Google and LinkedIn Speak Different Boolean

booleanstrings Boolean, Google, LinkedIn 1 Comment

Google and LinkedIn are two sites where sourcers spend most of their time. Both support Boolean search. Yet it works in very different ways.

For starters,

The search is not really Boolean (we can call it pseudo-Boolean).

Google finds synonyms to all terms entered without quotation marks. A search like backend java engineer -engineer returns results while “formally” it should not.

The order of words matters, too; Google attempts to interpret words following each other as phrases. Searching for a sentence from a public page even without the quotes will return that page on top.

Google also attunes the results on the perceived intent of the searcher based on the whole string. That is the definition of semantic search. Apple pie and Apple salary will show the results you expect.

If you want to control the interpretation, you can put keywords and phrases in quotes or use the Verbatim option. But in many cases, Google’s semantic features are trustworthy.

The same search on LinkedIn in Keywords also (unexpectedly) produces results: backend java engineer -engineer. When the Keywords contain a job-title-sounding sequence of words, LinkedIn interprets it as a title past or present and looks for similar titles. Unfortunately, LinkedIn’s interpretation may not match ours – and it is even less intuitive in Recruiter. For example, LinkedIn “thinks” that an Executive Assistant to CEO and CEO are “similar.”

So LinkedIn may add synonyms, but you can’t rely on it to do so as well as Google. LinkedIn Recruiter offers to select titles or sets of titles – but LinkedIn’s standardized values for titles, as well as companies, schools, skills, etc., are limited.

On LinkedIn, you want to avoid the interpretation and control the search using as many synonyms as you can come up with – sr OR snr OR senior, developer OR engineer OR coder, etc.

Choose to enter search strings vs. standardized selections. Text searches will perform better and are easier to adjust. Use our Boolean Builder tool to create LinkedIn-friendly strings (make a copy of the document).

Bottom line:

  • On Google, search simply.
  • On LinkedIn, use long Boolean search strings to cover every possibility in every field.

Are you guilty of using only LinkedIn when you search for potential candidates? Join us for the class Sourcing without LinkedIn – coming up shortly!




Diversity Filter Coverage – Women’s Names

booleanstrings Boolean, Diversity 2 Comments

Diversity Sourcing is not easy. There is no clear way to search for diversity categories on social sites or Google. Our less-than-perfect but necessary approach consists of “shortcuts” – ways to search that are likely to bring up groups of potential diversity candidates. As an example, Jonathan Kidder has a collection of diversity Boolean strings. Glen Cathey’s blog offers quite a few approaches. (In addition to using the shortcuts, we sift through “everyone” in professional search results and notice other profiles of interest.)

It makes sense to combine all possible “shortcuts” – such as the search for female names, associations, schools, etc., for the most inclusive approach. It also makes sense to get an idea of how large a population every approach covers.

Here is some research on finding professional women by the common first names in the US. (For other countries, this exact approach may or may not be possible – I am sharing it as an example).

First, I got a list of 1,000 most common female names in the US by Googling. I also found lists from the Social Security Baby Names page. In the end, the results for the names from both sources were similar. (But the popularity of the names has varied over the years.)

I did the research on LinkedIn, so it is affected by the data LinkedIn has – but it is LinkedIn that we use to search, so these conclusions are not affected by people (many!) who are not members.

I created a 1,000-long OR name string and tried it in LinkedIn Recruiter. While it was “too much” for it to search, it did show the numbers.

In the US, LinkedIn has 49% female members (compared to 43% worldwide) and a total of 170M+ members, i.e., it has 83M+ women. Our OR name search has found 73M+ results. So, a search for 1,000 common names (which you would have to do in portions to get results) amounts to 87% of women in the US. (If you were wondering about 1,000 common men’s names, the percentage is even higher.)

The results are affected by LinkedIn’s first names interpretation, which cannot be turned off – we cannot search “verbatim” (either in Recruiter or business account). It affects our results in good ways since we will see also nicknames and variations. However, some names (like “Andrea”) can be both men’s and women’s, and LinkedIn’s variations will include “Jerry” and “Gerry” if you search for “Geraldine”. That introduces false positives. But, examining results by adding extra filters, I can tell that the percentage of those is small. (We look at each result when sourcing anyway.)

There are name variations across locations. Here in the San Francisco Bay Area, the population is diverse ethnically, which results in fewer “American” names found – more like 50%, but it is still a high percentage. (To increase it, we can add lists of ethnic names). If you narrow to industry – for example, Software – the numbers will go down – but it only reflects the uneven numbers of men and women in the industry.

If you compare the high percentage we got with numbers for other filters (such as “she” or “her”, schools, and  memberships), it becomes clear that name search is powerful. The conclusion is – for the US, female name search is an excellent filter. Just make sure you include 1,000 and search in other ways as well.

