Tag Archives: Boolean Search Strings

Using Google to Search for People in Specific LinkedIn Groups

In this post, I am going to share with you the journey I took and the discoveries I made while investigating the answer to a Boolean search request for help I recently came across online about using -dir in a Google X-Ray search of LinkedIn. Some of you may enjoy and appreciate seeing my methodology, others will likely learn a thing or two about using Google to search for people in specific LinkedIn groups, and I’ll remind you of a few reasons why you can’t find everyone on LinkedIn using Google, Bing or any search engine other than LinkedIn’s.

Here’s the original search that was shared in the request for help:

site:linkedin.com “Front end developers group” (.Net |dot Net) Greater Boston Area) -dir -job -jobs -sample -samples -template -resume service -resume writers -resume writing

I was going to quickly answer with a cleaned-up search string, but what really caught my attention was that he was trying to target folks in a specific LinkedIn group.

Now, I have a LinkedIn Recruiter license and I don’t often X-Ray LinkedIn to find specific group members, so I poked around a bit on the topic and found this little gem posted by Balazs, my former partner in world sourcing domination, back in 2011:

Here’s the sample string Balazs offered: site:linkedin.com inurl:(in | pub) “logo Boolean strings” -inurl:dir

I used his search and noticed the total number of results  was very low (only 2 pages) and also that there were many false positive, non-profile results. It was clear that much has changed since Balazs wrote the above post nearly 3 years ago and that, among other things, “logo GROUP NAME” no longer works as it once used to.

LinkedIn Group X-Ray search 1

As such, I decided to take a look into one of the actual profile results and view the cached version to see what Google was hitting on.

Cached view option

Then I right clicked to viewed the page’s source:

Here’s what I saw after using CTRL-F to search for the word “Boolean:”

You can see that the the logo image is followed by the phrase “Boolean Strings – the Internet Sourcing Community logo.”

Here’s the specific piece of code:

<img src=”http://m.c.lnkd.licdn.com/media/p/8/000/2be/00f/059184c.png” width=”60″ height=”30″ alt=”Boolean Strings – the Internet Sourcing Community logo” />

So, in order to leverage that specific format/order of words, I constructed a quick search targeting the LinkedIn group that the person from the original question that caught my eye was trying to target, which was the Front End Developers group, using “Front End Developers Group logo” in the string:

site:linkedin.com “front end developers logo” (C# |.Net) “location * Greater Boston Area” 

Google estimates 83 results:

However, you should never pay attention to Google’s “About XX” results. If you click to page 2, you can see there are really only 11 results.

I then decided to check LinkedIn Recruiter looking for people who live in the Greater Boston Area and either mention C# or .Net, and, using LinkedIn’s brilliant Any Group functionality, I searched for members of the Front End Developers Group.

Surprise, surprise – I got 11 results:

However, while the number matches between Google and LinkedIn, the people do not.

Interesting, yes?

That opens up a whole different can of worms, so to speak.

Google is not an All Seeing Eye

Keep in mind that people have the option to not show the logo of a group they’re in on their LinkedIn profile.

LinkedIn Group Settings Visibility

If someone chooses not to display the group logo on their profile, then you won’t be able to use Google to find people in the manner demonstrated above presumably because the logo (and associated logo phrase) won’t be on their public profile. If it’s not there to be “seen” by Google, you can’t retrieve it.

Additionally, let’s not forget that:

1. Some people’s LinkedIn profiles are invisible to search engines which means you can’t X-Ray search them. People can choose to make their public profile “visible to no one” (see the image below for LinkedIn public profile content settings), meaning their profiles are not crawlable/indexable by search engines and thus cannot be retrieved.

2. Even if people choose to make their public profile “visible to everyone,” if they select “Basics,”  the only things that LinkedIn allows search engines to “see” are a person’s name, industry, location and number of recommendations. That means you can’t retrieve their profiles if you search for anything beyond their name, industry, and location. These folks could actually display group logos on their profiles, but searching for group logos won’t retrieve them because LinkedIn isn’t allowing search engines to “see” them.

3. Even if someone chooses to make their public profile visible to everyone and they don’t select “Basics,” they can still pick and choose from a long list of things that can or cannot be “seen” by search engines. Anything a person decides to not make visible can’t be found/retrieved with Google, Bing, etc., – this can include groups, skills, summaries, current and past positions, and more.

Another Way to X-Ray Search for and Target LinkedIn Groups

While writing this post, I stumbled across a specific question about how to use a Boolean search in Google to target members of a specific LinkedIn group. Lois Grimshaw responded to the question, and I noticed that she took a different approach than using the “GROUP NAME logo” phrase – she used “logo * GROUP NAME.” I added java to the search to get the results down to a more manageable number for testing:

site:linkedin.com “people you know” “logo * Ernst & Young Employees and Alumni” java

I scrolled to the last page of results to see that Google returned 461 results:

Googe X-Ray LinkedIn Group

I then decided to run the same search, but use my “GROUP NAME logo” phrase format, shortening it to “Young Employees and Alumni logo,” because there isn’t any other group that uses the same specific phrase, there is actually no need to include “Ernst &.” I also happen to be a fan of minimalist strings.

site:linkedin.com “people you know” “Young Employees and Alumni logo” java

That search returned more results – 496.

Google X-Ray LinkedIn Group

In case you were curious, I decided to also test this search format:

site:linkedin.com “people you know” “Ernst * Young Employees and Alumni logo” java – 480 results.

I then decided to check on LinkedIn using my Recruiter license – 735 results.

For the many reasons I listed above, I wasn’t surprised to find significantly more people using LinkedIn’s search than I did using Google.

Group Search: Asterisk vs. Straight Phrase

I decided to go back to my original Front End Developers group search and use Lois’s “logo * GROUP NAME” search approach to see how the results differed from my simple “GROUP NAME logo” phrase search:

site:linkedin.com “logo * front end developers” (C# |.Net) “location * Greater Boston Area” 

If you got to page 2, you can see that the search returns a total of 15 results, which is 4 more than my original search.

Works better, right?

Not so fast – in this case, using the asterisk allowed additional groups to get returned, such as the Chicago Front-End Web Developers group…

LI Group Logo 1

…and the Front End Web Developers – CA group.

LinkedIn Group Logo 2

One could argue that scooping up some additional groups is a good thing, and in this case, that is actually true (both folks live in Boston even though the groups are for locations other than Boston).

However, it’s easy to see that using the asterisk in a group-targeting search can have unintended consequences, and it’s not difficult to imagine scenarios in which irrelevant results could be returned.

Unique LinkedIn Group Name Search

Lastly, I’d like to point out that if the name of the LinkedIn group you’re interested in searching for is unique, you may not even need to use the word “logo” in your search.

For example – all 3 of these searches return 27 pages of results:

No mention of logo:

site:linkedin.com “boolean strings – the Internet Sourcing Community” “location * greater atlanta” “people you know” – 27 pages of results


site:linkedin.com “boolean strings – the Internet Sourcing Community logo” “location * greater atlanta” “people you know” – 27 pages of results

“logo * GROUP NAME”

site:linkedin.com “logo * boolean strings – the Internet Sourcing Community” “location * greater atlanta” “people you know”27 pages of results

The Boolean Search Bottom Line(s)

1. Be curious! Don’t just copy, paste and implicitly trust other people’s search strings – take the time to tinker with them to understand why and how they work (or don’t!), and to improve upon/simply them.

