From Social Media with News: Journalists’ Social Media Use for Sourcing and Verification
Bibliographic record
Abstract
Social media is widely used by journalists for sourcing and verification. While social media may either serve as supplementary to existing sources or replace traditional channels, it nevertheless poses challenges to the news professionalism. The present study examines the relationship between journalists’ use of social media and other channels for news sourcing and verification. It also examines how attitudes towards social media affect the use of social media for sourcing and verification. An online survey of journalists (n = 255) in local news organizations in Hong Kong—a society with a high social media penetration rate and a highly competitive media market—revealed that journalists rely on offline, elite, and ready-made sources (such as information released by public relations companies or governmental officials). Social media both replaces and complements existing channels for sourcing and verification. The perception that social media is a credible source for information was positively related to using social media for news production. The present paper is a modest first study to examine how social media is included in news production in a non-Western context. It offers a better understanding of how emerging technologies change the information repertoire during news production in a post-truth era.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".