Misframing Marine Plastic Pollution on TikTok
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
TikTok has emerged as a significant platform for environmental communication, particularly in ocean protection and waste cleanup. This paper analyzes 250 English-language videos tagged with #plasticpollution and #marineplasticpollution. The videos were retrieved in 2023 by searching hashtags and downloading available videos chronologically from the “Top 100” section. Our analysis includes a descriptive statistical analysis of content framing (cause, issue, solution) derived from marine plastic pollution literature and a 10% video sample, as well as stylistic framing (deficit/dialogue, fearful/hopeful) delineated from established environmental communication models. Our findings suggest a significant disjuncture between experts’ perceptions of marine plastic pollution, obtained through a literature review on the topic, and how the issue is presented on TikTok. Specifically, TikTok individualizes the causes and solutions to the challenge, tends to foreground technological answers, and primarily frames the nature of the issue as solely ecological. This presents a one-sided perspective on this systemic problem and neglects the socio-political injustices tied to plastic pollution. Stylistically, most videos use a data-centered deficit model and a fearful emotional genre, assuming the public needs information due to a knowledge gap while evoking apprehension to drive action. While these models could raise awareness of the issue, they differ from the preferred dialogue and optimistic communication models, which have been linked to greater public engagement based on previous research in the field. Generally, this research finds that the framing of marine plastic pollution in English-language TikTok videos perpetuates one-sided narratives, suggesting flaws in how demographics consuming these videos obtain information about the challenge.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it