Uncovering Susceptibility Risk to Online Deception in Aging
Bibliographic record
Abstract
OBJECTIVES: Fraud in the aged is an emerging public health problem. An increasingly common form of deception is conducted online. However, identification of cognitive and socioemotional risk factors has not been undertaken yet. In this endeavor, this study extended previous work suggesting age effects on susceptibility to online deception. METHODS: Susceptibility was operationalized as clicking on the link in simulated spear-phishing emails that young (18-37 years), young-old (62-74 years), and middle-old (75-89 years) Internet users received, without knowing that the emails were part of the study. Participants also indicated for a set of spear-phishing emails how likely they would click on the embedded link (susceptibility awareness) and completed cognitive and socioemotional measures to determine susceptibility risk profiles. RESULTS: Higher susceptibility was associated with lower short-term episodic memory in middle-old users and with lower positive affect in young-old and middle-old users. Greater susceptibility awareness was associated with better verbal fluency in middle-old users and with greater positive affect in young and middle-old users. DISCUSSION: Short-term memory, verbal fluency, and positive affect in middle-old age may contribute to resilience against online spear-phishing attacks. These results inform mechanisms of online fraud susceptibility and real-life decision-supportive interventions toward fraud risk reduction in aging.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".