Is your team sourcing for Diversity? Join us for the Certified Diversity Sourcing Professional (CDSP) Program August 2021! (June is now sold out.)





LinkedIn Search Solved

booleanstrings Boolean, LinkedIn 3 Comments

Searching for professionals on At this time, business and personal users have the most powerful – but not officially documented – search ways and filters and cross-referencing ability, exceeding (the expensive) Recruiter’s.

You can search for unique filters such as headlines and self-entered skills, and combine other filters such as company size, type, years of experience, or at school in Boolean expressions. The Boolean “limitations” can be overcome with modified search syntax. You can upload and cross-reference up to 10K (!) email addresses vs. Recruiter’s 200.

The extra sourcing power is not documented in LinkedIn’s Help.

Take a look at this comparison and join our webinar to learn all about LinkedIn Sourcing.

Search Filter/Account Type Basic or Business RPS and LinkedIn Recruiter
First/Last Name X X
Network Relationship X X
Industry X X
Headline X*
Current Title (Boolean) X X
Current Company (Boolean) X X
Current Company (Checkbox) X X
Past Company (Boolean) X
Past Company (Checkbox) X X
Years at Current Position/Company X
Years of Experience X* X
Company Size X* X
Company Type X* X
School (Boolean) X
School (Checkbox) X
Years of Study X* X
Field of Study X* X
Degree X
Profile Language X X
Spoken Language X* X
Self-entered Skills X*
Calculated Skills X
My Groups X* X
All Groups X
Location (by place name) X X
Location (zip, radius) X
Connections of X
Seniority X* X
Job Function X* X
Recently Joined X

* use the hidden search operators

Search for Group Members (to Message)

booleanstrings Boolean Leave a Comment

LinkedIn used to limit messages to your Group members to 15 per month. This restriction is gone. If you have a basic or business account, you can message fellow group members without restrictions.

However, Group member search only offers finding people by name. How do you find group members who match a professional requirement?

Here is a way I came up with. You can do an advanced LinkedIn people search for your filters. One of the filters is “connections”, 1st, 2nd, and 3rd level. There is no option to search for group members. To do so, all you need is adding &network=[“A”] to the end of the search URL. Add it after you have searched for other values – searching for your group members at start will break the other filters.

Here is an example search with some filters that would show people you can message – 1st connections and group members.

(BTW, if you want to see a “clean” URL, use a URL decoding tool like this. The above search URL will become more readable, like this:[“1441”]&geoUrn=[“102095887”]&network=[“F”,”A”]&title=manager. Here, &network=[“F”,”A”] stands for the first level connections and group members.)

There is a number of clicks to send a message to someone in the search results. If you go to their profile, you will see groups in common, then you can go there, find the person, and message.

But it beats InMails because it is free.

Combined with the search operators, it makes a business account an excellent option for sourcing. You can also learn to find members’ contact information in the upcoming class Find Anyone’s Contact Info on May 13, 2021.

Be Negative. Find More

booleanstrings Boolean 1 Comment

Can being “negative” help in sourcing? I do not mean to suggest that you will source better results when you are in a bad mood, voice dissatisfaction, or upset others. This is an essay on the Boolean “NOT” logic.

When searching, the productive approach we teach is to imagine the “right” terms you will find and put those terms and variations into the search field(s). We call it “visualize success.”

But many social network members do not follow standards in entering their profile data and forget to include “our” keywords. A Boolean “NOT campaign” is a way to dig deeper and find them.

Search, negating some seemingly necessary keywords or titles, and see which other relevant terms and results show up. Use the newly found terms to iterate the search. This approach discovers members who have not used the “right” keywords on the profiles but are worth reviewing.

For example, you might be struggling to locate a “purple squirrel” with rare coding skills and the title developer OR engineer. Try, in addition, searching for NOT developer NOT engineer and the skills. You will be finding people with the titles lead, coder, technical staff, abbreviations like MTS, etc., some “creative,” and even misspelled titles. (The latter helps if you decide not to hold misspellings against potential candidates.)

The following example search is for two obscure programming languages. Given the scarcity of the talent pool, it may help to search like this:

(malbolge OR lolcode) (NOT title:(developer OR engineer))

The results of such a search will likely include profiles that your competition will miss. (Companies that name their employees in non-standard ways like Technical Yahoo do a good job of protecting them from being sourced!)