2. Don’t pay any attention to an Internet search engine’s estimate of the number of results on page 1- always navigate to the last page of results to get the real number, especially when comparing alternative search strings.

3. Always, always, always inspect search results below the surface and look for patterns to make sure your searches are working precisely the way you intended. If they’re not, it’s an excellent opportunity to learn by tweaking your searches and watching how the volume of your results varies and how your results get more or less relevant, and specifically why.

4. There are often many different ways of achieving the same search / information retrieval / sourcing goals – very seldom are Boolean search strings “right” or “wrong,” although there can be a wide variance in the volume, relevance and inclusiveness between seemingly similar searches.

5. The “GROUP NAME logo” phrase search works well for using Google to search for people within specific LinkedIn groups, and for unique group names, you don’t even need to use the word “logo” in your search.

6. You can’t find everyone on LinkedIn using Google, Bing or any search engine other than LinkedIn’s. One could argue that perhaps some of the best people on LinkedIn are unfindable via X-Ray search because, as highly sought after passive talent, they’ve taken steps to limit what, if anything, Internet search engines can “see.”


LinkedIn Sourcing Ninja Webinar Recording now on YouTube


In case you missed my record-setting LinkedIn sourcing webinar on 6/4 (3,000+ attendees!), the fine folks at LinkedIn recorded the whole session and have graciously uploaded the presentation to YouTube, where you can find the Become a Sourcing Ninja: Earn your Boolean Black Belt with Glen Cathey video.



Be sure to change the quality to 720 for the best viewing experience.

Content covered includes:

  • Boolean search operators and query modifiers supported by LinkedIn
  • Beyond Boolean – asking better questions
  • Human-Computer Information Retrieval (HCIR)
  • Hidden Talent Pools
  • Diversity sourcing (gender demonstrated)
  • Agile Sourcing Methodology
  • Probabilisitic and Exhaustive Sourcing
  • Sourcing Capability Maturity Model
  • LinkedIn Signal
  • How to automatically find people who have just joined LinkedIn


Happy hunting!


How to Find People to Recruit on Twitter with Google & Bing


With over 200 million active Twitter users, you cannot and should not ignore Twitter for sourcing and recruiting talent. Here's how to find the people you need on Twitter using good old fashioned X-Ray search via Google and BingThere are over 500 million Twitter accounts with over 200 million represent active users globally. I’d say that qualifies it as a solid source for finding and engaging talent for recruitment.

Of course, you can’t engage someone you haven’t found in the first place, and it’s been far too long since I’ve posted an update to how to search Twitter to find people – can you believe it’s been 4 years?!?

It was just the other day that I was hacking around on Google and Bing trying to find people on Twitter based on the text in their bio’s (yes, I am familiar with Follwerwonk – you’ll see why I prefer Google/Bing in a moment) and while I was getting some results, I wasn’t getting as many as I thought I should, nor were the results as “clean” as I would like.

That led me to a few minutes of tinkering with Bing and Google and I made a few discoveries with some simple pattern recognition that I would like to share that will help you quickly find your target talent pool on Twitter.

I use two main examples – mechanical engineers in South Africa and software engineers in Chicago – you can of course fork my Boolean strings to suit your specific sourcing needs replacing my titles and locations with yours.

How to X-Ray Search Twitter with Bing

While I do search for what people tweet about, I prefer to search for information contained in Twitter bio’s/profile summaries where people often identify themselves by what they do for a living (e.g., software engineering, accounting, etc.).

Twitter bio example

Furthermore, I prefer to search for bio data using Google and Bing, as there is no service/app I am aware of that indexes as many Twitter profiles as the 2 search engine titans. When I was using Bing to search for Twitter profiles the other day, I was looking for patterns in the results that were consistent across my desired results (actual Twitter profiles) and not my undesired results (Tweets and jobs/job posting-only accounts),

I noticed that Twitter profiles all mentioned “followers,” “tweets” and “following.”

Twitter Bing 2 

I simply added “tweets” to a basic X-Ray search of Twitter and a little bit of magic happened. For example: site:twitter.com tweets “south africa” “mechanical engineer” 

Twitter Bing 7

Here is an example of a positive hit in the search results:

Twitter bio example South Africa Mechanical Engineer

Getting back to the Bing search results – you probably noticed the top 3 results were for “jobs” accounts.

I did too.

I tried adding a simple -jobs to the string and for some reason it kills the search and returns 0 results.

Then I noticed that many of the job posting accounts have “jobs” in the title lines, so I simply added -intitle:jobs to the string.

site:twitter.com tweets “south africa” “mechanical engineer” -intitle:jobs

As you can see below, only 1 job posting account was able to sneak in – the rest are profiles of people.

Twitter Bing X-Ray Search mehanical engineers in South Africa

Simply overlooking the job spewing Twitter profiles is easy – I often advise people that an acceptable percentage of false positives is fine with any search. Trying to “over cleanse” results can have undesired consequences, such as eliminating valid results.

Always remember – every search you run both includes AND excludes qualified people/desired results.  Think before you tweak!

So how many results would Follerwonk return in a Twitter bio search for mechanical engineers in South Africa? 51 vs 88 for Bing.

FollowerWonk search mechanical engineer south africa 

While there are no doubt a few false positives in the Bing search, I didn’t have much trouble quickly finding people in the Bing search results that Followerwonk did NOT find.

This confirms my concern with any search app/service like Followerwonk – they simply don’t index as many Twitter profiles as the major search engines such as Bing or Google.

Feeling pretty good about what I had found using Bing to find mechanical engineers in South Africa, I tried searching for software engineers in a large U.S. city.

site:twitter.com tweets “software engineer” “Chicago” -intitle:jobs

As you can see, 6 out of 12 of the first page results are people, and most of the other Twitter accounts are for actual companies, not just job spamming accounts.

Twitter Bing X-Ray Search software engineers Chicago 1

Twitter Bing X-Ray Search software engineers Chicago 2

Moving to page 2 of the results, 100% of the results are individual profiles of software engineers. Sweet!

Twitter Bing X-Ray Search software engineers Chicago 3

Twitter Bing X-Ray Search software engineers Chicago 4 

How to X-Ray Search Twitter with Google

When I switched over to Google, I tried the same search I used on Bing:

site:twitter.com tweets “south africa” “mechanical engineer” -intitle:jobs

As you can see from just the first page of results, Google turns up more job posting accounts than Bing, which returned only 1 job posting account with the exact same search. Google only returned 4 real people in the results.

I find it interesting to see the differences between Google and Bing, especially when it comes to such a simple search!

Google X-Ray Search of Twitter for Mechanical Engineers in South Africa 1

I’ve been trying to tell people for years that Bing is a bit “cleaner” than Google with regard to searching sites like LinkedIn and Twitter. The results above offer further evidence to support my claim.

Anyhow, I looked at the results and noticed a pattern in the false positives (job spewing/non-people Twitter accounts) – most mentioned “status” or “statuses,” so I decided to exclude those terms from the URL’s.

site:twitter.com tweets “south africa” “mechanical engineer” -intitle:jobs -inurl:(status|statuses)

Much better, yes?

