Analyzing Trends in the Global Electric Vehicle Conversation on Twitter
Bibliographic record
Abstract
As electric vehicles continue to become an increasingly important part of the transportation industry, it is crucial to understand which countries are leading the way in this area. This study explores the extent to which Twitter can provide insight into the global electric vehicle landscape. By analyzing hashtag usage levels in tweets from 2018-2022, this study identifies trends in how different countries are talking about electric vehicle adoption on Twitter. It has been shown that technology and policy are two of the most important factors influencing electric vehicle adoption. As such, hashtags were chosen to provide insight into four areas: Electric Vehicle Adoption Generally, Electric Vehicle Companies, Electric Vehicle Technologies, and Electric Vehicle Policies. The results suggest that the global conversations about electric vehicle adoption generally and electric vehicle charging are trending upward and that the conversations about autonomous vehicles and self-driving cars are trending downward. The results also show that the United States, Great Britain, India, and Canada are leading the conversation about electric vehicles on Twitter.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".