Bibliographic record
Abstract
Research in the area of misinformation online has identified various factors as the reason why people spread misinformation online such as availability of technology, entertainment, ignorance, to pass time, altruism etc. However, how these factors differ from one age group to another is not known. Research also suggests that people of different generations or age groups behave differently and are influenced differently. While people of each age range will have differences among them, they will likely behave similarly compared to people of other age groups. Therefore, in determining why people spread misinformation online, it is important to investigate any differences based on the age groups of online users. This will ensure that interventions designed to curb the spread of misinformation can be tailored to people based on their age. To contribute to research in the area of determining why people of different age ranges spread misinformation online, we surveyed 113 social media users of varying age groups. Our results show that the younger participants between 18 and 34 years are more likely to spread misinformation due to the availability of technology, entertainment, the need to pass time, the fear of missing out, peer pressure and trust in people online.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".