Moving on to the search for software engineers in Chicago, I went a little crazier and added a number of additional exclusions, as is often necessary with Google: site:twitter.com tweets “Chicago” “software engineer” -inurl:(search|favorites|status|statuses|jobs) -intitle:(job|jobs) -recruiter -HR -careers -job Only 1 sneaky job posting account was able to slip past this search:

Google X-Ray search of Twitter for Software engineers in Chicago

Final Thoughts

As you can see, Twitter search services like Followerwonk do a good job of making it easy to search for and find people on Twitter, but they don’t index as many Twitter profiles as the major search engines such as Google or Bing.

As such, if you’re only using Followerwonk or similar sites to find people on Twitter, you’re only finding some people – and certainly not all of the people that are actually on Twitter.

Also, when it comes to any information retrieval exercise, a little bit of pattern recognition goes a long way.

Hopefully I’ve provided you with at least a couple of new ways to search Twitter via Google and Bing to find people with specific skills/titles in your target locations while reducing false positive results. Grab these bits of Boolean and add your location and title/skills:


site:twitter.com tweets -intitle:jobs -recruiter [location] [keywords]


site:twitter.com tweets -inurl:(search|favorites|status|statuses|jobs) -intitle:(job|jobs) -recruiter -HR -careers -job [location] [keywords]

Of course, you should always be careful when searching social media/networking sites – especially Twitter. People can and do use non-standard terms to describe themselves and their locations. For example, here’s a project manager in “Chitown” that you can’t find by searching for “Chicago:”


Twitter Chitown nonstandard language location

Also, we’re lucky that this person took the time to explain what a “code sensei” is – if they didn’t make mention of “software engineer,” no one could find this person by searching for that title:

Example of non standard Twitter title software engineer cose sensei 

Imagine how many people describe themselves and their locations with non-standard terminology and you have a glimpse into the hidden talent pool waiting for you to explore on Twitter, Google Plus and other social networking sites.

Happy hunting!


Diversity Sourcing: Boolean Search Strings for LinkedIn



Note: I’ve updated this post as of August, 2015 with even more inclusive and effective diversity searches for LinkedIn.

When it comes to diversity sourcing and recruiting, the first thing that comes to mind for many people is posting jobs on diversity sites and in diverse groups. However, as I have written about many times, posting jobs is an intrinsically limited talent acquisition strategy and it fails to expose you to the “deep end” of the talent pool.

At best, posting jobs can only give you access to approximately 30% of the total talent pool – those active and casual job seekers who will actually take the time to run a search for jobs and apply to an opening.

How can you access the other 70%?

Proactive sourcing, of course!

I’ve spoken at a few conferences this year (HCI, LinkedIn Talent Connect, SourceCon) in which I’ve detailed some Boolean search strings for diversity sourcing on LinkedIn, and I’ve had several requests for the specific searches I’ve demonstrated.

While the search strings I’ve used in my presentations are already posted on the conference websites, I thought it would be a good idea to create and release some new and improved diversity sourcing search strings here for quick and easy access to some “starter” queries.

However, it’s important to know that what I’m publishing is the tip of the iceberg. I have no idea what your particular diversity sourcing need might be, or even what country you’re sourcing in – it’s up to you to adapt what you see here to your specific needs.

While I know some folks will be happy to simply snag the strings, what I really want my readers to get from this post is an understanding of and appreciation for the critical underlying thought process necessary for any successful sourcing endeavor, let alone diversity sourcing.

When it comes to information retrieval, if you can conceive it, you can almost always achieve it – including diversity sourcing – and there are often many different ways to achieving your search goals.

The “magic” of search strings does not lie in the Boolean logic or site specific search syntax, nor does it exist in the keywords and phrases you search for – the true power of search lies within your own mind.

What is Your Diversity Need?

So let’s get back to basics for just a second.

When you’re creating and executing Boolean search strings for talent discovery, you’re really performing information retrieval.

Information retrieval is the activity of obtaining information resources relevant to an information need.

An information retrieval process begins when you enter a query into an information system (e.g., databases, the Internet, social networks, etc.), and queries are simply formal statements of information needs.

So when it comes to diversity sourcing, what’s your information need?

This seems like such a simple question, but I honestly don’t think many people begin their sourcing efforts with this in mind.

Gender Diversity: Women

When it comes to gender diversity recruiting and sourcing, most people tend to think of searching for women’s groups, sororities, women-only sports, and women’s colleges, including searching explicitly for the words “Women,” “Women’s,” and “female” for an exploratory search into all of the various women’s groups.

However, many of these approaches are extremely narrow in scope and low in quantity of results (e.g. “Society of Women” produces a little over 60,000 results in the U.S., and “Association of Female _____” returns just shy of 4,000 results). If you try and search for (“women OR women’s”), while you get nearly 2M results in the U.S., if you scroll through the pages, you can see that there are a fair amount of profiles of men that are returned, and that’s to be expected given that the search terms aren’t exclusive to women’s profiles – they can show up on men’s profiles too.

As an example of something that is less obvious, outside of the box, and more exclusive to women’s profiles would be something I’ve hypothesized, tested, and confirmed on LinkedIn for years – searching for (her OR she), which returns nearly 900,000 profiles.

For those who haven’t seen me present on that search before – do you know why it returns LinkedIn profiles of women?

It works because “her” and “she” can be mentioned in the summary and recommendation sections of women’s profiles.

You would not likely find many LinkedIn profiles of men mentioning “her ” or “she,” although they do exist. (sourcing challenge – do you know how to find them exclusively?)

Yes, (her OR she) is a bit clever, and yes, I’m a bit proud of the discovery, but it clearly demonstrates the fact that all anyone needs to do is *think* about what terms that could be searched for that would be relatively unique to the people you are trying to find and test any ideas you come up with to verify.

While (her OR she) “works” in that it returns predominantly women-only results,  it returns less than 1M profiles in the U.S. – so certainly not a big slice of all of the women on LinkedIn.

How could we do better?

Let’s try another more traditional search approach – women’s universities and colleges.

(“Agnes Scott College” OR “Alverno College” OR “Barnard College” OR “Bay Path College” OR “Bennett College” OR “Brenau University” OR “Brescia University College” OR “Bryn Mawr College” OR “Carlow College” OR “Cedar Crest College” OR “Chatham University” OR “College of New Rochelle, The” OR “College of Saint Benedict” OR “College of Saint Elizabeth” OR “College of Saint Mary” OR “Columbia College” OR “Converse College” OR “Cottey College” OR “Douglass Residential College of Rutgers University” OR “Hollins University” OR “Judson College” OR “Mary Baldwin College” OR “Meredith College” OR “Midway College” OR “Mills College” OR “Moore College of Art & Design” OR “Mount Holyoke College” OR “Mount Mary College” OR “Mount St. Mary’s College” OR “Notre Dame of Maryland University” OR “Pine Manor College” OR “Russell Sage College” OR “St. Catherine University” OR “Saint Joseph College” OR “Saint Mary-of-the-Woods College” OR “Saint Mary’s College” OR “Salem College” OR “Scripps College” OR “Simmons College” OR “Smith College” OR “Spelman College” OR “Stephens College” OR “Sweet Briar College” OR “Trinity Washington University” OR “Wellesley College” OR “Wesleyan College” OR “Wilson College” OR “Women’s College”)

That search returns just over 410K results in the U.S.

That’s less than my (her OR she) search, although of course you could use the -/NOT operator to make each search mutually exclusive and to eliminate overlap.

