Social Media Influencers and Artificial Intelligence: Opportunities and Challenges
Bibliographic record
Abstract
This study examines social media influencers’ (SMIs) perceptions of the role of artificial intelligence (AI) in advancing their relationships with followers, their views of the opportunities/dangers of AI in assuring their authenticity, and their perspectives towards virtual SMIs in terms of affecting the future of SMIs. Adopting a qualitative phenomenological approach, the study explores how SMIs’ (a) perceive AI as a tool to help SMI activities, (b) perceive AI as a positive/negative effect on relationships with followers, (c) perceive virtual SMIs (non-human) as having an effect on the future of SMIs. Data was collected through 17 semi-structured interviews with Canadian SMIs in various fields. This study shows that AI is perceived to offer significant opportunities for SMIs including improved data analytics, enhanced connectedness, and strengthening SMIs’ relationships with their followers. However, SMIs are concerned about dangers of AI, including loss of expressing ones’ true self and loss of honesty with their followers. Collectively, participants indicated concerns about their authenticity when AI is used. Furthermore, virtual SMIs are perceived as a threat to SMIs, making it difficult for SMIs to compete on brand deals but also to possibly replace SMIs in the future. Additionally, this study reveals practical implications for SMIs by providing insights into opportunities and challenges being faced in the context of AI.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".