Proceedings of the 4th International Workshop on Modeling Social Media
Bibliographic record
Abstract
In our first workshop on Modeling Social Media (MSM 2010 in Toronto, Canada), we explored various different models of social media ranging from user modeling, hypertext models, software engineering models, sociological models and framework models. In our second workshop (MSM 2011 in Boston, USA), we addressed the user interface aspects of modeling social media. In our third workshop (MSM 2012 in Milwaukee, USA), we looked at the collective intelligence in social media, i.e. making sense of the content and context from social media websites such as Facebook, Twitter, Google+ and Foursquare by analyzing tweets, tags, blog posts, likes, posts and check-ins, in order to create a new knowledge and semantic meaning. The goal of this workshop (MSM 2013) is to continue our vibrant discussion on social media mining and modeling with a special focus on recommender systems for social media applications. Hence, the workshop aims to attract and discuss various novel aspects of social media mining, modeling and doing recommendations on top of these data/models. In short, the workshop invites topics such as social media mining methods/techniques, novel approaches to model users or things in social media, frameworks to harvest and/or display social media data and new social media recommender methods/techniques/algorithms or interfaces supporting users for instance in information finding, meta-data application etc. Thus, our goal is to bring together researchers and practitioners from all over the world with diverse backgrounds interested in 1) exploring different perspectives and approaches to mine (complex) and analyse social media data, 2) modeling social media users and 3) building applications such as recommender systems on top of this data/models.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".