Envisioning Interventions to Combat Misinformation Propagation on Social Media: Insights from Older Adults' Approaches to Credibility Assessment
Bibliographic record
Abstract
While users of all ages fall victim to misinformation sharing on social media, older adults (OAs) may be particularly vulnerable. Yet, current misinformation interventions have rarely incorporated their needs. With the growing adoption of social media among OAs, it is essential to understand their perspectives on credibility assessment and its perceived impact on sharing decisions. Leveraging friends and family (FnF) to support credibility assessment is a promising approach; however, little is known about how OAs value it relative to assessing credibility individually. To probe this question, we created a prototype with different design variants that nudge users before sharing potential misinformation. We conducted comparative structured observations of 12 OAs engaging with the design variants over selected social media posts, examining their perspectives on individual and FnF-based community assessment. Our thematic analysis reveals that OAs prefer an independent approach while assessing credibility because of its flexibility and autonomy, but involving FnF is often desired for the social opportunities it offers, such as shared decision-making and enhancing interpersonal relationships. OAs perceive multifaceted risks when sharing across inner (FnF) and outer circles and exhibit varying trust in the perceived expertise of individually explored information sources, compared to that of FnF. Drawing on a nuanced understanding of social and relational factors that shape OAs' outlook toward engaging FnF in fact-checking, we highlight the design challenges and opportunities for empowering OAs to mitigate the social risks in seeking assessment support from their closest communities.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".