Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media
Bibliographic record
Abstract
It is our great pleasure to welcome you to the Tenth Workshop on Natural Language Processing for Social Media-SocialNLP 2022, associated with NAACL 2022. SocialNLP is an inter-disciplinary area of natural language processing (NLP) and social computing. We hold SocialNLP twice a year: one in the NLP venue, the other in the associated venue such as those for web technology or artificial intelligence. This year the other version has been successfully held in conjunction with TheWebConf 2022 (formerly WWW), and we are very happily looking forward to the NLP version in NAACL 2022. We are very glad that the number of submissions to this year's workshop keeps increasing, and the submissions themselves were still of high quality with the accepted threshold of 3.33 (maximum 5), which again leads to a competitive selection process. We received submissions from Asia, Europe, and the United States. Considering the review process is rigorous and we want to encourage authors to participate in the workshop, we accepted 8 oral papers. These exciting papers include novel and practical topics for researchers working on NLP for social media, such as bias mitigation, domain transfer, and dataset constructed for the newly emerged research problems. We believe they will benefit our research community.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".