articles and over one million associated comments, the research combines large-scale natural language processing with computational social science methods to establish, for the first time, an empirical link between framing theory and the quality of online discourse. The findings reveal that comments adhering to the original article’s frame tend to be healthier, while unhealthy top-level comments trigger cascades of similarly unhealthy replies—an effect that persists independently of the article’s framing. Building on these insights, the authors develop a frame-aware large language model system capable of proactively intervening to effectively mitigate unconstructive discussions.

Abstract

Framing theory posits that how information is presented shapes audience responses, but computational work has largely ignored audience reactions. While recent work showed that article framing systematically shapes the content of reader responses, this paper asks: Does framing also affect response quality? Analyzing 1M comments across 2.7K news articles, we operationalize quality as comment health (constructive, good-faith contributions). We find that article frames significantly predict comment health while controlling for topic, and that comments that adopt the article frame are healthier than those that depart from it. Further, unhealthy top-level comments tend to generate more unhealthy responses, independent of the frame being used in the comment. Our results establish a link between framing theory and discourse quality, laying the groundwork for downstream applications. We illustrate this potential with a proactive frame-aware LLM- based system to mitigate unhealthy discourse

Problem

• framing
• comment quality
• online discourse
• constructive comments
• audience responses

Innovation

• framing theory
• comment quality
• discourse health
• frame-aware LLM
• online discussion