The Role of Media Watermarks in Shaping Credibility Judgments: A Qualitative Study of Premier League Transfer Rumors among Chinese Gen Z Football Fans
Bibliographic record
Abstract
In todays increasingly visual social media environment, users make rapid judgments about information credibility based on visual cues. Among Chinese Gen Z football fans, Premier League transfer rumors frequently circulate on platforms such as Weibo, Rednote, and WeChat, blending verified and unverified information. This study investigates how digital media watermarks influence the credibility judgments of Chinese Gen Z fans (born 19952010) when encountering transfer rumors online. Using qualitative research through semi-structured interviews with 20 frequent users of football-related content, the study analyzes how watermark characteristics (e.g., visibility, origin, platform context) affect trust and skepticism. The findings reveal that while some users still associate watermarks with copyright or authenticity, many no longer see them as reliable credibility signals due to widespread watermark manipulation and platform-specific misunderstandings. Notably, trust increasingly shifts toward first-hand foreign platforms (e.g., Twitter, Instagram), while Chinese platforms face greater skepticism. This research contributes to the understanding of digital visual literacy, challenges conventional assumptions about watermark credibility, and offers insights into sports media consumption and misinformation in cross-cultural online environments.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".