Why LinkedIn Is Unavoidable and Irreplaceable

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For the majority of sourcing tasks, LinkedIn provides the best database to use:

  • It is the largest professional database (for comparison, only a tiny percent of Facebook or Twitter users have added their job title and employer to the bio)
  • It has self-entered data, the latest from each member
  • It offers many decent search filters.

If you are considering an alternative to LinkedIn such as Hiretual, SeekOut, AmazingHiring, or another aggregator, they do have data collected from many sources, but:

  • Professional data (including contact info) gets outdated fast. It is not possible to keep the base up-to-date due to the massive volume of it. The older the product, the more data gets wrong
  • You may be able to find the same people with a LinkedIn search
  • The data may not cover your location or target technology (something to test).

(So do not be unnecessarily jealous of your colleagues with these subscriptions.)

An excellent (but partial) solution would be a tool that does a dynamic (live) search to pull up-to-date data on each search result/profile. I do not know of a sourcing tool doing that; I believe the OSINT tool SocialLinks has this technology. It is only a partial solution because there is still no access to an updated cross-referenced base for a filtered search. So we are back to LinkedIn to search. 🙂

Indeed, sourcing elsewhere puts you at an advantage. Finding online directories or membership profiles feeds the process with valuable information. But only on LinkedIn and in resumes do most people write out their title, company, length of the current job, and location. Github, for example, does not even offer a field for the job title (while the site gets more and more participants who do not write code for a living.)

So getting data from elsewhere and cross-referencing on LinkedIn will add value compared on only searching on LinkedIn.

Of course, in some industries or at entry levels, people rarely join LinkedIn. Then, sourcing is a much more challenging task.

We all wish they would have Customer Support and a better UX, though!

Scraping Values to Feed the Boolean Builder

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Undeniably, scraping has become a must-use skill for anyone who sources for hard-to-fill roles, profiles outside of LinkedIn, or Diversity.

Sadly, Sourcers are not blessed with a satisfactory advanced scraper. (We know of the perfect design, but the app was abandoned. Know of a strong developer team?) However, for the simpler job of recognizing and collecting similar records on a page and following pages of results if necessary (on Google or elsewhere, but not on LinkedIn), Instant Data Scraper is the perfect solution.

One delightfully easy – non-technical and fast – yet productive application of scraping using the tool is quickly collecting lists of values such as common womens’ names or company, school, or association names (etc.) and automatically assembling OR search strings for LinkedIn such as, for example, members of one or more Nurse Associations in the US:

(“American Association of Critical-Care Nurses” OR(“American Association of Managed Care Nurses”) OR(“American Association of Nurse Anesthetists”) OR(“American Association of Nurse Practitioners”) OR(“American Association of Occupational Health Nurses”) OR(“American Nurses Association”) OR(“Association of Pediatric Hematology/Oncology Nurses”) OR(“Association of periOperative Registered Nurses”) OR(“Emergency Nurses Association”) OR(“Infusion Nurses Society”) OR(“National Association of Clinical Nurse Specialists”) OR(“National Association of Neonatal Nurses”) OR(“Society of Pediatric Nurses”))

(you know why the formatting is strange, right?)

This string looks for members of at least one of the national associations for nurses in one shot. There are many hack applications other than searching for association memberships. Examples are searching for graduates of a specific kind of Universities (like HBCU) or employers from a list (like top image recognition companies). The technique allows access to search filters that the site otherwise will not provide. Specifically, you can use it in Diversity sourcing for each Diversity type. The good news is that these searches are easily automated; do the following.

You have seen how simple Googling quickly lands on a page with the correct list for any category (check the info for validity, though, just in case). Go to that page, run Instant Data Scraper, and download the list of terms in CSV. Next, paste the list into your copy of the LinkedIn Boolean Builder. Finally, transfer the auto-generated OR string to the right search field in the advanced LinkedIn people search. That’s it – you automatically run inclusive searches such as the long one above.

To summarize:

  1. Google for a list or get it elsewhere (like a list of target companies from the Hiring Manager – that would give you more choices)
  2. Use Instant Data Scraper to collect the values if Googled
  3. Use the LinkedIn Boolean Builder to create an OR string
  4. Paste the generated string into advanced LinkedIn people search.

When people see a live demo of the steps for the first time, they have a “wow” reaction. (Let me know if you have trouble reproducing them.)