Let’s try a sorority search:

(“Alpha Chi Omega” OR “Alpha Delta Chi” OR “Alpha Delta Pi” OR “Alpha Epsilon Omega” OR “Alpha Epsilon Phi” OR “Alpha Gamma Delta” OR “Alpha Kappa Alpha” OR “alpha Kappa Delta Phi” OR “Alpha Phi Gamma” OR “Alpha Phi” OR “Alpha Pi Omega” OR “Alpha Pi Sigma” OR “Alpha Rho Lambda” OR “Alpha Sigma Alpha” OR “Alpha Sigma Kappa” OR “Alpha Sigma Omega” OR “Alpha Sigma Rho” OR “Alpha Sigma Tau” OR “Alpha Xi Delta” OR “Ceres” OR “Chi Omega” OR “Chi Upsilon Sigma” OR “Delta Chi Lambda” OR “Delta Delta Delta” OR “Delta Gamma” OR “Delta Gamma Pi” OR “Delta Kappa Delta” OR “Delta Phi Epsilon” OR “Delta Phi Lambda” OR “Delta Phi Mu” OR “Delta Phi Omega” OR “Delta Psi Delta” OR “Delta Sigma Chi” OR “Delta Sigma Theta” OR “Delta Tau Lambda” OR “Delta Xi Nu” OR “Delta Xi Phi” OR “Delta Zeta” OR “Gamma Alpha Omega” OR “Gamma Eta” OR “Gamma Phi Beta” OR “Gamma Phi Omega” OR “Gamma Rho Lambda” OR “Gamma Sigma Sigma” OR “Kappa Alpha Theta” OR “Kappa Beta Gamma” OR “Kappa Delta Chi” OR “Kappa Delta Phi” OR “Kappa Delta” OR “Kappa Kappa Gamma” OR “Kappa Phi Gamma” OR “Kappa Phi Lambda” OR “Kappa Phi Zeta” OR “Lambda Pi Chi” OR “Lambda Pi Upsilon” OR “Lambda Psi Delta” OR “Lambda Tau Omega” OR “Lambda Theta Alpha” OR “Lambda Theta Nu” OR “Mu Sigma Upsilon” OR “Omega Phi Beta” OR “Omega Phi Chi” OR “Phi Beta Chi” OR “Phi Mu” OR “Phi Sigma Rho” OR “Phi Sigma Sigma” OR “Pi Beta Phi” OR “Pi Lambda Chi” OR “Sigma Alpha Epsilon Pi” OR “Sigma Alpha Iota” OR “Sigma Delta Tau” OR “Sigma Gamma Rho” OR “Sigma Iota Alpha” OR “Sigma Kappa” OR “Sigma Lambda Alpha” OR “Sigma Lambda Gamma” OR “Sigma Lambda Upsilon” OR “Sigma Omega Nu” OR “Sigma Omega Phi” OR “Sigma Omicron Pi” OR “Sigma Phi Kappa” OR “Sigma Phi Omega” OR “Sigma Pi Alpha” OR “Sigma Psi Zeta” OR “Sigma Sigma Rho” OR “Sigma Sigma Sigma” OR “Tau Theta Pi” OR “Theta Nu Xi” OR “Theta Phi Alpha” OR “Zeta Phi Beta” OR “Zeta Tau Alpha”)

That search returns nearly 1.2M results in the U.S.

Not bad – now we’re over 1,000,000 profiles, which is actually much higher than simply searching for the term “sorority.”

However, instead of trying these more traditional search ideas, let’s try to think of the single most inclusive way of finding women on LinkedIn.

Have any ideas?

Well, what’s more inclusive than first names?

Of course you can search by groups, sororities, and sports, but you can find a larger portion of people by searching by first name.

You might be asking, “How can I possibly create and run a search by all of the female names – there must be thousands?!?!”

Yes, there are thousands, and no, we can’t practically search for all of them – and certainly not in a single search.

However, what you can do is go to a number of websites and find the most popular female names and search for those, which will statistically yield a significant portion of the women represented on LinkedIn. In the U.S., we can use the Social Security Administration website, which conveniently lets you search for the top 200 most common first names for girls and boys by decade.

Fortunately, I’ve done the heavy lifting for you. I copied the top 200 female first names from the 1950’s, 1960’s, 1970’s, 1980’s and 1990’s into Excel, sorted them alphabetically, then removed the duplicates to come up with the most popular 417 names from those 5 decades, which nearly covers the entire span of LinkedIn’s strongest representation.

Then I used those names to create a Boolean OR statement, which looks like this:

(Abigail OR Adriana OR Adrienne OR Aimee OR Alejandra OR Alexa OR Alexandra OR Alexandria OR Alexis OR Alice OR Alicia OR Alisha OR Alison OR Allison OR Alyssa OR Amanda OR Amber OR Amy OR Ana OR Andrea OR Angel OR Angela OR Angelica OR Angie OR Anita OR Ann OR Anna OR Anne OR Annette OR Annie OR April OR Ariana OR Ariel OR Arlene OR Ashlee OR Ashley OR Audrey OR Autumn OR Bailey OR Barbara OR Becky OR Belinda OR Beth OR Bethany OR Betty OR Beverly OR Bianca OR Bonnie OR Brandi OR Brandy OR Breanna OR Brenda OR Briana OR Brianna OR Bridget OR Brittany OR Brittney OR Brooke OR Caitlin OR Caitlyn OR Candace OR Candice OR Carla OR Carly OR Carmen OR Carol OR Carole OR Caroline OR Carolyn OR Carrie OR Casey OR Cassandra OR Cassidy OR Cassie OR Catherine OR Cathy OR Charlene OR Charlotte OR Chelsea OR Chelsey OR Cheryl OR Cheyenne OR Chloe OR Christie OR Christina OR Christine OR Christy OR Cindy OR Claire OR Claudia OR Colleen OR Connie OR Constance OR Courtney OR Cristina OR Crystal OR Cynthia OR Daisy OR Dana OR Danielle OR Darlene OR Dawn OR Deanna OR Debbie OR Deborah OR Debra OR Delores OR Denise OR Desiree OR Destiny OR Diamond OR Diana OR Diane OR Dianne OR Dolores OR Dominique OR Donna OR Doreen OR Doris OR Dorothy OR Ebony OR Eileen OR Elaine OR Elizabeth OR Ellen OR Emily OR Emma OR Erica OR Erika OR Erin OR Eva OR Evelyn OR Faith OR Felicia OR Frances OR Gabriela OR Gabriella OR Gabrielle OR Gail OR Gayle OR Geraldine OR Gina OR Glenda OR Gloria OR Grace OR Gwendolyn OR Hailey OR Haley OR Hannah OR Hayley OR Heather OR Heidi OR Helen OR Holly OR Irene OR Isabel OR Isabella OR Jackie OR Jaclyn OR Jacqueline OR Jade OR Jaime OR Jamie OR Jan OR Jane OR Janet OR Janice OR Janis OR Jasmin OR Jasmine OR Jean OR Jeanette OR Jeanne OR Jenna OR Jennifer OR Jenny OR Jessica OR Jill OR Jillian OR Jo OR Joan OR Joann OR Joanna OR Joanne OR Jocelyn OR Jodi OR Jody OR Jordan OR Josephine OR Joy OR Joyce OR Juanita OR Judith OR Judy OR Julia OR Julie OR June OR Kaitlin OR Kaitlyn OR Kara OR Karen OR Kari OR Karina OR Karla OR Katelyn OR Katherine OR Kathleen OR Kathryn OR Kathy OR Katie OR Katrina OR Kay OR Kayla OR Kaylee OR Kelli OR Kellie OR Kelly OR Kelsey OR Kendra OR Kerri OR Kerry OR Kiara OR Kim OR Kimberly OR Kirsten OR Krista OR Kristen OR Kristi OR Kristie OR Kristin OR Kristina OR Kristine OR Kristy OR Krystal OR Kylie OR Lacey OR Latasha OR Latoya OR Laura OR Lauren OR Laurie OR Leah OR Leslie OR Lillian OR Linda OR Lindsay OR Lindsey OR Lisa OR Lois OR Loretta OR Lori OR Lorraine OR Louise OR Lydia OR Lynda OR Lynn OR Lynne OR Mackenzie OR Madeline OR Madison OR Makayla OR Mallory OR Mandy OR Marcia OR Margaret OR Maria OR Mariah OR Marianne OR Marie OR Marilyn OR Marisa OR Marissa OR Marjorie OR Marlene OR Marsha OR Martha OR Mary OR Maureen OR Mckenzie OR Meagan OR Megan OR Meghan OR Melanie OR Melinda OR Melissa OR Melody OR Mercedes OR Meredith OR Mia OR Michaela OR Michele OR Michelle OR Mikayla OR Mildred OR Mindy OR Miranda OR Misty OR Molly OR Monica OR Monique OR Morgan OR Nancy OR Natalie OR Natasha OR Nichole OR Nicole OR Nina OR Norma OR Olivia OR Paige OR Pam OR Pamela OR Patricia OR Patsy OR Patti OR Patty OR Paula OR Peggy OR Penny OR Phyllis OR Priscilla OR Rachael OR Rachel OR Raven OR Rebecca OR Rebekah OR Regina OR Renee OR Rhonda OR Rita OR Roberta OR Robin OR Robyn OR Rosa OR Rose OR Rosemary OR Roxanne OR Ruby OR Ruth OR Sabrina OR Sally OR Samantha OR Sandra OR Sandy OR Sara OR Sarah OR Savannah OR Selena OR Shannon OR Shari OR Sharon OR Shawna OR Sheena OR Sheila OR Shelby OR Shelia OR Shelley OR Shelly OR Sheri OR Sherri OR Sherry OR Sheryl OR Shirley OR Sierra OR Sonia OR Sonya OR Sophia OR Stacey OR Stacie OR Stacy OR Stefanie OR Stephanie OR Sue OR Summer OR Susan OR Suzanne OR Sydney OR Sylvia OR Tabitha OR Tamara OR Tami OR Tammie OR Tammy OR Tanya OR Tara OR Tasha OR Taylor OR Teresa OR Terri OR Terry OR Theresa OR Tiffany OR Tina OR Toni OR Tonya OR Tracey OR Traci OR Tracie OR Tracy OR Tricia OR Valerie OR Vanessa OR Veronica OR Vicki OR Vickie OR Vicky OR Victoria OR Virginia OR Vivian OR Wanda OR Wendy OR Whitney OR Yesenia OR Yolanda OR Yvette OR Yvonne OR Zoe)

