Personality-Driven Innovation Adoption: Modeling ChatGPT Diffusion with BERT and Random Forest
Bibliographic record
Abstract
In innovation adoption, individuals' decision-making process is shaped by innovation's attributes and is primarily affected by their unique personality traits, which have a bearing on their perception levels.This current study introduces a novel personality-driven innovatin adoption model that combines Rogers' diffusion theory with the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) personality model.Our approach accounts for the significante influence of individual differences in adoption decisions by using personalized features.Our approach accounts for the significante influence of individual differences in adoption decisions by using personalized features.We analyze ChatGPT adoption using a dataset of 38,939 tweets and user metadata after preprocessing.A BERT-Random Forest model predicts users' Big Five traits, which help refine innovation attributes such as relative advantage, uncertainty, and acceptability, ensuring the adoption process aligns with psychological factors.This proposed framework holds significant potential for forecasting individual adoption behaviors by acknowledging the relevance of individual psychological traits in the decision-making process.Furthermore, We compare our personality prediction approach with baseline models including LSTM + Random Forest, BiLSTM + Random Forest, and RoBERTa + Random Forest, and show that BERT + RF achieves the most realistic personality feature estimates.Incorporating personality-driven and recalculated perceived innovation attributes enhances the model's ability to simulate real-world adoption patterns, providing a deeper understanding of how innovations diffuse in social networks.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".