Conspiracy beliefs and analytical thinking in COVID-19 information web search
Bibliographic record
Abstract
The phenomenon of conspiracy theories has seen a considerable increase in popularity on the internet, especially in the health domain. Surprisingly, despite a substantial body of research, none has directly examined the information-search process of conspiracists as they navigate on the Internet. This study examines how conspiracy theorists search for online information (through the Exploration/Exploitation trade-off), using a simulated COVID-19 fact-finding task on vaccine side effects presenting official and conspiracy webpages. The study investigates how conspiracy levels and analytical thinking predict navigational strategies and the acquisition of new knowledge. Results show that analytical thinking predicts the use of exploratory navigation strategies. Analytic thinkers gather more useful information from official webpages and have more confidence in this information. Conversely, conspiracists gather more novel information from conspiracy webpages and have more confidence in these sources. This study offers a novel approach by combining the psychology of belief, reasoning, and Internet information search. • Conspiracy beliefs relate to how people search health info on the Internet. • Analytical thinkers tend to be more critical regarding their evaluation of online information. • Conspiracy theorist tend to visit fewer “official” webpages during information search. • Reasoning style is related to navigation search strategies. • Exploratory findings reveal distinct information processing patterns across conspiracy theorist and analytic thinkers.
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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".