Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media
Bibliographic record
Abstract
Social Media e.g., Twitter, Blogs, Forums, FaceBook, Google Buzz has exploded over the last few years. FaceBook is now the most visited site in the US, overtaking Google in the first quarter of 2010. These sites contain the aggregated beliefs and opinions of millions of people on an epic range of topics, in a multitude of languages. Social Media presents many challenges and opportunities to the ACL community, with this workshop being the first of its kind at a computational linguistics venue. Accepted papers range from story detection and tracking to discourse, applied across new and old media including company announcements, news, forums, blogs and micro-blogs. A notable aspect is the predominance of Twitter as a Social Media resource.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.023 |
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".