The Shifting Landscape of Web Search and Mining
Bibliographic record
Abstract
The Web's content has been going through major changes, triggered by multiple factors including changes in user demographic and authoring behaviour, a shift in device types that access the Web, and changes in common use cases of the Web. More specifically, the number of mobile internet users has surpassed the desktop users according to different statistics; a considerable portion of web use cases are in the form of social interactions rather than information seeking; and the authoring behaviour has transformed from compiling a page and linking resources to sharing content with like-minded followers and leaving likes and comments on posts. Those changes have influenced and are expected to shape the way the content is organized, searched, ranked and analyzed. This panel brings together researchers who have been working in different established areas related to web search and mining, web content and social network analysis, and semantics and knowledge management. The panel will draw from the experience of the panellists, dealing with changes in their respective fields. In the first (role-playing) round, each panellist will strongly take a side on where the changes are heading, arguing that one form of content will dominate in the near future. In the second round, the panellists will counter each other and will share their vision on what future holds in terms of research problems and directions. The members of the audience will participate, in a QA session with the panellists, bringing their own perspectives to the discussion.
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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".