Abusive Metajournalistic Discourse Towards Journalists on Social Media
Bibliographic record
Abstract
In this study, we used a mixed method approach to investigate online abuse mostly targeting journalists, by focusing on the use of the term presstitute. We inductively analyzed the professional categories of the targeted people, whether be journalists, news organizations, politicians, etc., and the countries where these professionals live. Our findings show that Twitter was the most active platform for attacking journalists, and that the top targeted groups were Indian journalists followed by American and Filipino ones. Building on Metajournalistic Discourse theory, we introduce the concept of Abusive Metajournalistic Discourse (AMD) as a form of reactive discourse, and we argue that using the term presstitute is one manifestation of AMD. To corroborate our findings, we used the collected datasets to identify the journalists who are being trolled with different types of abusive content and interviewed 12 journalists through semi-structured interviews in order to elicit their views on the abusive content targeting them. Five themes emerged from these interviews including: (1) cloaked coordinated AMD; (2) hyper-nationalist, racist and sexist AMD; (3) state-sanctioned AMD and disinformation; (4) journalists’ resolve and resilience; and (5) sinister AMD outcomes, which help shed light on the impact of AMD on journalism practice.
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.
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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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 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".