Classifying the Arabic web — A pilot study
Bibliographic record
Abstract
The world-wide-web has become the favorite destination of information seekers across the globe. With its massive amount of information that includes billions of web pages, information for just about any topic is a click-of-finger away. Analyzing the massive content of the web has many important aspects such as information discovering, efficient search engines and social and political patterns. Web mining techniques such as text classification and categorization are being used to provide an "under-the-microscope" picture of the web. The Arabic web represents an important portion of the web. With Arabic as the 5th most spoken language in the world and with the increasing number of Arabic Internet users at exponential rates, it is becoming important to analyze the Arabic web content and study its trends. This paper presents a close look at the content of the Arabic web. It presents the percentiles of the contents of the web in five categories, namely, politics, culture, sports, economics and religion. We used two different text classification algorithms and compared their results. We have also compared between the two text classification techniques in terms of precision and recall. The classifiers shown that the economics and politics are the highest percentiles (65% combined) while the culture and religion categories scored the lowest percentiles (about 10% combined).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".