Emerging practices for managing user misconduct in online news media comments sections
Bibliographic record
Abstract
Purpose The purpose of this paper is to bridge a gap in knowledge on the professional information practices of a group of people whose daily work of managing user-generated content online exposes them to users whom they perceive as acting aggressively or otherwise offensively online. Design/methodology/approach Journalists’ narratives of practices for managing and responding to user comments perceived as offensive are analysed qualitatively. For this purpose, ten interviews with journalists from nine different news organisations in Sweden, Denmark, Germany and Canada were conducted. Findings The study finds that the environment in which the journalists work plays a vital role in the evolution of the practices. Practices, indissolubly tied to the contexts or sites in which people’s activities take place, are conditioned by moral values, traditions and collective experiences which journalists enact through the practice they engage in when they are dealing with user posts online. The site, conceived as an information landscape, is that of the newsroom. Practices for managing users online evolve through actors participating in a process of learning and their ability to adopt the cultural norms and values of their environment. Originality/value This study sheds light on the mechanisms behind the evolution of practices for handling user-generated content online and it reports on the importance of properties such as norms, values and emotions for how things are done in the information landscape of news journalism.
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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 | 0.029 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".