Automated Discovery of Emerging Online Communities Among Blog Readers: A Case Study of a Canadian Real Estate Blog
Bibliographic record
Abstract
More than 184 million people worldwide have started a web blog, that collectively attracted at least 346 million blog readers. Due to their popularities, web blogs have been the focus of many recent Internet studies. Aside from being a great publishing platform, many of these studies confirmed the fact that modern blogs with commenting-capabilities are also great places for meeting like-minded individuals and forming new social relationships. As a result, it is not surprising that there is also a growing interest in discovering and characterizing online communities that tend to naturally form around some web blogs. Traditional analyses of blog are hampered by the expensive and time consuming processes. To address these problems, this paper proposes an automated approach for the discovery of social networks among blog readers just from their comments posted to a blog. This new approach is called “name network” and is an integral part a companion web-based tool called Internet Community Text Analyzer or ICTA for short (http://textanalytics.net). The “name network” method is capable of automatically discovering a social network among blog readers that accurately represents group dynamics.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".