You can then take that Boolean OR statement and enter it into the first name field in LinkedIn. Unfortunately, while that search *used* to work with a free LinkedIn account, that no longer seems to be the case, as I keep getting errors.

It does appear that you can search for about half of that list at once in LinkedIn for free. Try it for yourself here, but I have to warn you, LinkedIn still appears to choke on the search when you try it with a free account. If you can get it to work, you can only view 100 results with a free account, and even then, you will likely run into LinkedIn’s commercial use limit.

The best approach would be to use a premium LinkedIn account in which you can actually fit the entire search into the first name field and view up to 1,000 results at a time.

In LinkedIn Recruiter, my search of 417 female first names returns over 38M results in the U.S. alone.


When it comes to finding women on LinkedIn, how big of a slice does a little over 38M represent in relation to all of the U.S. women on LinkedIn?

Let’s do a little math.

LinkedIn claims about 115M U.S. profiles.

Assuming that this Forbes article is accurate in reporting that LinkedIn has an even ratio of men and women (51%/49%), then there should be approximately 56.4 M female profiles on LinkedIn.

So the ~38.4M results from my search of the 417 first names I ran above could be capturing up to 68% of all of the U.S. women on LinkedIn (38.4M / 56.4M).

Not bad for a single search, and massively more inclusive of any other way of searching for women on LinkedIn (groups, colleges, sororities, etc.)!

If you’re looking for ways to specifically source and recruit women in engineering, I highly recommend you read this LinkedIn post on the topic – it is 1,000 times more effective than trying to hop on the #ILookLikeAnEngineer bandwagon. :)

Of course, if your diversity need is to find male candidates, you can do the exact same thing as above, using the most common male names.

LinkedIn Diversity Sourcing: Racial and Ethnic Diversity

Is your information need to find racially or ethnically diverse candidates?

Well then, all you have to do is think about what might show up predominantly, and ideally only on profiles of people representing specific racial and ethnic groups.

What comes to mind for many people includes searching for groups, fraternities and sororities, and historically black colleges and universities.

Speaking of which, here is a search for 105 HCBU’s:

(“Alabama A&M University” OR “Alabama State University” OR “Albany State University” OR “Alcorn State University” OR “Allen University” OR “University of Arkansas at Pine Bluff” OR “Arkansas Baptist College” OR “Barber-Scotia College” OR “Benedict College” OR “Bennett College” OR “Bethune-Cookman University” OR “Bishop State Community College” OR “Bluefield State College” OR “Bowie State University” OR “Central State University” OR “Cheyney University of Pennsylvania” OR “Claflin University” OR “Clark Atlanta University” OR “Clinton Junior College” OR “Coahoma Community College” OR “Concordia College, Selma” OR “Coppin State University” OR “Delaware State University” OR “Denmark Technical College” OR “Dillard University” OR “University of the District of Columbia” OR “Edward Waters College” OR “Elizabeth City State University” OR “Fayetteville State University” OR “Fisk University” OR “Florida A&M University” OR “Florida Memorial University” OR “Fort Valley State University” OR “Gadsden State Community College” OR “Grambling State University” OR “Hampton University” OR “Harris-Stowe State University” OR “Hinds Community College at Utica” OR “Howard University” OR “Huston-Tillotson University” OR “Interdenominational Theological Center” OR “J. F. Drake State Technical College” OR “Jackson State University” OR “Jarvis Christian College” OR “Johnson C. Smith University” OR “Kentucky State University” OR “Knoxville College” OR “Lane College” OR “Langston University” OR “Lawson State Community College” OR “LeMoyne-Owen College” OR “Lewis College of Business” OR “Lincoln University” OR “Lincoln University of Missouri” OR “Livingstone College” OR “University of Maryland Eastern Shore” OR “Meharry Medical College” OR “Miles College” OR “Mississippi Valley State University” OR “Morehouse College” OR “Morehouse School of Medicine” OR “Morgan State University” OR “Morris Brown College” OR “Morris College” OR “Norfolk State University” OR “North Carolina A&T State University” OR “North Carolina Central University” OR “Oakwood University” OR “Paine College” OR “Paul Quinn College” OR “Philander Smith College” OR “Prairie View A&M University” OR “Rust College” OR “Saint Paul’s College” OR “Savannah State University” OR “Selma University” OR “Shaw University” OR “Shorter College” OR “Shelton State Community College” OR “South Carolina State University” OR “Southern University at New Orleans” OR “Southern University at Shreveport” OR “Southern University and A&M College” OR “Southwestern Christian College” OR “Spelman College” OR “St. Augustine’s College” OR “St. Philip’s College” OR “Stillman College” OR “Talladega College” OR “Tennessee State University” OR “Texas College” OR “Texas Southern University” OR “Tougaloo College” OR “Trenholm State Technical College” OR “Tuskegee University” OR “University of the Virgin Islands” OR “Virginia State University” OR “Virginia Union University” OR “Virginia University of Lynchburg” OR “Voorhees College” OR “West Virginia State University” OR “Wilberforce University” OR “Wiley College” OR “Winston-Salem State University” OR “Xavier University of Louisiana”)

While people have been leveraging HCBU’s for years in their diversity sourcing efforts, unlike most (all?) ATS’s, resume databases, and Internet search engines, LinkedIn is the only place that I am aware of that can handle 3,000+ character Boolean search strings to allow you to search for all of them at once (thank you LinkedIn!).



