Disinformation and online harms: Understanding the links to private messaging apps in Canada
Bibliographic record
Abstract
Amid the COVID-19 pandemic, governments have been facing an increased spread of disinformation on social media by foreign and domestic actors. Indeed, the COVID-19 pandemic has highlighted the online challenges of disinformation facing Western governments and societies, including Canada. However, much of the scholarly work on disinformation has focussed on analyzing the flows of false content during political or electoral processes including how political disinformation can undermine voter autonomy by changing opinions and eventually voting preferences.12Furthermore, the focus of such works is mostly centred around the role of open social media platforms (e.g., Facebook, Instagram, Twitter, etc.). This presentation on the other hand seeks to counter this dominant trend. Based on findings from original research, supported by the Democratic Institutions Secretariat in Canada’s Privy Council Office, this presentation will delve deeper into our survey of 2,500 Canadians in March 2021, revealing their experiences with disinformation and other online harms on private messaging apps (e.g., WhatsApp, Facebook Messenger, WeChat, etc.) in a post-pandemic context. Indeed, private messaging apps have been described as the next refuge for actors such as members of the far-right and white nationalists, as social media platforms like Facebook face increased political pressure to remove harmful content.3This presentation will shed light on the types and impacts of mis/disinformation encountered through the private platforms. Nearly half of respondents reported receiving false information at least monthly and those who believe in COVID-19 conspiracy theories were much more likely to regularly receive news through private messages. This presentation will provide a deeper and broader understanding on the spread and evolution of disinformation in Canada, and importantly, it will discuss potential regulatory measures and the need to balance policy with democratic rights and freedoms including privacy and free expression.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".