Follow-Up Investigation on the Promotional Practices of Electric Scooter Companies: Content Analysis of Posts on Instagram and Twitter
Bibliographic record
Abstract
BACKGROUND: Electric scooters (e-scooters) have become a popular mode of transportation in both the United States and Europe. In the wake of this popularity, e-scooters have changed the commuting experience in many metropolitan areas. Although e-scooters offer an efficient and economical way to travel short distances in traffic-congested areas, recent studies have raised concerns over their safety. Bird and Tier Mobility are 2 popular e-scooter companies in the United States and Europe, respectively. Both companies maintain active social media accounts with hundreds of posts and tens of thousands of followers. Recent studies have shown that consumer behavior may be influenced by the content posted to popular social media platforms, such as Instagram and Twitter. OBJECTIVE: This study aimed to examine the official Instagram and Twitter accounts of Bird and Tier Mobility to determine whether these companies promote and demonstrate the use of safety gear in their posts to their consumers. METHODS: Posts to Bird's (n=287) and Tier Mobility's (n=190) official Instagram accounts, as well as Bird's (n=313) and Tier Mobility's (n=67) official Twitter accounts, were collected from November 9, 2018, to October 7, 2019. Rules for coding content of posts were informed by previous research. RESULTS: Among posts to Bird's Instagram account, 69.3% (199/287) had a person visible with an e-scooter, 9.1% (26/287) contained persons wearing protective gear, and there were no mentions of protective gear in captions corresponding to the post. Among posts to Tier Mobility's Instagram account, 84.7% (161/190) contained a person visible with an e-scooter, 36.3% (69/190) contained persons wearing protective gear, and 4.2% (8/190) of captions corresponding to posts mentioned protective gear. Among posts to Bird's Twitter account, 71.9% (225/313) had an image, of which 44.0% (99/225) contained a person visible with an e-scooter and 15.1% (34/225) contained persons wearing protective gear. Among posts to Tier Mobility's Twitter account, 78% (52/67) had an image, of which 52% (27/52) contained a person with an e-scooter and 21% (11/52) contained persons wearing protective gear. CONCLUSIONS: Findings show that modeling and promoting safety is rare on Bird's and Tier Mobility's official social media accounts, which may contribute to the normalization of unsafe riding practices. Social media platforms may offer a potential avenue for public health officials to intervene with rider safety campaigns for public education.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".