The In-Situ Effect of Offensive Ads on Search Engine Users
Bibliographic record
Abstract
Unscrupulous advertisers may try to increase attention to search ads by using offensive ads, which can increase attention and recall to the detriment of individuals and society. Here, we investigate whether offensive ads, when shown to search engine users, have such effects. We developed 12 search scenarios and created 4 versions of the search results page (SERP) for each scenario, where some of the ads were changed to be irrelevant and/or offensive. Crowdsourced judges found a strong correlation ( \(\geq 0.63\) ) between the reported number of annoying ads and the actual number of offensive and irrelevant ads, suggesting people conflate these attributes. Furthermore, we found that judges who assessed the SERPs for themselves reported lower positive affect and higher negative affect than judges asked to imagine the results were provided to someone else. In the latter case offensive ads also lead to slightly lower positive ( \(-4\%\) ) and higher negative affect ( \(+61\%\) ). Finally, in a recall test, only 6% of judges reported seeing an offensive ad when using search engines. Our work should further detract advertisers from using offensive ads since, in addition to previously documented adverse effects, such ads have a small but statistically significant negative effect on people’s emotional experience.
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 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".