While anyone who performs diversity sourcing in the U.S. is familiar with HCBU’s, not everyone knows that you can also search for colleges and universities that have a high percentage of other racial or ethnic groups, such as historically Native American colleges and universitieshere’s the search on LinkedIn – it all comes down to your specific need.

If you’d like to take the fraternity/sorority approach, here is a LinkedIn search for African American fraternities and sororities.

(“Sigma Pi Phi” OR “Alpha Phi Alpha” OR “Kappa Alpha Psi” OR “Omega Psi Phi” OR “Phi Beta Sigma” OR “Sigma Rhomeo” OR “Wine Psi Phi” OR “Iota Phi Theta” OR “Phi Delta Psi” OR “Delta Psi Chi” OR “Beta Phi Pi” OR “MALIK Fraternity” OR “Sigma Phi Rho” OR “Phi Rho Eta” OR “Gamma Psi Beta” OR “Alpha Kappa Alpha” OR “Delta Sigma Theta” OR “Zeta Phi Beta” OR “Sigma Gamma Rho” OR “Phi Delta Kappa” OR “Iota Phi Lambda” OR “Eta Phi Beta” OR “Gamma Phi Delta”)

Depending on need, you can also construct queries for Asian American, Latino, LGBT, and other cultural interest fraternities and sororities.

As with all information retrieval efforts, it comes down to your specific information need and discovering ways of achieving those needs.

LinkedIn Diversity Sourcing: Surname Search

Speaking of specific diversity sourcing needs, you may be able to experiment with searching for last names to achieve your diversity sourcing goals.

Just as a quick and random example, here is search for the top 100 Chinese surnames:

(Lǐ OR Wáng OR Zhāng OR Liú OR Chén OR Yáng OR Zhào OR Huáng OR Zhōu OR Wú OR Xú OR Sūn OR Hú OR Zhū OR Gāo OR Lín OR Hé OR Guō OR Mǎ OR Luó OR Liáng OR Sòng OR Zhèng OR Xiè OR Hán OR Táng OR Féng OR Yú OR Dǒng OR Xiāo OR Chéng OR Cáo OR Yuán OR Dèng OR Xǔ OR Fù OR Shěn OR Zēng OR Péng OR Lǚ OR Sū OR Lú OR Jiǎng OR Cài OR Jiǎ OR Dīng OR Wèi OR Xuē OR Yè OR Yán OR Yú OR Pān OR Dù OR Dài OR Xià OR Zhōng OR Wāng OR Tián OR Rén OR Jiāng OR Fàn OR Fāng OR Shí OR Yáo OR Tán OR Shèng OR Zōu OR Xióng OR Jīn OR Lù OR Hǎo OR Kǒng OR Bái OR Cuī OR Kāng OR Máo OR Qiū OR Qín OR Jiāng OR Shǐ OR Gù OR Hóu OR Shào OR Mèng OR Lóng OR Wàn OR Duàn OR Zhāng OR Qián OR Tāng OR Yǐn OR Lí OR Yì OR Cháng OR Wǔ OR Qiáo OR Hè OR Lài OR Gōng OR Wén)



You could of course combine this approach with one or more of the gender diversity Boolean search strings if that would help you achieve your diversity sourcing goals.

If you’re wondering if anyone actually performs these kinds of diversity-focused sourcing strategies, the answer is a resounding “yes!” I have people approach me all the time at conferences referencing how they’ve successfully leveraged the diversity sourcing strategies and tactics outlines in this post. Recently, while attending and speaking at the always awesome Talent42 technical recruiting conference in Seattle, I had someone tell me how they leveraged the most common Korean surnames to find a bilingual engineer which made short work of the otherwise seemingly impossible challenge.

Final Thoughts on LinkedIn Diversity Sourcing

To be sure, what I’ve demonstrated here has some obvious limitations and is far from perfect, but it does effectively illustrate that you do have some creative proactive sourcing options for underrepresented gender and racial/ethnic groups in your organization, allowing you to move beyond relying solely on posting jobs and hoping to get qualified (and diverse!) applicants from active candidates.

Proactive diversity sourcing has the distinct benefit of giving you access to the deeper end of the talent pool – those people (typically more than 2/3rds of any given population) who aren’t actively seeking employment and thus cannot be reached through any form of job advertisement, no matter where you post or share it.

Ultimately, what I really wanted to accomplish by writing this article was to get you thinking a little bit differently when it comes to diversity sourcing and sourcing in general. Effective sourcing, diversity or otherwise, isn’t about Boolean search strings – it’s about critical thinking and always seeking to step outside of the box to find ways to meet your information needs.

As an added bonus for reading this entire post, here is a LinkedIn search of the top 200 most popular female first names in the United States from the 1950’s, 60’s, 70’s, and 80’s de-duped to the most popular 354 names from those decades, which captures the 23-62 year old demographic, which nearly covers the entire span of LinkedIn’s strongest representation.





How Would You Search for these Positions on LinkedIn?

One of the things that has always struck me as extremely odd with regard to sourcing is the fact that there appears to be so little sharing of Boolean search strings.

While one can find basic search string examples in training materials and in various sourcing groups online, I know plenty of sourcers and recruiters that have never seen another person’s production search strings – those used to actually fill positions.

Why do you think that is? I have my ideas, and I’d like to know yours.

I believe there may be several contributing factors:

  1. Some people just don’t save their searches. If I were a betting man, from what I’ve seen over the past 15+ years, I’d wager that the majority of people don’t save their search strings. If they’re not saved anywhere – you severely limit any sharing opportunities to live, in-the-moment situations that may or may not ever present themselves.
  2. It simply never occurs to some people to share their searches with others – unless someone specifically asks, why would someone?
  3. Plain old insecurity. Some folks might not want to share their search strings with others because they are afraid theirs are somehow “wrong,” inferior or inadequate.
  4. The belief that their Boolean search strings are somehow their “secret sauce” and that in sharing their searches might somehow expose their competitive advantage.

What do you think?

How Would You Search for these Positions on LinkedIn?

Are you up to the challenge of sharing some of your searches with a global audience of talent acquisition professionals? Continue reading

Why Boolean Search is Such a Big Deal in Recruiting

In the past, I’ve explained the Boolean Black Belt concept and exposed what I feel is the real “secret” behind learning how to master the art and science of leveraging information systems for talent identification and acquisition.

Now I would like to show you precisely WHY Boolean search is such a big deal in recruiting.

