Scholars’ temporal participation on, temporary disengagement from, and return to Twitter
Bibliographic record
Abstract
Even though the extant literature investigates how and why academics use social media, much less is known about academics’ temporal patterns of social media use. This mixed methods study provides a first-of-its-kind investigation into temporal social media use. In particular, we study how academics’ use of Twitter varies over time and examine the reasons why academics temporarily disengage and return to the social media platform. We employ data mining methods to identify a sample of academics on Twitter (n = 3,996) and retrieve the tweets they posted (n = 9,025,127). We analyze quantitative data using descriptive and inferential statistics, and qualitative data using the constant comparative approach. Results show that Twitter use is predominantly connected to traditional work hours and is well-integrated into academics’ professional endeavors, suggesting that professional use of Twitter has become “ordinary.” Though scholars rarely announce their departure from or return to Twitter, approximately half of this study’s participants took some kind of a break from Twitter. Although users returned to Twitter for both professional and personal reasons, conferences and workshops were found to be significant events stimulating the return of academic users.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".