drops on a lotus leaf
Here’s a strategy for scoring your own search results.
“Keywords” are the seven words most commonly occurring on the page. If these seven words are seen to be repeated on the page to an unusual degree, then it is a good assumption that the page was designed by the author to appear high on search results.
Keyword density is a measure of “gloss.” Most people will read pages with high keyword density as unusually glossy. Keyword density is not necessarily related to how genuine the page content appears to be otherwise, but most people will look askance at a page that is too glossy.
It should come as no big surprise that the pages that appear high on search results have been designed that way. They are deliberately glossy with high keyword density. You may consider whether to skip reading them or even loading them in your browser. Chances are good that the glossy pages are mostly advertising.
Generally you will find interspersed in your results a handful of sites with low keyword density. These are likely from universities, government sites, and research institutions that have sources of revenue beyond advertising. You may consider whether to load these up and skim through them. Probably they will show a publication date, author, and list of references, which will move your research forward.
It can be noted that AI-generated sites often exhibit high keyword density. This is probably deliberate so that they garner advertising revenue. However, it may also be due to “bot 'splaining,” which is polly-paraphrasing a series of several (perhaps contradictory) articles.
Keyword density is not the only measure of gloss. There are others that have been developed to measure ratios between parts of speech. Unfortunately NONE OF THESE — including keyword density — distinguish sharply between pages that naturally convey genuine information and pages that have been designed to convey fluff for ulterior purposes. It is unlikely that combining measures of gloss will result in a tool that discriminates much better than keyword density by itself.
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Piskorski, Jakub, Marcin Sydow, and Weiss Weiss. “Exploring Linguistic Features for Web Spam Detection: A Preliminary Study.” Airweb '08: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web. Ed. Carlos Castillo, Kumar Chellapilla, and Dennis Fetterly. New York: ACM, Apr. 2008. 25-28. ISBN:9781605581590. DOI:10.1145/1451983. 09 Nov. 2025 https://users.pja.edu.pl/~msyd/lingFeat08draft.pdf.
Nevertheless, you may wish to explore keyword density as a means to rank search results.
When I try to include a direct link to my python scripts, which do that, my responses and in fact the whole posted discussion are taken down. … something to do with self promotion of untested software I suppose. But you can find them in the Cheese Shop (See Wikipedia “Python Package Index.”) under clanker_score.
We don’t want to make this too easy for just anyone to censor all his search results. Rather, these scrips are meant as a learning tool. They demonstrate generally how rotten search results can be on one particular and not very compelling dimension. It should not be necessary to download and scan each and every page. You should be able to train yourself to ignore a priori results that include handfuls of pages from unauthoritative sites.
I’ve written a python/Qt script that keeps lists like those you mention. It’s sort of a poor-man’s database manager, but its only database is a collection of csv files.
You can use an address list to keep track of contacts’ phone numbers, mailing addresses, and eMail addresses.
You can use a calendar to remind you about events and appointments including date, time, and duration. You can add notes about finding the location and other prerequisites to attendance.
You can keep separate passwords in a password list for every website you visit and every piece of gear you own.
You can keep links to favorite websites in a bookmark list.
When I try to include a direct link to my python script, which does that, my responses and in fact the whole posted discussion are taken down. … something to do with self promotion of untested software I suppose. But you can find it in the Cheese Shop (See Wikipedia “Python Package Index.”) under tonto2.