The Influence of Text Variation on User Engagement in Cross-Platform Content Sharing

TL;DR

This work investigates how textual variations in cross-platform, multimodal content influence engagement, focusing on Reddit posts that share YouTube videos. It combines a large-scale observational dataset with a multi-phase controlled experiment to isolate title effects from video popularity, timing, and community norms, revealing that longer, lexically rich, emotionally resonant titles drive higher engagement. A fine-tuned BERT model achieves about 74% accuracy in pairwise title ranking, outperforming GPT-4o and simple baselines, demonstrating that textual features contain predictive signals beyond video popularity. The study provides a replicable framework for cross-platform content optimization and offers practical guidance for crafting titles that align with community norms and maximize engagement, while highlighting avenues for extending the approach to other platform pairs and incorporating richer multimodal signals.

Abstract

In today's cross-platform social media landscape, understanding factors that drive engagement for multimodal content, especially text paired with visuals, remains complex. This study investigates how rewriting Reddit post titles adapted from YouTube video titles affects user engagement. First, we build and analyze a large dataset of Reddit posts sharing YouTube videos, revealing that 21% of post titles are minimally modified. Statistical analysis demonstrates that title rewrites measurably improve engagement. Second, we design a controlled, multi-phase experiment to rigorously isolate the effects of textual variations by neutralizing confounding factors like video popularity, timing, and community norms. Comprehensive statistical tests reveal that effective title rewrites tend to feature emotional resonance, lexical richness, and alignment with community-specific norms. Lastly, pairwise ranking prediction experiments using a fine-tuned BERT classifier achieves 74% accuracy, significantly outperforming near-random baselines, including GPT-4o. These results validate that our controlled dataset effectively minimizes confounding effects, allowing advanced models to both learn and demonstrate the impact of textual features on engagement. By bridging quantitative rigor with qualitative insights, this study uncovers engagement dynamics and offers a robust framework for future cross-platform, multimodal content strategies.

Figures (9)

• Figure 1: Example posts from r/videos, Reddit’s largest video-sharing subreddit. The ranking and visibility of posts are influenced by user votes, titles, video content, timing, and interaction dynamics.
• Figure 2: Word clouds illustrate the distinct thematic focuses of video posts in three large subreddits: r/kpop, r/SquaredCircle, and r/WayOfTheBern, highlighting their unique community norms.
• Figure 3: Temporal variation in mean post scores by (A) hour of the day, (B) day of the week, and (C) month (smoothed over three-month intervals for three subreddits during 2020-2021)
• Figure 4: Video Statistics: (A) Frequency distribution of YouTube video categories; (B) Log-scaled distribution of video views; (C) Positive correlation between video views (log scale) and post scores, with mean scores peaking at around $10^7$ views.
• Figure 5: Title modifications and engagement: (A) Degree of rewriting in post titles compared to their corresponding video titles, measured using Levenshtein Distance (LD, A1) and SBERT cosine similarity (A2); (B) Mean post scores across LD groups, showing that rewritten titles lead to higher engagement than copied titles. (C, D) Statistical significance (log-scale p-values) for fixed-width LD bins (C) and quantile-based LD bins (D), comparing each group to minimally modified titles (final bins, highlighted in yellow).