A WISER intervention to combat the influence of misinformation on social media
Bibliographic record
Abstract
Purpose Misinformation is a major threat to individual, organizational and societal well-being. Combating it requires change on multiple levels. The purpose of this paper is to describe two co-created initiatives designed to holistically reduce the prevalence and effects of misinformation on social media. Design/methodology/approach The authors adopted a co-creation approach by convening a stakeholder group including social media, advertising, artificial intelligence and advocacy leaders. The authors also engaged social media users to collectively generate, test and implement an anti-misinformation strategy. These stakeholders shaped the development of the tools described in this paper. Findings First, the authors present a Social Media Ecosystem Map and Incentive Analysis that achieve impact by documenting avenues for action. Second, the authors illustrate impact at the user level by developing and disseminating the WISER framework, which delivers an implementable and memorable anti-misinformation strategy. Research limitations/implications Strategies tailored to the needs and characteristics of stakeholders are most likely to be persuasive. The discoveries and conclusions incorporate the contexts of the stakeholders with whom the authors collaborated. Future work will reveal recommendations for a broader audience. Practical implications The Social Media Ecosystem Map, Incentive Analysis and WISER framework represent promising foundations to spark novel insights and actions for ways to be WISER about information on social media. Originality/value Impact is often limited because strategies are developed in academic, corporate or policy silos. The authors adopt an original approach by exchanging knowledge among industry, academia and users with the aim of maximizing effectiveness and adoption.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".