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HotComment: A Benchmark for Evaluating Popularity o... | AI Research

Key Takeaways

Online comments are a powerful force in shaping public opinion, yet predicting which comments will become popular is difficult.
Online comments play a crucial role in shaping public sentiment and opinion dynamics on social media.
Furthermore, we propose StyleCmt, inspired by social ripple effects, where multiple stylistic dimensions align to amplify socially resonant expressions and suppress incongruent ones.
Popularity is not just about the text itself; it depends on linguistic quality, emotional resonance, and how well a comment fits the specific culture of a platform or community.
The researchers developed a framework to quantify popularity through three primary lenses.

Paper AbstractExpand

Online comments play a crucial role in shaping public sentiment and opinion dynamics on social media. However, evaluating their popularity remains challenging, not only because it depends on linguistic quality, originality, and emotional resonance, but also because stylistic preferences vary widely across platforms and user groups, causing the same comment to resonate differently in different communities. In this work, we present HotComment, a multimodal benchmark integrating video and text modalities that comprehensively quantifies popularity from three enhanced aspects: (1) Content Quality, which evaluates semantic similarity with ground-truth human comments and extends quality assessment through four interpretable dimensions; (2) Popularity Prediction, based on trends from models trained on real-world interaction data; and (3) User Behavior Simulation, which models the distribution of platform users and approximates \textbf{engagement scores} through an agent-based framework. Furthermore, we propose StyleCmt, inspired by social ripple effects, where multiple stylistic dimensions align to amplify socially resonant expressions and suppress incongruent ones.

Online comments are a powerful force in shaping public opinion, yet predicting which comments will become popular is difficult. Popularity is not just about the text itself; it depends on linguistic quality, emotional resonance, and how well a comment fits the specific culture of a platform or community. To address this, the paper "HotComment: A Benchmark for Evaluating Popularity of Online Comments" introduces a new multimodal benchmark designed to measure and predict the success of online comments across both text and video content.

Measuring Comment Quality

The researchers developed a framework to quantify popularity through three primary lenses. First, they evaluate "Content Quality" by comparing comments against ground-truth human examples. This process goes beyond simple text matching by using four distinct, interpretable dimensions to assess the quality of the content. By breaking down quality into these specific categories, the benchmark provides a more nuanced view of what makes a comment effective.

Predicting Popularity and User Behavior

Beyond static quality, the benchmark incorporates dynamic elements to simulate real-world social media environments. The researchers utilize models trained on actual interaction data to predict popularity trends. Furthermore, they employ an agent-based framework to simulate how different types of platform users interact with content. This allows the system to approximate "engagement scores," providing a more realistic prediction of how a comment might perform within a diverse user base.

The Role of Style

A key innovation in this work is the introduction of "StyleCmt." Inspired by the concept of social ripple effects, this approach recognizes that stylistic choices play a major role in whether a comment resonates with an audience. StyleCmt works by aligning multiple stylistic dimensions to amplify expressions that are socially resonant while simultaneously suppressing those that are incongruent with the community. This mechanism helps explain why the same comment might succeed in one digital space but fail in another.

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