Repeatable measurement of Twitter user impact NASA and the great American Eclipse of 2017
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it