As a warning, this automation hack is for LinkedIn only and not for Google. I hope we are in agreement on that.

BTW, have you noticed how the respective searching abilities of Google and LinkedIn make it a solution?


How to Talk to Image Search

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To access the largest number of Google Image Search filters, it is best to use the Advanced Image Search Dialog. Image Search also accepts the operators: site:, filetype: (image file type, not page file type), and imagesize:.

But did you know that you can also affect some advanced filters (for which there are no operators) by adding specific keywords to your search?

Using a color name, you can affect the color of results. An illustration is green cats that mostly arrives at Christmas cat pictures as above. (Creative uses, anyone?) For black-and-white images, use black and white: black and white moscow. (I had also tried non-standard color names, but it did not work.)

Not practical (I think), but fun: naming an image size like icon, small, medium, or large finds images of that size; for example, small picasso finds small images.

Google ignores regions and aspect ratios in a search string, but there wouldn’t be a need for that.

Image search is an underused technology. To learn about its other nifty features, check out this class.






The Education Confusion on LinkedIn

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LinkedIn people search would work better if everyone had:

  • one degree with the dates
  • one current job with a start date.

But so many members have more than one degree. Let us see how it affects the search.

  1. The education dates and schools/majors are not connected. If you search – in Recruiter or an alumni school page – for a date range and a school, you will find people who have been students there but got another degree within the specified years.
  2. Searching by education dates also finds people with no dates.

For that reason, you will not be able to find exactly people who got, say, a Master’s in Computer Science within 2017-2021.

You cannot find people with a completed degree either. If you search for a Bachelor’s with the grad dates in the past, you will find current undergrads who have listed a high school on the profile.

Similarly, you cannot find people who majored in a given subject at a given school.

However, searching for a graduation date in the future in LinkedIn Recruiter reliably finds current students. My problem is that I am looking for students with good grades, and LinkedIn Recruiter misses most.

X-Ray helps, but X-Raying for current students is not easy. Most students work part-time, and the profile titles reflect jobs, not the study – though searching for “junior” or “intern” in the page titles helps – as well as “student,” of course.

I have been using future years in X-Ray because the keywords 2023, 2024, etc. are likely graduation dates (though not just). Example:

site:pl.linkedin.com/in “GPA * *” “computer science” 2023 student.

But finding people who will graduate in 2022 is problematic since many people have started working in 2022, so using this year as a keyword is a weak method.

This –

site:linkedin.com/in “computer science” “expected to graduate in 2022”

gets perfect results, but too few of them.

It has been quite an adventure!

When a search is hard to master, scraping can solve it. If you scrape results, you can go with wide-open searches (on either Google or Recruiter) and filter results further in Excel. I believe that scraping has become a must-have skill for anyone who sources.

There is no perfect tool for the X-Ray for my “education” case since I need to scrape “under” the links, i.e., go to each profile in the search results and copy the Education section. (We used to have Ally from include.io, and I miss it dearly every day.) It should be doable with Data Miner, but I am not a fan of the UI. Those of you who write scripts are at an advantage!

Сорсинг на LinkedIn – знаете ли вы, что…

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This post if for my Russian-speaking subscribers. (Please feel free to translate if you do not read Russian.)

Без LinkedIn в рекрутинге не обойтись. Но поиск в нем весьма ограничен, а главное, пользователю часто непонятно, как именно работает алгоритм.

Знаете ли вы, что:

  • Поиск по Булевому выражению находит больше записей, чем по выбору (например, поиск по имени компании)?
  • В бесплатном и бизнес-аккаунтах можно переформатировать Булев запрос, так что он будет справляться с 12-15 терминами, а не 4-6, как обычно?
  • Больше половины компаний на LinkedIn не имеют размеров?
  • Какие преимущества у X-Ray по сравнению с бизнес-аккаунтом и LinkedIn Recruiter? (Если Вы имеете доступ к Рекрутеру, используете ли вы эти возможности?)
  • Если у коллеги есть доступ к Recruiter, а у вас нет, то коллега может улучшить привилегии для Вас.
  • В Рекрутере работают секретные операторы поиска (например, headline: и skills:)?
  • Можно без всякого кода собрать списки адресов разработчиков с Github, найдя их по “главному” языку и месту жительства, и )бесплатно) получить список их профилей на LinkedIn?
  • Какие Chrome-расширения лучше всего находят контакты, и какие – находят по списку (умеет, например, SalesQL)?

