From the beat to the tweet: the use of Twitter (X) by police officers
Bibliographic record
Abstract
Purpose The police regularly incorporate social media into their outreach portfolios. Although much social media is handled at the organizational-level, some police officers also use social media in a professional capacity at the individual-level. Little research, however, has examined how individual police officers engage with social media. As part of the present research, we explored how individual police officers use Twitter (now named X): a popular microblogging platform. Design/methodology/approach We constructed a dataset of police officers who use Twitter in a professional capacity at the individual-level. We then extracted and coded tweets from the public accounts of such officers to analyze their characteristics. We also examined potential differences in Twitter use by officer gender, officer rank, and frequency of posting. Findings Our analyses revealed that individual police officers largely use Twitter to push content rather than pull information or network, mirroring organizational-level trends. Our analyses also revealed some differences in posting practices as a function of officer characteristics. Originality/value Most previous research regarding social media in policing has examined police social media practices at the organizational-level. For our research, we explored police social media practices at the individual-level – assessing the characteristics of police officers who use Twitter in a professional capacity as well as the characteristics of their tweets.
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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 | 0.001 | 0.014 |
| 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 | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".