What makes online political ads unacceptable? Interrogating public attitudes to inform regulatory responses
Bibliographic record
Abstract
Abstract Online political advertising is often portrayed negatively, yet there is limited evidence regarding what exactly the public deems unacceptable. This paper provides new insights into public attitudes based on an online survey conducted in 2022, in which 1881 respondents evaluated political ads placed on Facebook during the 2019 UK General Election. We find that citizens do not inherently view political ads as unacceptable, and that perceptions of acceptability are influenced by partisan and demographic factors. We also find that ads deemed compliant with existing regulatory protocols for non-political advertising are considered more acceptable, suggesting a case for extending the existing regulatory regime to political ads. Delving deeper into our survey data, we explore the drivers behind these perceptions of acceptability and find that concerns about the content and tone of ads play a significant role. These findings provide valuable insights for those seeking to develop codes of conduct to govern practices in this space. Overall, our study offers a nuanced understanding of public attitudes toward online political advertising and identifies possible pathways for regulatory reform.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".