Lessons in resilience: Canada's Digital Media Ecosystem and the 2019 Election
Bibliographic record
Abstract
Researchers, policy-makers and the public at large are paying more attention to the threats that disinformation and other forms of online media manipulation pose to democratic institutions and political life. Starting with the Brexit referendum and United States election in 2016, and building through the European Parliament elections in 2019, concerns about co-ordinated disinformation campaigns organized by state or other actors, automated social media accounts (“bots”), malicious and deceptive advertising, and polarization enhanced by algorithmic “filter bubbles” have reached an all-time high.The Digital Democracy Project (DDP) was set up to help build the international evidence base on the impact of these trends with a robust Canadian case study whose methods could be applied in other locations. The project consists of three phases. The first was a two-day workshop for journalists about disinformation threats, held in May 2019 in Toronto. The second, and the focus of this report, was researching and monitoring the digital media ecosystem in real time ahead of the Canadian federal election on Oct. 21, 2019. The third and final phase, beginning in early 2020, will involve further research and consultations with experts and public representatives to develop policy recommendations.We launched this phase of the project in August 2019 and continued collecting data until the end of November. This work builds on the growing field of study of election integrity and, in particular, on the study of the spread and influence of media exposure (both online and offline) and of disinformation and toxic content on the behaviour of voters. Using a novel approach that combined online data analysis with a battery of representative national surveys, we sought to contextualize and better understand developing patterns of online activity with measures of media consumption, trust and partisanship.Overall, our findings suggest the Canadian political information ecosystem is likely more resilient than that of other countries, in particular the U.S., due to a populace with relatively high trust in the traditional news media, relatively homogenous media preferences with only a marginal role for hyperpartisan news, high levels of political interest and knowledge, and — despite online fragmentation — fairly low levels of ideological polarization overall. While we do find affective polarization, which involves how individuals feel about other parties and their supporters, we find less polarization on issues, which has been a key point of vulnerability in other international elections.Despite some worries about automated activity being used to game trending hashtags on Twitter or the presence of a few disreputable online outlets, our research suggests their impact was limited. While there remain significant blind spots in the online ecosystem caused by limited data access for researchers, based on the communication we could see, we did not find evidence of any impact attributable to co-ordinated disinformation campaigns.Looking forward, however, we find evidence to suggest potential future vulnerabilities, most of which are related to growing partisanship and polarization, as well as the segmentation of the populace into online information environments that reinforce existing world views.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".