Classifying the Arabic web — A pilot study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
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) | 0.000 | 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 it