Welcome to MegaGladys.

MegaGladys is a collection of people search tools based on the Wikipedia API. It uses Wikipedia and Wikidata information to both get preliminary data on someone with a Wikipedia page and to build useful external queries about them. Additional tools allow you to find news sources to search and discover similarities amongst a group of people or people and companies.

Enter a name in the form on the left, or use the search form below to keyword-search for people on Wikipedia. After you click the button you'll get a drop-down list of Wikipedia articles. Pick the one you want and it'll automatically appear in the name form on the left. Then click on one of the search tool buttons and start exploring. .





Currently MegaGladys has the following tools:

OG MegaGladys -- The original. Enter a name and get Wikipedia's information about them along with a ton of Wikidata (when available: social media accounts, official Web sites, reference links, and RSS.)

Gossip Machine -- Analyze Wikipedia's page views by month to find dates when someone's page got unusually high page views, then search those specific dates on Google News.

TV News Search -- Browse FCC-licensed TV stations by state and bundle them into a Google search (your name query is added automatically.)

Non-Sketchy News Search -- Keyword search Wikipedia to find media sources and bundle them into a Google search with your name query.

Biography Builder -- Build external reference searches bounded by someone's birth/death date (or birth/current date if they're still alive)

Crony Corral -- Check name pairs for common Wikidata characteristics, then search for Wikipedia pages that mention them both.

PeopleLinx Affiliations Lookup -- Search for large groups of names at a time. PAL will search Wikidata to find the education, employers, and organization memberships for each name and group names which have the same affiliations.

PeopleLinx Affiliations Filter -- Enter a list of people and a list of companies/institutions/organizations, and PAF will use Wikidata to find affiliations between the two groups.
MegaGladys is brought to you by Calishat.

Welcome to Gossip Machine

Gossip Machine uses Wikipedia page views to find days your Wikipedia topic got an unusual number of views, then turns those dates into Google searches. Choose a month and year (It starts with 2017) and click the button. You'll get a list of dates with z-scores and links to date-specific Google searches. You'll also see the average daily pageviews for that article in that month. Please note that the lower the pageviews, the more chance there is of wonkiness. 4000 avg pageviews will give you decent results. 311 page views a day probably won't.

Official and Reference Properties

Social Media: List 1

Social Media: List 2

As the volume of scholarship posted on the Internet increases by the day, it gets more difficult to find information about a subject that's contemporary to them.

Yes, research on Louisa May Alcott is extensive and important. But what were newspapers saying about her in 1854? The Contemporary Biography Builder works for any person listed in Wikipedia, but the further back in history you go, the less you're going to find (I would guess the big dropoff would be late 18th century.) CBB looks up your query's birth and death dates on Wikipedia (if the person is still alive it substitutes the current year), then creates searches limited to the person's lifespan for the following resources:

  • Google Books
  • Internet Archive
  • Digital Public Library of America (DPLA)
  • Chronicling America (Historical Newspaper Database from Library of Congress)
Do you ever find yourself doing research on a person where you have a name and a lot of biographical information, but no real direction for searching? Biography Buckets is for you. Biography Buckets takes biographical events or keywords associated with a person and uses them to build date-based searches across the following Web resources: Google News, Google Scholar, Google Books (Magazines Only), Google Books (Newspapers Only), Twitter, Reddit (via SocialGrep) and Newspapers.com. Twitter, Reddit, and to a lesser extent Google News are only suitable for searches spanning ~2010 and later.

You've already specified who you're searching for. With Biography Buckets, you'll add search terms you want to include in your search of this person. These terms can include where they went to school, spouses, names of notable works, where they lived, etc. You can even include generic terms that are frequently associated with the person, like "vaccine" in this example.

After you've done that, choose the time spans that you want to run searches for and click the "Generate Search URLs" button. Experiment with different span lengths -- in the case of Jonas Salk, for example, maybe you do a thirty-year search to cover his early life, do a somewhat shorter search that spans the time between his education and the polio vaccine, do a very short two-year search focused on the vaccine era, etc.

Biography Buckets will generate the search links with each date-bounded search term going in its appropriate time span(s) query along with the name of the person for whom you're searching. Click on the link and it'll open in a new tab.

 
 

Web Resource You Want to Search:

List the Biographical Events You Want to Build Into a Search

List places/events/people relevant to the person's life and the span of years they were relevant.

Add Date Ranges You Want to Search

Enter the date spans you want your resource searches to cover. Biography Buckets will automatically sort the biographical events into the right spans and build your queries.

Generated URLs

Crony Corral searches Wikipedia to find people/companies/organizations with matching Wikidata properties and further searches for Wikipedia topics they have in common.


Properties searched include:

  1. P159: headquarters location - Location of the main office of an organization, company, or institution.
  2. P108: employer - Used to link a person to the organization or company they work or have worked for.
  3. P69: educated at - The educational institution(s) a person has attended.
  4. P551: residence - The place where a person lives or has lived.
  5. P102: member of political party - The political party a person is or has been a member of.
  6. P106: occupation - Refers to the main job or profession of a person.
  7. P39: position held - Used to link a person to the political, organizational, or professional positions they have held.
  8. P937: work location - Indicates the place where a person primarily conducts their work.
  9. P452: industry - Refers to the main industrial sector or sectors that a company, organization, or product is involved in.
  10. P17: country - Indicates the country that a geographical entity or organization is part of or associated with.
  11. P1056: product or material produced - Refers to the main product(s) or material(s) produced by a company or organization.
  12. P749: parent organization - Indicates the higher-level organization that a subsidiary or lower-level organization is part of.
  13. P414: stock exchange - Refers to the stock exchange where a company's shares are traded.
  14. P112: founded by - Indicates the person or organization that founded a company, organization, or institution.
  15. P127: owned by - Refers to the person, organization, or entity that owns a particular asset or resource. (I think institutional / stock ownership is in here too.)
  16. P355: subsidiary - Used to link a parent company or organization to its subsidiaries or lower-level organizations.
  17. P27: country of citizenship - Indicates the country where a person holds citizenship.
 

 

PeopleLinx Affiliations Lookup (PAL)

Enter a list of names separated by commas. (I have entered up to 125 and that works, but it takes a minute to sort and group the names.) PAL will search Wikidata to find the education, employers, and organization memberships for each name and group names which have the same affiliations.

Each row has a set of links for searching the row's name and its affiliation on Google, Bing, and DuckDuckGo. Click one and it'll open in a new tab.

If you want an example, try Annie Lennox, Elton John.





 

PeopleLinx Affiliation Filter (PAF)

Enter a list of people separated by commas and a list of companies / organizations separated by commas. PAF will use Wikidata data to find affiliations between the first list and the second and present you with a list. I have checked 125 people against 75 companies at a time and it works but you'll need to wait several seconds for the names to be sorted. Make sure you enter possibly-ambiguous company names correctly -- Steve Jobs will not match Apple but he will match Apple Inc.





    This tool finds American TV stations by state (via the FCC Licensing & Databases Public Inspection file), groups them by city, and lets you search their Web space with Google as well as find recent published news stories via Google News. Your name search will automatically add to the query. Start by using the pull-down menu to find a state.

    Please note that station lists, especially for large states, may take several seconds to load.

    No Stations Selected No Stations Selected

    Non-Sketchy News Search (NSNS) v2

    NSNS uses SPARQL queries to find media resources by keyword and bundle them into Google queries. Your search name will be automatically added to the query via the additional search term form unless you change it.




    After you run the search you'll get a list of media outlets that match your search terms. Click on the checkboxes of the outlets you'd like to include in a Google search and click the button at the bottom of the list. A Google search will open in a new tab.

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