Web Scraping Techniques and Applications: A Literature Review
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
Big data analytics gives organizations a way to analyze huge data sets and gather new information. It helps answer basic questions about business operations and business performance. It also helps discover unknown patterns in vast datasets or combinations thereof. In the current data-driven world, it becomes increasingly essential that big data techniques are applied and analyzed for organizational growth. More specifically, with the large availability of data on the Web, whether from social media, websites, online portals, or platforms, to name but a few, it is important for organizations to know how to mine that data in order to extract useful knowledge. Web scraping represents a fundamental approach in this regard. Therefore, this paper aims to provide an updated literature review about the most advanced Web Scraping techniques to better equip scholars and managers with helpful knowledge on how to mine most effectively online data. The paper starts with presenting the basic design of a web scraper and the applications of web scraping in diverse sectors and areas. Next, the different Web scraping methods and Web scraping technologies are presented. Finally, a procedure to develop Web scraping with various tools is proposed before a conclusion wraps up the paper.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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