What to believe, whom to blame, and when to share: exploring the fake news experience in the marketing context
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
Purpose The spread of fake news on social networking sites (SNS) poses a threat to the marketing landscape, yet little is known about how fake news affect consumers’ perceptions, attitudes and behaviors. This study aims to explore when consumers believe fake news, whom they blame for it (e.g. negative attitudes toward brands or SNS) and when they choose to share it. Design/methodology/approach Data obtained from 80 open-ended, semistructured interviews, conducted with SNS consumers and experts, is analyzed following the principles of grounded theory and the Gioia methodology. Findings Factors affecting consumers’ perceptions of fake news include skepticism, awareness, previous experience, appeal and message cues. Consumers’ brand- and SNS-related attitudes are affected by consumers’ blame, which is determined by consumers’ perceptions of the vetting efforts, role and ethical obligation of SNS. Consumers’ motives for sharing fake news include duty, retaliation, authentication and status-seeking. Theoretical and practical implications derived from the study’s novel conceptual framework are discussed. Practical implications This study identifies communication strategies that marketing professionals can use to mitigate and counter the negative effects of fake news. Originality/value By simultaneously considering consumers’ perceptions of the source, information and medium (i.e. SNS), this study presents a novel conceptual framework providing a marketing-centered, dynamic view on consumers’ fake news experience and connecting consumers’ perceptions, attitudes and behaviors in the context of fake news.
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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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