There are 2 main factors:

  1. Candidate variable control
  2. Speed of qualified candidate identification.

The goal of this article is to shed significant light on the science behind talent mining, how it can lead to higher productivity levels (more and better results with less effort), why I am so passionate sourcing, and why everyone in the HR, recruiting, and staffing industry should be as well.

Control is Power

Talent identification is arguably the most critical step in recruiting life cycle – you can’t engage, recruit, acquire, hire and develop someone you haven’t found and identified in the first place.

My experience has shown me that properly leveraging deep sources of talent/candidate data (ATS/CRM’s, resume databases, LinkedIn, etc.) can enable recruiters to more quickly identify a high volume of well matched and qualified candidates than any other method of candidate identification and acquisition (e.g., cold calling, referral recruiting, job posting).

The true power of Boolean search lies in the intrinsically high degree of control over critical candidate variables that using Boolean strings to search deep data sources such as resume databases, the Internet, and social media affords sourcers and recruiters.

Applying that that high degree of control to large populations of candidates – tens of thousands (small internal ATS, niche resume database) to tens of millions (large ATS/CRM, Monster resume database, LinkedIn, etc.) enables adept sourcers to perform feats of talent identification and acquisition most would think impossible.

Continue reading

What is a Boolean Black Belt Anyway?

I’ve been blogging nearly 3 years now, and I realized I’ve never come out and actually defined the term “Boolean Black Belt.”

The concept seems pretty self explanatory, but there has been at least 1 person who’s taken the opportunity to point out (and gain some traffic in the process – but it’s all good!) that it could be perceived as a bit of an oxymoron to be an “expert” in something as simple as 3 Boolean operators.

Interestingly, however, I’ve found that most sourcers and recruiters don’t even fully exploit the various powers of the OR and NOT operators – not even close.

So what is a “Boolean Black Belt” anyway? Continue reading

Recruiting Technology is Not Anti-Relationship!

Technology and Relationships are not Oil and Water

When I write posts about creating Boolean search strings to source and find talent/human capital – I often get responses from readers and those I train, especially staffing industry veterans who focus on executive search, that state that the foundation of recruiting is based on relationships built by human interaction and networking.

I couldn’t agree more.

Why does it seem to be ingrained in human nature to have an either/or mentality – as if things have to be one way or the other, but not both. Like phone sourcing vs database sourcing. You can and should do both, and I hope you are trying to contact and develop relationships with people identified via both methods.

If I wanted to be obtuse, I could argue that the phone is impersonal – and that to be a really good recruiter, I should never leverage the phone to make contact with people. Instead – I’ll just wander around looking for people to meet in person to establish a wonderful professional relationship with.

By the way – there isn’t anything instrinsically impersonal about leveraging technology to find or communicate with people. In case you haven’t noticed, there’s this thing called email that quite a few people use these days, and you know what? – it seems to work. I’ve also heard that there are millions of people communicating with something called text messaging, and that there are more text messages sent every day than phone calls made. How impersonal! :-)

Let’s face it – if it didn’t work, it wouldn’t exist and be used by so many people so often.

When I talk about leveraging technology for talent identification and acquisition, my primary point is NOT that it is a replacement for any other method of candidate identification, nor am I saying technology is a replacement for human interaction and relationship building. My point is that there is more and more information stored about more people somewhere electronically every day – and you can either learn how to harness the power of using Boolean logic to create search strings for Talent Mining that can ACCELERATE your ability to establish MORE relationships with MORE of the RIGHT people, MORE quickly…..or not. Continue reading

Basic Boolean Search Operators and Query Modifiers Explained


Basic Boolean Operators Explained

No, those aren’t my hands.

I never cease to be amazed by what you can find on the Internet and what people take pictures of.

Now that I have your attention, this post is going to focus on the basic Boolean search operators and search modifiers symbols and will not go into any detail of the many special Internet-only search commands/operators.

Although a great many people seem to think that Boolean = Internet search, Boolean logic and searching has been around WAY before the Internet. And here’s a quick fact: you don’t have to capitalize Boolean operators on any of the major job boards and many of the major ATS’s.

Go ahead – try it. Nothing will explode and your searches will execute.

And now, back to the Boolean basics…

Boolean Search Operator: AND

The AND operator is inclusionary and thus limits your search.

It should be used for targeting required skills, experience, technologies, or titles you would like to limit your results to. Unless you are searching for common words, with every AND you add to your Boolean query, the fewer results you will typically get.

Example: Java AND Oracle AND SQL AND AJAX

On most Internet search engines and LinkedIn, every space is an “implied AND,” and you don’t have to type it, as every blank space is interpreted as an AND operator.

Example: Java Oracle SQL AJAX

Bonus: You can use the ampersand (&) as the AND operator on Monster.

Boolean Search Operator: OR

The OR operator offers flexible inclusion, and typically broadens your search results.

Many people incorrectly think the Boolean OR operator is an either/or operator, when in fact it is not.

The OR operator is technically interpreted as “at least one is required, more than one or all can be returned.”

Although some search engines, such as Google, do not require you to encapsulate OR statements with parentheses, if you don’t on most databases and LinkedIn – your search will run but execute in a way that you probably did not intent.  As a best practice, I tell people to always use parentheses around OR statements as a matter of good search syntax.

Example: Java AND Oracle AND SQL AND AJAX AND (apache OR weblogic OR websphere)

The returned results must mention at least one of the following: apache, weblogic, websphere. However, if candidates mention 2 or all 3, they also will be returned, and most search engines will rank them as more relevant results because of such.

The best ways to use OR statements is:

  1. To think of all of the alternate ways a particular skill or technology can be expressed, e.g., (CPA OR “C.P.A” OR “Certified Public Accountant”)
  2. To search for a list of desired skills where you would be pleased if a candidate had experience with at least one, e.g., (apache OR linux OR mysql).

Bonus: You can use the pipe symbol (|) for the OR operator on Google, Bing, and Monster.

Boolean Search Operator: NOT

The NOT operator is exclusionary – it excludes specific search terms and so the query will not return any results with that term (or terms) in them.

Example: If you were searching for an I.T. Project Manager, you may want to employ the NOT operator in order to eliminate false positive results – results that mention your search terms but do not in fact match your target hiring profile.  In this case, you could run: “project manager” and not construction – this search will not return any results with “project manager” and the word “construction” contained within them.

On all of the major job board resume databases, some ATS’s and LinkedIn, you can use the NOT operator in conjunction with an OR statement.

Example: .Net AND NOT (Java OR JSP OR J2EE) – that search will not return any results with any mention of Java, JSP, and/or J2EE.

Bonus: NOT has 2 main uses

  1. Excluding words you do not want to retrieve to reduce false positive results (most common usage)
  2. Starting with a very restrictive search with many search terms, you can use the NOT operator to systematically and progressively loosen the search into mutually exclusive result sets (not so common usage, but very effective strategy)

Basic example:

  1. “Project Manager” AND SQL AND Spanish
  2. “Project Manager” AND SQL AND NOT Spanish
  3. “Project Manager” AND NOT SQL AND Spanish
  4. “Project Manager” AND NOT (SQL OR Spanish)

Bonus: You can use the minus sign as the NOT operator on many sites and search engines, including LinkedIn.

Boolean Search Modifier: ASTERISK *

The asterisk can be used on most resume databases and non-Internet search engines as a root word/stem/truncation search. In other words, the search engine will return and highlight any word that begins with the root/stem of the word truncated by the asterisk.

