Bibliographic record
Abstract
ChatGPT is reshaping ideas of agency, companionship, and emotional intelligence in a post digital world. Research has often focused on ChatGPT’s implications in industry settings, such as education, but less attention has been given to how humans personally engage emotionally with digital AI tools. TikTok, shaped by algorithmic visibility and creative user content, offers a unique space to examine these interactions, particularly among younger generations. As ChatGPT’s popularity grows, TikTok has become a site where discourse around generative AI’s adoption and usage unfolds and can be situated as a rich context for analyzing its role in everyday life. This work-in-progress adopts a posthumanist lens to examine how TikTok users construct narratives around ChatGPT's social roles, exploring the platform as a system where norms are renegotiated, and social narratives are co-constructed. The findings reveal that ChatGPT is frequently positioned as a friend, attributed to perceived affordances such as its perceived neutrality, limited need for socioemotional reciprocity, and constant availability. These affordances can lead to emotional dependency and overreliance, as users view these AI features as more advantageous for social and emotional connections than human interactions. The preliminary findings underscore ChatGPT’s emerging role as a relational entity in human-AI interactions. This paper contributes to ongoing discussions about the implications of AI integration as an agent in society and highlights broader shifts in societal norms, and provides insights into how TikTok, as a networked platform, plays a role in these shifts.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".