The trouble with search today is that people put too much trust in search engines – online, resume, social, or otherwise.

I can certainly understand and appreciate why people and companies would want to try and create search engines and solutions that “do the work for you,” but unfortunately the “work” being referenced here is thinking.

I read an article by Clive Thompson in Wired magazine the other day titled, “Why Johnny Can’t Search,” and the author opens up with the common assumption that young people tend to be tech-savvy.

Interestingly, although Generation Z is also known as the “Internet Generation” and is comprised of “digital natives,” they apparently aren’t very good at online search.

The article cites a few studies, including one in which a group of college students were asked to use Google to look up the answers to a handful of questions. The researchers found that the students tended to rely on the top results.

Then the researchers changed the order of the results for some of the students in the experiment.  More often than not, they still went with the (falsely) top-ranked pages.

The professor who ran the experiment concluded that “students aren’t assessing information sources on their own merit—they’re putting too much trust in the machine.”

I believe that the vast majority of people put too much trust in the machine – whether it be Google, LinkedIn, Monster, or their ATS.

Trusting top search results certainly isn’t limited to Gen Z – I believe it is a much more widespread issue, which is only exacerbated by “intelligent” search engines and applications using semantic search and NLP that lull searchers into the false sense of security that the search engine “knows” what they’re looking for.

This is Your Search Without a Brain

It’s easy to see why people and companies create search products and services using semantic search and NLP that claim to be able to make searching “easier” – they are looking to sell a product  based on the value of making your life easier, at least when it comes to finding stuff.

If you take a look at some of the marketing materials for intelligent search and match search products, you’ll find value propositions such as “Stop wasting time trying to create difficult and complex Boolean search strings,” ”Let intelligent search and match applications do the work for you,” and “A single query will give you the results you need – no more re-querying, no more waste of time!”

I love saving time and getting to what I want faster, but my significant issue with “intelligent search and match” applications is that they try to determine what’s relevant to me.

And that’s a rather large issue, because only I know what I am looking for.

It’s critical to be reminded that the definition of ‘relevance,’ specifically with regard to information science and information retrieval, is “how well a retrieved document or set of documents meets the information need of the user.”

The only person that can make the judgment of how well a search result meets their information need is the person conducting the search, because it’s their specific information need.

Any reference to “relevance” by a search engine, whether it be Google, Bing, LinkedIn, Monster, etc., is based purely on the keywords, operators, and/or facets used.

Search engines don’t know what you want – they only know what you typed into or selected from the search interface.

Poor use of keywords, operators or facets will don’t stop you from getting results. All searches “work,” as I am fond of saying – but the quality or relevance will likely be low.

Of course, that assumes that the person conducting the search is actually proficient at judging the quality or the relevance of the results – comparing results to their specific information need and experimenting with different combinations of keywords, operators and facets to look for changes in relevance.

Related Does Not Equal Relevant

I personally never implicitly trust first page or top ranked search results online, nor top ranked results on LinkedIn, Monster, or anywhere I search. Some of the best search results I have ever found were buried deep in result sets – far past where most people would typically review, and essentially in the territory of results the search engine deemed least “relevant.” Pshaw!

One reason for this is because I understand that any search engine I use, no matter how “dumb” (straight keyword matching), or “intelligent” (semantic/NLP), they can only work with the terms I give it. What do you think the  most “intelligent” search engine can do with poor user input?

When it comes to searching, unfortunately everyone’s a winner, because every search “works” and returns results.  The problem is that few searchers know how to critically examine search results for relevance.

Regardless how how “intelligent” a search engine might be, it can only try to find terms and concepts related to my user input.

This is an often overlooked but critical issue – just because terms might be related, it does not mean they are relevant to my information need.

It certainly helps to understand that some of the most relevant search results can’t actually be retrieved by the obvious keywords, titles or phrases, or even those that a semantic search algorithm deems related to them. In fact, some of the best results simply cannot be directly retrieved – see my post on Dark Matter for more information on the concept.

However, to appreciate the concept that no single search, no matter how enhanced by technology, can find all of the relevant (by human standards and judgment) results available to be retrieved, you have to know a thing or two about information retrieval in the first place.

