The public’s appropriation of multimodal discourses of fake news on social media
Bibliographic record
Abstract
This study empirically examines tweets and Instagram posts that reference the hashtag #fakenews in connection to Canadian issues to understand the nature of the public’s political and multimodal discourses. Taken from larger datasets consisting of over 255,000 Instagram posts and over 14 million tweets, we used a mixed method, partly analyzing more than 4100 most retweeted messages and Instagram posts and manually categorizing them into seven topic types along with their political tone. Theoretically, we argue that the term fake news has lost its core meaning as it is appropriated by the social media public to communicate a variety of messages especially in relation to politics. The findings show that although there are differences between the two social media platforms, the majority of Instagram and Twitter topics that reference fake news are political in nature and anti-liberal in tone. Methodologically, the inclusion of multimodal analysis helps identify the sentiment and emotional aspects which are critical aspects for the spread of fake news and polarization on social media. Despite the different political contexts, our findings on Instagram and Twitter align with other studies that examined political polarization and the prevalence of conservative voices in the United States.
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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".