Bibliographic record
Abstract
Political memes have been previously studied in different contexts, but this study fills a gap in literature by employing a mixed method to provide insight into the discourses of fake news on Instagram. The author collected more than 550,000 Instagram posts sent by over 198,000 unique users from 24 February 2012 to 21 December 2018, using the hashtag #fakenews as a search term. The study uses topic modelling to identify the most recurrent topics that are dominant on the platform, while the most active users are identified to understand the nature of the online communities that discuss fake news. In addition, the study offers an analysis of visual metadata that accompanies Instagram images. The findings indicate that Instagram has become a weaponized toxic platform, and the largest community of active users are supporters of the US President Donald Trump and the Republican Party, mostly trolling liberal mainstream media especially CNN, while often aligning themselves with the far-right. On the other hand, a much smaller online community attempts to troll Trump and the Republicans. Theoretically, the study relies on political memes literature and argues that Instagram has become weaponized through an ongoing ‘Meme War,’ for many members in the two main online communities troll and attack each other to exert power on the platform.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".