MétaCan
Menu
Back to cohort
Record W7161972174 · doi:10.82308/33644

Resilient? Perceptions, Spread, and Impacts of Misinformation in the New Political Information Environment

2024· dissertation· en· W7161972174 on OpenAlexaboutno aff
Mathieu Lavigne

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMisinformationPoliticsPerceptionSocial mediaIdeologyPolitical communication

Abstract

fetched live from OpenAlex

Over the past years, Western democracies’ media and political environments have experienced important changes. The proliferation of content producers, algorithmically-driven information distribution, declining trust in the media and governments, the rise of right-wing populism, and political polarization are all conducive to a political information environment in which true and false information coexists and citizens are increasingly divided into “truth publics,” with different realities, facts, authorities, and narratives. This dissertation focuses on how the current political information environment can influence citizens’ perceptions of and vulnerability to misinformation and examines the consequences for social cohesion and democracy.Specifically, this dissertation asks: 1) How do citizens perceive misinformation, and what influences these perceptions? 2) How does the coexistence of different information environments in multilingual countries influence the spread of misinformation? 3) How is misinformation related to societal polarization? In response to the first question, the first two chapters show that citizens have a broad understanding of misinformation, perceive many different forms of misinformation as being prevalent and harmful to democracy, and continue to be critical of politicians spreading misinformation. While perceptions of misinformation form a relatively coherent belief system, citizens’ perceptions are influenced by their political information environment. Given current political discourses around misinformation, individuals with a right-wing ideology or consuming alternative right-wing media are more likely to perceive media misinformation as prevalent and less likely to perceive misinformation spread by social media users as prevalent and harmful than left-wing and centrist citizens. Right-wing citizens are also more indifferent to misinformation and less likely to support misinformation interventions, partly because they perceive current discourses around misinformation and content moderation as biased against them. I discuss how these findings can hinder the effectiveness of our response to misinformation.To answer the second question, Chapter 3 takes advantage of the high prevalence of COVID-19 misinformation in the United States and differential exposure to U.S.-based information among Canada’s English- and French-speaking populations to evaluate whether language creates a barrier to the spread of misinformation. The results suggest that Francophones insulated from the English-language information environment had somewhat lower levels of misperceptions than exposed Francophones and Anglophones, in part because of their lower exposure to U.S.-based content on social media. Exposed Francophones (i.e., bilinguals), especially heavy social media users, were slightly more likely to believe and spread misinformation online. However, compared to Anglophones, their misinformation-sharing behaviors were less dependent on their exposure to U.S. content. This chapter highlights the necessity of considering the globalized and interconnected nature of information environments when evaluating national resilience to misinformation.Finally, Chapter 4 introduces the concept of issue-based affective polarization – the distance between citizens’ positive feelings towards those who share their issue positions and negative feelings towards those who do not. It provides insights into the third question by showing that misinformation contributed to the high level of affective polarization on COVID-19 vaccines and climate change among the Canadian public by intensifying opinion divergence on these issues. Finally, it shows that affective polarization can persist even as the issue becomes less salient. The concluding chapter discusses the theoretical and practical implications of these findings

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)0.004
metaresearch head score (Gemma)0.018
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0070.007
Open science0.0000.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.010
GPT teacher head0.311
Teacher spread0.300 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicMisinformation and Its ImpactsFrench-language works237,207