Understanding government social media users’ role in disseminating misinformation: A comparative study of Canada and the United Kingdom
Bibliographic record
Abstract
The role social media plays in creating and disseminating misleading information, including fake images and visuals is well recognized by scholars and governments around the world. Many governments, including in Canada and the United Kingdom (UK), are working on, or have already introduced regulatory frameworks to combat disinformation and prevent harms for vulnerable populations. Among these vulnerable populations, migrant social media users form a distinct group of government social media users with specific information and interaction needs. As literature demonstrates, migrants are actively present on social media, including government social media and can benefit from information-sharing and connections it enables. However, social media can also deter migrants from pursuing migration opportunities. This paper examines the use of X (formerly known as Twitter) accounts by Immigration, Refugees and Citizenship Canada (IRCC) and the UK Home Office (Home Office) by government social media users, including migrant social media users. It specifically investigates if government social media users are spreading misinformation and the prevalence of misinformation on the IRCC and Home Office X accounts. As findings demonstrate, these two government accounts are used differently: IRCC X is used to advocate for causes and ask questions, while Home Office social media users are mostly voicing their opinions by critiquing politicians and government policies, and these critiques often contain misinformation. Finally, misleading tweets remain accessible for public consumption.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".