For example: admin* will return: administrator, administration, administer, administered, etc.

The asterisk is a time saver for search engines that recognize it (most major job boards and ATS’s) because it saves you from creating long OR statements and having to think of every way a particular word can be expressed.

LinkedIn does not support the asterisk, so you will have to construct large OR statements to search for all of the various ways someone could mention each term you’re searching for. For example: (configure OR configuring OR configured OR configures)

Boolean Search Modifier: PARENTHESES

As a best practice, use parentheses to encapsulate OR statements for the search engines to execute them properly.

Remember, the OR operator is interpreted as “I would like at least one of these terms.” Think of parentheses as your way of telling the search engine you’re looking for one of THESE: (_______________).

For example: (apache OR weblogic OR websphere)

If you don’t enclose all of your OR statements, your search may run but it will NOT run as intended.

Boolean Search Modifier: QUOTATION MARKS ” “

Quotation marks must be used when searching for exact phrases of more than one word, or else some search engines will split the phrase up into single word components.

For example: “Director of Tax” will only return “Director of Tax.” If you searched for Director of Tax without the quotation marks, on some search engines, it will split up the words Director and Tax and highlight them as relevant matches even when not mentioned as an exact phrase.

Bonus: Google auto-stems many search terms, so if you are looking specifically for the word manager, it will still return managed, management, etc. – even if you don’t want it to. If you put quotation marks on a single word in Google, it will defeat the auto-stemming feature and only return that specific word.

There you have it – Boolean basics!

If there is something you would like to see me post about with regard to Boolean logic and search tactics and strategies – let me know.



Master Boolean Logic and Raise Your Game!

When it comes to golf, what’s more important – the clubs or the golfer?

It should be obvious that it is not the clubs, but the technique and skill of the person wielding the clubs.  Tiger Woods could play better than most people even with 20 year old clubs found at a yard sale. 

If you own a set of golf clubs but can’t play 18 holes in under 100 strokes, it’s more likely due to your skill and ability level rather than the brand and price of your clubs. Simply owning a set of clubs (even the best available) does not make you a great golfer.

Likewise, just because you have access to the Internet, an internal database/ATS, social networks, and perhaps a job board to two (which all “speak” Boolean, by the way!) – it does not automatically mean you are adept at leveraging those information systems to quickly find great candidates. You either know how to wield Boolean operators to quickly find the best talent available in these resources or you don’t. Your ability (or lack thereof) isn’t due to the Boolean operators themselves – it’s knowing how to use them and the search strategies you apply.

If you are in a sourcing and/or recruiting role and you are not fluent in Boolean, you are no different than someone who owns a set of golf clubs, but who cannot play very well. It’s not the clubs – it’s on you.

More information about more people is being stored somewhere electronically every day and it will only continue to accelerate and increase. Whether you realize it or not, if you are not adept at interfacing with databases, applications, the Internet and social networks (in other words, creating Boolean search strings) to find and retrieve human capital data you are already at a significant competitive disadvantage, and it will only get worse over time.  Technology can be a productivity multiplier, but only if you know how to use it to its full potential. 

I continue to be fascinated by recruiting and staffing professionals who show no desire to learn how to apply Boolean logic to query sources of candidates for talent.  Hearing a sourcer or recruiter complain about having to learn how to harness the power of Boolean search strings is like running into someone on a golf course complaining that golf is a difficult game.  Why are they on the golf course? Why are they even trying to play if all they are doing is complaining about how hard it is? They’ve chosen to play the game – why don’t they stop complaining, take some more golf lessons, practice a lot, and get better? Golf is golf – the game doesn’t really change – it doesn’t get more difficult with each passing day. People who set a goal of becoming good at golf make a conscious decision to get better and take lessons and practice a lot to improve their skill and ability.

Similarly, if you’ve chosen a career in recruiting and staffing (by design or by accident), instead of making excuses and complaining about how hard it is to learn Boolean logic and to create effective Boolean search strings, why not stop complaining, make a conscious effort to improve your skill and ability – get some training on how to create and leverage effective Boolean search strings, and practice a lot to get better? In this case, it’s not a hobby – it’s your career! What could be more important than learning how to be more effective at your chosen career?!?!?

Technology isn’t going away.  There won’t be any less information about people stored electronincally in the future – quite the opposite. Learning how to apply Boolean logic to create effective search strings to leverage information systems to increase your effectiveness and your productivity as a sourcer or recruiter isn’t that difficult – all it takes is a conscious decision to commit to improving your game, getting some training, and lots of practice.

Why learn how to master Boolean search strings?

Image by shawnblog

Image by shawnblog

Why bother to learn the arcane art and science of Boolean search logic?

It really bothers me when I read or hear about the idea that sourcers and recruiters don’t need to worry about learning how to craft and execute Boolean queries for talent identification and acquisition. This opinion usually has something to do with the idea that creating effective Boolean search strings is a time-consuming and difficult-to-learn process, and ultimately ends up in lowly “buzzword matching.”

It’s one thing to hear this kind of thought coming from a software vendor that’s selling a product, claiming that their “fuzzy logic” or “artificial intelligence” application can match candidates to job openings as well as a senior sourcer or recruiter can, without the need to learn how to create and run advanced Boolean queries. I get it – they’re selling something…the idea that their software can reduce or eliminate the need to train yourself or your sourcing/recruiting team on how to create effective Boolean search strings. I can’t blame the software vendors – they’re trying to make money.

It’s another to hear this kind of thought coming from a staffing professional – that’s just scary. It tells me very clearly that the person expressing this opinion doesn’t have a strong understanding of, or a high level of expertise with, the inherent power and control advanced Boolean search tactics and strategies can afford a sourcer or recruiter when it comes to talent identification and acquisition. If you don’t know how to use it or only have a basic level of understanding of it, how are you qualified to have an opinion on it, least of all a potentially negative and damaging opinion? Yes, I do know what they say about opinions. I’ll keep it clean here.

Discounting the power and value of learning how to effectively wield Boolean search strings is no different than saying that there’s little value in learning how to effectively perform cold calling/phone sourcing. With either method of sourcing, primary or secondary, it is more the person applying the concepts, tactics, strategies, and techniques than the Boolean operators or the phone sourcing scripts themselves. Make no mistake – it’s the human element that gets the results.

Okay, so Boolean Logic isn’t as sexy as Social Media and certainly isn’t the staffing buzzword du jour. However, does anyone think for a second that the world is going to go backwards to storing everything on paper? HELLO?!? With more and more information being stored electronically (pretty much everything, really) – online somewhere (Social Networks, blogs, job boards, etc.) or buried in a corporate database/ATS, it’s worthless unless you can retrieve it. You can’t retrieve information electronically without using some kind of query logic. So how does it make sense to think that it’s not critically important that sourcers and recruiters learn how to manipulate information retrieval logic? Continue reading

Black Belt Boolean

I know – you may be asking yourself, “What is this guy thinking?” A blog about Boolean queries when all the buzz is currently about Social Networking, Mobile Recruiting and such? True – Boolean search strings aren’t as sexy, shiny or new as Facebook, Twitter or Cloud Recruiting. However, in the hands of an expert, and in my direct experience, advanced Boolean search strategies and tactics used in conjunction with internal/corporate resume databases and job boards (yes, I said job boards – more on that later) can and do yield higher quantities of highly relevant results more quickly than any other method of talent identification and acquisition. Continue reading