Iterate. If most results come from several companies, exclude those companies. Or, if most people with the skills reside in a few locations, exclude them and search again:

aws architect NOT geo:”san francisco” NOT geo:”new york”

NOTs are also necessary for exploratory research, the initial and ongoing part of every sourcing project, and one of the six core skills we test. For example:

  • By negating the desired title, you will find other possible titles that you can use in your searches.
  • By negating the desired skill, you will find people with comparable skills and learn more terminology.
  • By negating the seniority (excluding directors and managers) but using words pointing to someone in charge – as simple as managed or – “in charge” – you will find others. And you will learn how managers call themselves in some cases – some companies have their own “sets” of job titles.
  • You will also find out what the market is like.

The above applies to LinkedIn and LinkedIn Recruiter. But NOTs also help in Googling and to find sites to X-Ray in particular. If you are Googling for sites to source from, search for the terms and start excluding sites appearing in the results one by one, like, etc. You will find useful, targeted sites to source from. Dan Russell of Google likes to write about this. A search engine Millionshort does this type of “thinking” showing results you may not ever see unless you make an effort.



Are 35% of US LinkedIn Members Unemployed? Globally, 320 MLN?

booleanstrings Boolean, LinkedIn Leave a Comment

It has occurred to me that the operators not only allow to expand the searching power in “positive ways” but also make it possible to search for the absence of some values. To search for a field not to have any value, we need to know all possible values – and this is true for the “seniority” filter. The seniority codes range from 1 to 10.

We know that people without current jobs do not have a seniority value assigned. Therefore, by excluding all the 10 levels, we should find all unemployed. (People without a job are like “dead souls“). Here is the search for all of them.

All unemployed LinkedIn Members – (, or

NOT(seniority:1 OR(seniority:2) OR(seniority:3) OR(seniority:4) OR(seniority:5) OR(seniority:6) OR(seniority:7) OR(seniority:8) OR(seniority:9) OR(seniority:10))

Right now, the total is 320 MLN. For the US, it is 60K+ unemployed vs. a total of 170 MLN. This means that 35% of American LinkedIn members do not list a current job. Globally, LinkedIn has 44% unemployed members.

Really? The numbers seem way too high, especially as the economy recovers. Surely, there are abandoned profiles but they usually have a “current” position. There must be employed members who are “false positives”. Adding extra filters (example) was showing nice, matching results.

Digging deeper, I have realized that LinkedIn also does not assign seniority to people whose titles it fails to interpret. This is what is responsible for the large numbers! What it means is that 1) there is a lot of “junk” data and 2) LinkedIn can do better at recognizing the non-standard job titles of its members as well. (It is a good reminder to not fully depend on LinkedIn’s selection search for any facet.) The search does find all unemployed but may pull some “false positives” as well, depending on your other keywords.

Please make a note of it. Recruiters, search for the unemployed while the operators allow! Let’s help them find jobs.

An in-depth webinar about mastering the operators and ways to source like a genius on LinkedIn is on its way (will appear on this page). If you have a Recruiter subscription, join this session (which will be just as informative).

The Power of the Hidden Operators

booleanstrings Boolean 2 Comments

At this time, we have the best free LinkedIn search ever available, surpassing even Recruiter’s in several ways. Searching for skills, exact location, spoken language, fields of study, years of experience, and at school, are welcome (unplanned) additions to the search. The never-documented LinkedIn search operators offer filters that are otherwise paid – or are not offered at all – such as the filter for the headline:

headline:”open to work”.

The searching power of the operators goes beyond utilizing the previously unavailable filters because you can combine the hidden operators and Boolean logic.


Operators allow you to search for members who do NOT have a particular value for a field. For example, you can search for a Developer NOT in the Gaming industry, or a member who has your keywords but is NOT in HR, or a member who speaks Italian and lives outside of Italy:

(NOT geo:italy) spokenlanguage:italian

Boolean Combinations

Since the operators bring back the search from the boxes in the advanced dialog into one string, you can use the Boolean logic unavailable in the faceted dialog. Example:

((NOT company:bank) title:manager) OR (company:bank title:”vice president”)).

Can you still benefit from LinkedIn Recruiter? Yes, though the new version has much weaker functionality than the one before it. Come to the upcoming webinar to learn

how to use Recruiter masterfully!

How to Verify Email Guesses on the Professional Network

booleanstrings Boolean, Hack Leave a Comment

“Which tools are best at finding emails”? – seems to be every other question from Recruiters on Facebook groups, always triggering multiple answers. But here is an approach that does not require any Chrome Extensions or other email-finding tools.

If you are looking at someone’s LinkedIn profile or just know someone’s full name and the company name, you can start guessing their email address, either work or private. For example, for a Jason Smith at Amazon, his email may be:

[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]


There are email permutators out there that can give you more suggestions.

And now, you can find out which email is the right one! There’s no need for a paid LinkedIn account. Follow the steps; pause a little between them.

