Article and Comment Frames Shape the Quality of Online Comments

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

Figures (5)

• Figure 1: Health by frame alignment across platforms. A clear gradient emerges where comments matching article frames have the highest health rates, followed by selective reframing (adopting secondary frames present in the article), with complete reframing (introducing frames absent from the article) showing the lowest health. Error bars represent 95% confidence intervals.
• Figure 2: Comment health by frame alignment (Match, Diff in Article, Never in Article) across topics for NYT (top) and SOCC (bottom). The dashed line represents overall health.
• Figure 3: LLM prompt used for comment reformulation.
• Figure 4: Landing page with topic search functionality. Users can enter keywords or select predefined topics to retrieve 3 relevant articles from The Conversation. Article view with sentence-level frame analysis. Detected frames are displayed as tags; hovering over a frame highlights corresponding sentences in the article text.
• Figure 5: Moderation output for an unhealthy comment expressing climate skepticism. The system assigns a high risk level based on healthiness score, detects frames and retention, and recommends refusing the comment. The LLM generates three ways to reformulate more constructively while preserving the user's core concern and idea.