Repeatable measurement of Twitter user impact NASA and the great American Eclipse of 2017
Bibliographic record
Abstract
NASA is viewed as part of the frontier of human knowledge by several generations, and is relied upon to educate the public on astronomical matters. For decades NASA has provided not only North America but the entire world with information and events pertaining to our Universe and beyond. With the Great American Eclipse of 2017, NASA?s production was crucial to the general public?s awareness and understanding of the event. To date, it may have been NASA?s largest production of an event spanning many social media platforms and hundreds of Media outlets. With the eruption of data mining avenues and techniques available, being able to study and quantify such major events from a ?reach? perspective has become of utmost importance for many of the groups involved. Our goal with this paper is to understand how the public perceived the social media coverage that NASA had provided, specifically in the world of Twitter, a free social networking microblogging service that allows registered members to broadcast short posts called tweets. We accomplish this through sentiment analysis and the spotting of trends within Twitter data. Furthermore, we follow a framework of study that allows simple and cost-effective analysis of discrete events of arbitrary nature.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".