Social media marketing: Who is watching the watchers?
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
The ready access to and availability of social media has opened up a wealth of data that marketers are leveraging for strategic insight and digital marketing. Yet there is a lack of professional norms regarding the use of social media in marketing and a gap in understanding consumers’ comfort with marketers’ use of their social media data. This study analyzes a census-balanced sample of online adults (n=751) to identify consumers’ perceptions of using social media data for marketing purposes. The research finds that consumers’ perceived risks and benefits of using social media have a relationship with their comfort with marketers using their publicly available social media data. The research extends the applicability of communication privacy management theory to social media and introduces marketing comfort—a new construct of high importance for future marketing research. Marketing comfort refers to an individual’s comfort with the use of information posted publicly on social media for targeted advertising, customer relations, and opinion mining. In the context of the construct development, we find that targeted advertising is the strongest contributing component to marketing comfort, relative to the other two dimensions: opinion mining and customer relations. By understanding what drives consumer comfort with this emerging marketing practice, the research proposes strategies for marketers that can support and mitigate consumers’ concerns so that consumers can maintain trust in marketers’ digital practice
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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