The public’s appropriation of multimodal discourses of fake news on social media
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
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.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it