Bibliographic record
Abstract
Traffic incident information is disseminated via Twitter from various types of accounts. It is more common to find active or transitive verbs and adverbs in tweets from personal accounts because individuals report personal experiences (e.g., “just saw an accident”). Tweets from an organization or agency [e.g., 511 (a telephone hotline for transportation information widely used in the United States and Canada), departments of transportation] are more structured and commonly include nouns and past participles (e.g., “one lane blocked”). Organization accounts mostly provide incident location, type, severity, and so on, whereas personal tweets do not usually provide such details. However, an agency tweets about an incident usually after the incident management (IM) officials have already been notified. Because of this timing, a personal tweet is more likely to carry useful “new” information for IM purposes. This study investigated the detection of traffic incidents through Twitter feeds by using these differences in structure and information content found in organization and personal social media accounts. Tweets collected via the Twitter public application programming interface were manually coded and treated separately as either personal or organizational, and the “dictionaries” used to perform relevancy classification were derived separately. Combinations of dictionaries (i.e., personal only, organizational only, personal and organizational) were used for “term frequency–inverse document frequency” and naïve Bayesian analysis. It was shown that analysis specific to account types helped achieve better accuracy in classification for targeting relevant tweets. Therefore, account-specific analysis should be considered for more efficient and effective event detection for IM purposes.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".