Записывайтесь на наш первый русскоязычный вебинар – Сорсинг на LinkedIn – 9 февраля 2022г. Участие включает запись, слайды и месяц поддержки. Количество мест ограничено.




Excellent Grades: a Sourcing Challenge

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Have you ever had a need to find LinkedIn members who have earned high grades at a University? It’s hard. No LinkedIn account, including Recruiter, will search for the entered grades. You can search for something like “cum laude” OR honors OR honours OR hons OR distinction (etc.), but, unless they have mentioned these in the summary or education description, LinkedIn will miss them.

I had hopes for the “Test Scores” section that some members use – but, upon testing, found that LinkedIn misses it as well!

My current project is to find students of Computer Science in several European countries. Let me share what I have observed.

While people use “cum laude” or GPA internationally, each country has its own grade system. I Googled for university grades <country name> (example) to find additional terms. In Germany, it is a number between 1 and2 (1, 1.1., 1.2, etc.); in Italy, 110/110.  (But if it is “5” or “A”, the terms did not help much due to multiple false positives). Then, I searched on LinkedIn for a long OR of the “excellence” keywords to find members who did put them outside of the University grades and test scores.

To find the missed ones, I went to X-Ray: Google searches for all words on profiles, including University grades. I used all the previously found good grades words. But the most effective term that worked internationally, was “GPA * *”, for example,

site:ro.linkedin.com/in “GPA * *” “computer science” student

It is easy to pick the high GPA profiles from the search. If there were lots of results, I scraped them with Instant Data Scraper first and filtered:

Another approach that finds those whom LinkedIn does not, is using the AROUND operator, for example,

site:uk.linkedin.com/in Bsc AROUND(3) hons “computer science”.

(Note: if you want to be found for high grades, add them in the summary.)

Finding current students was another challenge. I will write about it in a future post.

Refresh your X-Ray skills by taking the Advanced X-Ray class.





A Few Words About Yandex

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Yandex.com is the third major global English search engine (it searches – obviously – in Russian – and other languages as well).

As I am preparing the 6th (!) edition of the “300 Best Boolean Strings” ebook, I am reviewing the chapter on Yandex. Let me share it with you.

Here is the Syntax Description for Yandex.

Like other search engines, Yandex supports the site: operator and relies on quotation marks (“”) to indicate a phrase. This search works on Yandex:

site:linkedin.com/in “Greater Philadelphia Area” “Inside Sales Representative”.

Alas, Yandex has indexed very few LinkedIn profiles.

Yandex has a whole array of X-Ray operators.

To search for files of a specific type on Yandex, use the mime: operator:

mime:xlsx “new york” “vice president of” “supply chain” phone email

Yandex has some unique search operators as well. Here are some Yandex search abilities that stand out.

The exclamation mark preceding a word tells Yandex not to modify the word:

buy !apples wholesale

The plus in front of a “stop” (i.e., insignificant) word makes sure the word is included.

what to do +if the computer shuts down

The square brackets tell Yandex to find the words inside them in a particular order:

tickets [from london to paris] – in the results, London will always precede Paris.

The operator lang: narrows results to pages in the language. Use two-letter language abbreviations: passport lang:en.

You can select a region, sort by date, or narrow to a recent date range if, after searching, you press the “advanced search” icon. Yandex has search operators for the date range as well.

Search settings allow entering several “preferred” sites, which will rank high (but not much else).

Unfortunately, Yandex has lost its specific proximity operators, likely, for the lack of usage. But the Asterisk works similarly to Google’s.

I recommend using Yandex for image search and reverse image search – currently, results are better than in Google or Bing. When you run reverse image search using someone’s photo, Yandex will involve facial recognition! It works even better if you run it from a Russian IP address.


The Best of 2021

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Thank you for reading my blog! 1,80000 views and counting.

1,600 Recruiters took The CDSP Sourcing Certification Program for Recruiters and Teams. See feedback.

200 researchers bought the one and only book on CSEs – Custom Search – Discover more: 1st Edition. It took a year and 1/2 to write.

LinkedIn Operators work in LinkedIn Recruiter Job Title or Company Links (thanks Aaron Lintz!)

