The Role of Advertisers and Platforms in Monetizing Misinformation: Descriptive and Experimental Evidence
Bibliographic record
Abstract
The financial motivation to earn advertising revenue by spreading misinformation has been widely conjectured to be among the main reasons misinformation continues to be prevalent online.Research aimed at reducing the spread of misinformation has so far focused on user-level interventions with little emphasis on how the supply of misinformation can itself be countered.In this work, we show how online misinformation is largely financially sustained via advertising, examine how financing misinformation affects the advertisers and ad platforms involved and outline ways of reducing the financing of misinformation.First, we find that advertising on misinformation outlets is pervasive for companies across several industries and is amplified by digital ad platforms that automatically distribute companies' ads across the web.Using an information provision survey experiment, we show that people decrease their demand for a company's products or services upon learning about its role in monetizing misinformation via online ads.To shed light on why misinformation continues to be monetized despite the potential backlash for the advertisers involved, we survey decision-makers at companies.We find that most decision-makers are unaware of their companies' ads appearing on misinformation websites but have a strong preference to avoid appearing on such websites.Moreover, those uncertain and unaware about their role in financing misinformation increase their demand for a platform-based solution to reduce monetizing misinformation upon learning about how platforms amplify ad placement on misinformation websites.We identify low-cost, scalable information-based interventions that digital platforms could implement to reduce the financial incentive to misinform and counter the supply of misinformation online.
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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 | 0.022 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".