STEP ONE. Check whether you have imported data. If you do, remove it.

STEP TWO. Create a CSV file in the following format:

first last email
jason smith [email protected]
jason smith [email protected]
jason smith [email protected]
jason smith [email protected]
jason smith [email protected]
jason smith [email protected]
jason smith [email protected]

STEP THREE. Upload the file.


Now, in the imported contacts, see the results. The emails associated with LinkedIn accounts bring up the profiles (the first and last name in the input does not matter). The emails that are not associated with profiles show up as the last name from the input file, in our case, “smith”.

What’s (quietly) new and beautiful is that you can see which email points to which profile. That would tell you the correct email address for the person in question! Click on a profile, see the contact:

Here is a twist to the above. If you have a list of email addresses and want to quickly check which ones were used on LinkedIn, here’s a hack variation. Replace the last name by the email in the input:

first last email
jason [email protected] [email protected]
jason [email protected] [email protected]
jason [email protected] [email protected]
jason [email protected] [email protected]
jason [email protected] [email protected]
jason [email protected] [email protected]
jason [email protected] [email protected]

The result will show the email addresses NOT on LinkedIn. Use an Excel function to find those that do point to profiles.

LinkedIn Recruiter, in theory, has an import function, but it has been broken for a while with no estimated fix date. (Recruiter won’t even upload LinkedIn’s own example file). But, if it is fixed, you are still limited to 200 records now. Yet you can upload a thousand or two addresses and see the matches with the free personal contact input, just as described above.

We’ll talk about LinkedIn Recruiter Mastery at our upcoming class on April 21st (Wednesday) – and it includes working outside of the platform to compensate for its deficiencies.


What You Are Missing (in Recruiter)

booleanstrings Boolean Leave a Comment

Are you struggling to find more matching potential candidates in LinkedIn Recruiter? The reason may be that you are using some search fields that restrict your results without your knowledge.

I see two reasons for the search algorithm to challenge us:

  1. The original, kept in place, profile data design does not work well with the actual data that members enter, and poor search follows.
  2. LinkedIn’s “semantic” interpretation of our keywords has ways to go (to put it mildly).

Here are some tips on search variations to expand your coverage in Recruiter.

Company Size

Apparently, about 50% of LinkedIn members do not have an associated company size. (No, it is not an April Fool’s joke.) It is easy to check: search for “all members” with each company size selected, and you will see the results go down fifty percent. (Ask me or David Galley if you want to know how it happens.) If you are searching by company size, you are excluding half of LinkedIn!

Therefore, drop the company size filter from some of your searches, and you will find extra results.

Company Name (Boolean) vs. Company Selection

Often, recruiters have a preference for searching either one way or the other. But to be thorough, it is best to use both. The results will overlap but differ. If in a hurry, use Boolean.

Job Title (Boolean) vs. Job Title Selection

LinkedIn’s idea of “similar” job titles does not reflect the reality in so many cases. So it is best to use Boolean (and be imaginative and thorough when writing your “OR” statements).

For “selections,” LinkedIn Recruiter may bring in “similar terms” that you do not wish to see and miss those you do. But if you have the time, search by selections, too, to possibly see different results.

The difference between the “Boolean” vs. “selections” options to search in all the fields where it is offered is not subtle – results are quite different.

Finding the Unemployed

Another warning: If you select the “function” or “seniority,” you will miss people without current jobs. Company names, types, sizes, years at the company and in position will do the same.

There is more to be said about the Recruiter search that is absent in the help documentation!

We have just announce a LinkedIn Recruiter Mastery webinar for Wednesday, April 21st. Don’t miss it!

Enjoy the Operators While They Last #OSINT

booleanstrings Boolean, OSINT 11 Comments


The LinkedIn hidden search operators are back! Nobody knows for how long they will work this time (we enjoyed them for a year and a half a while ago). But they offer any LinkedIn user, whether basic or paid, significant searching power and an important filter unavailable with any subscription.

LinkedIn never documented the operators, apart from the less-useful firstname:, lastname:, title:, company:, and school: (all achievable via the advanced dialog). Nobody has any idea who had implemented the other ones. But the code came alive again.

The operators were featured in Nathan Palin’s Bellingcat’s Invitation Is Waiting For Your Response: An Investigative Guide To LinkedIn.

You will find some again-working search examples earlier on my blog, posted when I was discovering the operators. Here are more examples:

You can combine the operators and any search terms.

It very well may be that there are other operators to be discovered. Profiles have tons of data, of which we can search only part. But we can search better while the discovered operators last. 🙂

Please check out our class on some OSINT advanced (often, also hidden) features of LinkedIn, Google, and Custom Search Engines –

Advanced Google and LinkedIn for #OSINT Research.