And if you already lack the ability to critically judge search results and evaluate them for relevance, how can you be expected to be able to evaluate and critically examine the search results returned by intelligent search and match applications?

The “black box” matching algorithms of intelligent search and match applications pose significant issues to users in that searchers have absolutely no insight as to why the search engine returns the results it does. Without this, what option does a user have other than to implicitly trust the search engine’s matching algorithm?

Searching Ain’t Easy

Who says search has to be easy anyway?

Just because you might want it to be, should it be? Does it have to be?

Let’s face it – a lot of people look for the easy way out. The sheer volume of advertisements pushing diet supplements that claim you can lose a ton of weight without having to watch what you eat and exercise is evidence that people want to get the results they want without working for them.

You know the best way to lose weight? A healthy diet combined with regular exercise. The problem is that eating healthy and exercising regularly is that it requires discipline and hard work.

I’m not saying there isn’t a better way to search – I am a fan of Thomas Edison’s belief that “There is always a better way.”

However, I believe that the better way, specifically when it comes to information retrieval, involves discipline and the hard work of people using critical thought in the search process – not short-cutting or completely removing it from the equation.

And I am not alone.

There is already considerable work being done to create new kinds of search systems that depend on continuous human control of the search process. It’s called Human-Computer Information Retrieval (HCIR) - which is the study of information retrieval techniques that bring human intelligence into the search process.

Truly intelligent search systems should not involve limiting or removing human thought, analysis, and influence from the search process – in fact, they should and can involve and encourage user influence.

When you break it down, the information retrieval process has 2 basic parts:

1. The user enters a query, which is a formal statement of their information need
2. The search engine returns results

The key, in my opinion, is that the search engine should return results in a ”Is this what you were looking for?” manner and allow you to intelligently refine your results, as opposed to a “This is what you were looking for” manner.

There’s a BIG difference.

The former begs for user influence and input, the latter does not – it makes the assumption that it found what you wanted

The bottom line is that no matter what you are using to search for information, only you know what you’re looking for and therefore judge the relevance of the search results returned.

Intelligent search isn’t easy, because you actually have to think before and after hitting the search button.

The Intelligent Search Process

As I have written before, searching should not be a once-and-done affair – there is no mythical “once search to find them all.”

Searching is ideally an iterative process that requires intelligent user input.

Here is an example of an intelligent, iterative search process applied to sourcing talent:

1. Analyzing, understanding, and interpreting job opening/position requirements
2. Taking that understanding and intelligently selecting titles, skills, technologies, companies, responsibilities, terms, etc. to include (or purposefully exclude!) in a query employing appropriate Boolean operators and/or facets and query modifiers
3. Critically reviewing the results of the initial search to assess relevance as well as scanning the results for additional and alternate relevant search terms, phrases, and companies
4. Based upon the observed relevance of and intel gained from the search results, modifying the search string appropriately and running it again
5. Repeat steps 3 and 4 until an acceptably large volume of highly relevant results is achieved

Anyone can enter search terms and hit the “search” button, but not everyone can effectively and intelligently search.

Until you’ve witnessed intelligent and iterative search in action, you likely wouldn’t know the difference between “great” search results, “good” search results and “bad” search results.

It’s as dramatic as the difference between and experienced professional offshore fisher, a recreational fisher, and someone going offshore fishing for the first time.

The ocean holds the same fish for everyone fishing it. While a first-time or recreational fisher can get lucky every once in a while, only a person who really knows what they’re doing can get “lucky” on a consistent basis and catch the fish  the recreational fisher only dreams of catching.

Final Thoughts

The ability to enter in some search terms and click the “search” button doesn’t convey any supernatural search ability, but it does certainly make people feel like they are good at searching, because unless you mistype something, everyone’s a winner.

Ultimately, search engines of all types retrieve information, but information requires analysis, and only humans can analyze and interpret for relevance.

Eiji Toyoda, the former President of Toyota Motor Corp., has observed that “Society has reached the point where one can push a button and immediately be deluged with…information. This is all very convenient, of course, but if one is not careful there is a danger of losing the ability to think.”