The last and most read 20 LinkedIn Profile X-ray Strings for 2022. Our cat R2D2 messed up with it and now wants to have his own blog!

Github – Tech Recruiters Paradise sponsored by AmazingHiring.

X-Ray webinar recording.

Most read post ever – Hidden Profiles

8K+ people joined the FB Boolean Strings Group

I am open to sourcing projects(10K+ views).

Follow Cyb_detective. #OSINT

Email Collector

Watched “New Tricks” and “Line of Duty”. British TV is awesome!

Happy sourcing!



20 LinkedIn Profile X-ray Strings for 2022

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By current job title:

site:linkedin.com/in intitle:”salesforce consultant”

By current company:

site:linkedin.com/in intitle:oracle

By any company:

site:linkedin.com/in “amazon graphic”

Company past not present

site:linkedin.com/in “ibm graphic” -intitle::ibm 

Group member:

site:linkedin.com/in “sourcing summit graphic”


site:linkedin.com/in “cissp graphic”


site:linkedin.com/in “american hospital association graphic”


site:linkedin.com/in “Lomonosov Moscow State University (MSU) graphic”


site:linkedin.com/in “recommendations given”

Presence of “Honors and Awards”

site:linkedin.com/in intext:”honors and awards”

Good grades:

site:linkedin.com/in “cum laude” OR hons OR honours OR honors OR “first class” OR “1st class” OR bien OR “2:1”

First and last name:

mary AROUND(2) jones site:linkedin.com/in

Current location:

“new orleans” AROUND(5) connections site:linkedin.com/in

Job location for service providers

site:linkedin.com “work location * san francisco bay area”

Public Gmail address

site:linkedin.com/in “gmail.com”

Job title at a past company:

site:linkedin.com/in “chief * officer” AROUND(4) microsoft -intitle:chief -intitle:microsoft.

Service providers

site:linkedin.com/in “work preference”


“I accept direct messages and business inquiries by anyone on LinkedIn for free even if we’re not connected.”

People recommended by Donna site:linkedIn.com/in “Click here to view Donna Svei Executive Resume Writer’s profile”


site:linkedin.com/in 🌈

Scrape X-Ray for Research Including Diversity

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I have a demanding client who keeps sending me challenging sourcing requests, needing more than Boolean Search in LinkedIn Recruiter can achieve. (I love those.) Her last three requests have been – all in Europe – to find:

  1. Frontend Developers who have LinkedIn recommendations.
  2. Backend Developers who used to work as Research Assistants and have good grades (like “cum laude”).
  3. Software Engineers with under ten years of experience who are Diversity (not just women, she insisted).

The expectation was to get lists with hundreds of profiles. I was able to deliver, but it involved a lot of head-scratching.

You cannot search for Recommendations with any LinkedIn account. You cannot look for school grades. If you search for an OR or “good grades” terms, you will only find a small portion of those who have put it elsewhere on the profiles (good for them!). And, LinkedIn does nothing to help search for Diversity.

Since many diverse categories represent the minority of professionals in the desired professions, just searching on LinkedIn and screening the results for Diversity is too time-consuming. I had to go with X-Ray.

So, for Task 1, I X-Ray LinkedIn, patiently, by country, for the words “recommendations received” to be there. I scrape results with Instant Data Scraper and filter out false positives. Now I have a list of promising LinkedIn URLs. A way to go is to paste the list to SalesQL‘s (brilliant!) upload function. Its export features more fields from profiles than I have seen anywhere else. (An alternative is Phantombuster.) As a result, I have a rich Excel file, which I clean up, sort, filter, and select my prospects, whom I can email. Because of this additional step, my search strings can be imprecise, and I get results which won’t surface in a tighter search.

Task 2, same story with various “cum laude” words.

Task 3 is more complicated because I need to find “Diversity Indicators” for each type of Diversity. But the principle is the same. I start with researching Diversity Identifiers such as women’s names, relevant organizations, diversity schools, etc. Note that simple scraping helps here as well. I can ask Google questions and get lists of terms as Featured Snippets. Then I use an OR of the terms both on Google and LinkedIn.

Another type of sourcing to be done with scraping is searching for job stability, sadly, never offered by any sites.

It is time to update your scraping skills! Join me for the upcoming class Web Scraping For Recruiters on January 5th, 2022.