Proceedings of the 3rd 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 this workshop, we will look at collective intelligence in social media, that is, looking at how we can make 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 checkins, in order to create new knowledge and semantic meaning. The goal of this workshop is to continue our vibrant discussion on modeling social media, focusing on the collective intelligence. The workshop aims to attract and discuss various aspects of collective intelligence in social media which involve frameworks for collecting appropriate data and collecting enough amount of data, mining the data from different social media and context (e.g., from mobile phones), creating models for inferring collective intelligence, and evaluating the framework to determine the accuracy of the gathered collective intelligence. We want to bring together researchers and practitioners with diverse backgrounds interested in 1) exploring different perspectives and approaches to modeling complex social media phenomena and systems through collective intelligence, 2) the different purposes and applications that can be created from collective intelligence of social media, and 3) issues of integrating and validating collective intelligence of social media. The call for papers attracted 10 submissions, from which we were able to accept seven submissions (four full papers and three short papers) based on a rigorous reviewing process. Additionally, the workshop features an invited talk on pragmatics and semantics in social tagging systems. The accepted papers cover a variety of topics, including social media and physical proximity, communities and influence, user interests in social media, and an application-oriented view on collective intelligence in social media. We hope that these proceedings will serve as a valuable reference for researchers and developers.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".