Critical thinking is perhaps the most important skill a knowledge worker can possess.

The reason why so many people stink at search is because most people simply don’t think before or after they search, and they place too much trust in the machine.

Additionally, the quality of the search terms/info entered directly affects the quality of the results. “Garbage in = garbage out” certainly applies here. And effective searching is rarely a “once and done” affair – the ability to critically evaluate search results for relevance and successively refine the search criteria to increase relevance is the key to true “intelligent search.”

“Black box” matching algorithms can be wonders of technology and engineering, but they pose significant problems in that searchers have absolutely no insight as to why they return the results they do, and in many cases, the engineers creating these semantic/NLP matching algorithms assume they know what their users are looking for better than the users themselves. I’m sorry if I am the only person offended by such an assumption.

Okay, I’m not sorry.

I love technology, and I use and have used some of the best matching technology available, but also I know it’s not a good idea to try to limit or remove intelligent critical thinking from the search process and completely replace it with matching algorithms.

The term human–computer information retrieval was coined by Gary Marchionini whose main thesis is that “HCIR aims to empower people to explore large-scale information bases but demands that people also take responsibility for this control by expending cognitive and physical energy.” (emphasis mine)

For those who simply want information systems to magically provide them with the most relevant results at the click of a button, you should take special note of the fact that experts in the field of HCIR do not believe that people should step out of the information retrieval process and let semantic search/NLP algorithms/AI be solely responsible for the search process.

If you want to get better search results, use the latest technologies, but don’t put too much trust in the machine.

Instead, put some skin in the game, take responsibility for the search process, and expend some cognitive energy critically thinking through not only your search input, but also the results for relevance.

“In the age of information sciences, the most valuable asset is knowledge, which is a creation of human imagination and creativity. We were among the last to comprehend this truth and we will be paying for this oversight for many years to come.” — Mikhail Gorbachev, 1990

Strictly For the Search Geeks

Check out this HCIR Challenge, and at least read the  introduction which compares and contrasts precision vs. recall, and references iterative query refinement.

Recently, I wrote about the intrinsic issues associated with searching LinkedIn for potential candidates with specific industry experience, and how using the “Industry” field can actually prevent you from finding the people you’re looking for. 

A number of readers responded by suggesting a logical solution to the issue – searching by specific company name(s) instead of using LinkedIn’s ”Industry” field.

It is a logical solution, but a potentially flawed one nonetheless.

I’m going to show you some reasons why, and if you read this post within the next 5 minutes, I’ll even throw in a LinkedIn  company search anomaly as an added bonus.

User Generated Content has Issues

As William Uranga pointed out in his comment, “Behaviorally-speaking, you need to use the fields in your search that most profiles have completed. “Industry” is not one of them. ‘ Company name’, ‘title”, and using geographic modifiers still yield the best results when searching your network. Even ‘keyword’ is not reliable.” 

I agree that when sourcing candidates on LinkedIn you need to use the fields that most profiles have completed – but from my research, it does appear that when you create a LinkedIn profile, you actually do have to choose an industry. I tried not selecting an industry and leaving it at “Choose industry” and LinkedIn would not allow me to save my profile without selecting one from the list – I got an angry red “Please enter a value” for my efforts.

So it appears that every LinkedIn profile will actually have an industry selected – but the issue remains that it may not be the industry you’d assume people would use.

I agree with William that searching by keyword is not reliable – there are many LinkedIn profiles that do not have any text entered into the description field under each work experience. I’m curious to know the exact percentage of these “skeletal” LinkedIn profiles that only have company names and titles entered, but something tells me LinkedIn wouldn’t be motivated to release that number. If I had to hazard a guess based on my experience searching LinkedIn, I’d say at least 40%. 

Searching LinkedIn using the ”Company” and “Title” fields as William suggests generally does yield good results. However, with these fields we are dealing with user generated content. Instead of choosing from a fixed list (which has its own set of issues), people can choose to enter whatever they want into these fields – and it may not be what you’d assume.

