The impact of emotional expressions on the popularity of discussion threads: evidence from Reddit
Bibliographic record
Abstract
Purpose Users contribute to online communities by posting and responding to discussion threads. Nonetheless, only a small fraction of threads gain popularity and shape community discourse. Prior studies have identified several factors driving thread popularity; however, despite their prevalence, the role of emotional expressions within discussion threads remains understudied. This study addresses this gap by investigating the impact of thread starters’ valence and embedded discrete emotions of anger, anxiety and sadness on thread popularity, drawing on the negativity bias and the emotion-as-social-information theories. Design/methodology/approach Using two samples from Reddit, this study employs negative binomial regression analysis to examine the hypothesized relationships. Findings The results demonstrate that negativity in thread starters significantly influences thread popularity; however, the expression of discrete emotions impacts popularity variously. In some contexts, such as COVID-19 vaccination subreddits, embedded anger in thread starters decreases thread popularity, whereas anxiety and sad expressions enhance it. In other contexts, such as professional discussions (e.g. r/Medicine subreddit), anger and anxiety expressions increase thread popularity, while sad expressions have no significant influence. Research limitations/implications The study is limited by its focus on specific emotions and contexts. Future research could examine a broader range of emotions, post-content modalities and the impact of cultural and linguistic differences. Originality/value This study contributes to theory by offering a new definition of thread popularity and enhancing our understanding of the impact of emotions in online discussions. It also provides practical implications for online community members and moderators seeking to promote discussion posts that help achieve community goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".