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Record W3082309897 · doi:10.31219/osf.io/3sutz

Lessons in resilience: Canada's Digital Media Ecosystem and the 2019 Election

2024· preprint· en· W3082309897 on OpenAlexfundaboutno aff
Taylor Owen, Aengus Bridgman, Robert Gorwa, Peter John Loewen, Eric Merkley, Derek Ruths, Oleg Zhilin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
FundersUniversity of TorontoMcGill UniversityRossy FoundationMozilla FoundationUniversity of Ottawa
KeywordsDisinformationReferendumPolitical scienceSocial mediaBrexitParliamentGeneral electionDemocracyPublic relationsDigital mediaEuropean unionPoliticsPublic administrationInternet privacyBusinessComputer scienceLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.151
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0230.013
Scholarly communication0.0190.007
Open science0.0020.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.014
GPT teacher head0.299
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2024
Admission routes2
Has abstractyes

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