User experience with disinformation-countering tools: usability challenges and suggestions for improvement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital media has facilitated information spread and simultaneously opened a gateway for the distribution of disinformation. Websites and browser extensions have been put forth to mitigate its harm; however, there is a lack of research exploring their efficacy and user experiences. To address this gap, we conducted a usability evaluation of two websites and three browser extensions. Using a mixed methods approach, data from a heuristic evaluation and a moderated, task-based usability evaluation are analyzed in triangulation with data collected using summative evaluations. Challenges are identified to stem from users’ inability to understand results due to the presentation of information, unclear terminology, or lack of explanations. As a solution, we recommend four design principles: First is to establish credibility, second is to improve the general visual layout and design of the tools, third is to improve search capabilities, and finally, heavy importance should be given to the depth and presentation of information.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it