Comments, Shares, or Likes: What Makes News Posts Engaging in Different Ways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a digital media environment where content distribution is shaped by technology companies’ algorithms and user behaviors, news organizations try to post content that can prompt user engagement in forms such as comments, shares, and likes or reactions. This study employs a content analysis of 1,600 messages and analyses of engagement metrics for 157,962 messages to examine to what extent and how Facebook messages of US and Israeli news organizations differ in the engagement modes they generate: commenting versus sharing versus liking/reacting. Drawing on the participation paradigm in audience research, news value theory, and literature on engagement enhancers, the study shows that certain content characteristics are associated with each of the examined engagement modes in more than one country while other content characteristics are associated with particular modes, but not with all of them. It offers a nuanced understanding of user interaction with news-related content and helps think about content units as more engaging or less engaging than others, or as engaging in different ways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it