Allow me to demonstrate…

Searching by Company

For some companies, there may only one way in which a company’s name can be expressed/written. However, there are many companies where people can and do write the company names in a wide variety of ways - not only on LinkedIn, but on their resumes as well.  

Financial Services Example

Let’s say you were looking for people with experience in the Financial Services industry, and after reading my recent article on the industry search issue, you wisely realized that people who work in the Financial Services industry might not actually select that industry when they create or modify their LinkedIn profile. So instead, you start thinking of target companies to search for using the “Company” field.

So you start selecting companies, and let’s say one of the companies you’d like to target is JPMorgan Chase. If you’re a regular reader of my blog, you’d know that the first thing you’d need to do is obey the Cardinal Rule of E-Sourcing, which states that for every term you are thinking of including in your search, consider how many ways it can be expressed.

Okay, so to find people who have experience working for JPMorgan Chase, you need to think of all of the other ways that people who’ve worked for the company can express that fact. An easy one is JPMC. Others would include JPMorganChase, “JPMorgan Chase,” and “JP Morgan.”

Here’s positive proof:

There might even be more (such as WAMU, etc.) – but my point here is that if you go beyond searching by industry (which you actually have to), you must be careful to think of all of the various ways people who have worked in your target industry and target companies could possibly express that experience.

If you don’t – you create Hidden Talent Pools of candidates that exist in LinkedIn (or wherever you search), and you cannot find them.

Pharmaceutical Example

If you were looking for people with big pharma experience, you might want to target GlaxoSmithKline. To do so, you’d quickly and correctly assess the fact that not everyone who has worked for GlaxoSmithKline will actually write it that way on their LinkedIn profile (or resume).

Similar to the JPMC example above, we can safely assume some people might abbreviate the company name down to GSK. Of course, some people might also write “Glaxo SmithKline,” “Glaxo Smith Kline,” or “GlaxoSmith Kline.” And they do:

If you didn’t think of those alternate ways of expressing experience working for GlaxoSmithKline, you quite simply would not and could not find those candidates. You would not even be aware that they exist.

Yeah, But this Doesn’t Apply to MY Industry…

If I haven’t got your mind racing on how you can apply this process to your own sourcing efforts, perhaps thinking that I picked a couple of “ringers” with JPMC and GSK – think again. This phenomenon isn’t limited to any particular industry, nor is it limited to the more obvious companies such as PricewaterhouseCoopers (PWC, “Pricewaterhouse Coopers,” etc.).

In fact, inherently one-word company names aren’t immune either.

How about Microsoft?

How could someone who’s worked for Microsoft mention the company other than “Microsoft?”

I know of at least one way:

Yeah – there’s a couple hundred of those in the U.S. alone on LinkedIn.

Are there other ways people might express working for Microsoft? Maybe

Trust me – your target industry and at least some of your target companies are not immune to this principle. In fact, it’s highly likely that you’ve been missing candidates in your sourcing efforts for a long time now because of the intrinsic issues associated with user generated content.

Going Confidential

Let’s not forget all of the people who don’t actually list the names of the companies they’ve worked for.

If you’re searching by company name, you simply cannot find people who actually work for your target company but do not list the company, who instead use “confidential” as their employer.

There’s over 5,000 U.S. LinkedIn profiles like this.

Have you ever specifically searched for people who list “confidential” as their current employer? If so, I’d say you’re a rare breed of sourcer – perhaps 1 in 100 sourcers have ever even thought to do this. It’s not rocket science by any stretch, but most people simply don’t think enough before they search for candidates. This technique is pretty obvious once I point it out though, right?

Company Research on LinkedIn

Remember that LinkedIn anomaly I alluded to in the intro of this article? 

Well, let’s say you are doing some research on LinkedIn to find the names of other companies in your target industry to include in your search, and your target industry is “Defense and Space.”

If you select “Defense and Space” from the industry list…

… you’d get 50 results.

But do you think there are only 50 companies in the defense and space industry represented on LinkedIn?

Me neither.

I noticed that when I select an industry under LinkedIn’s company search functionality, LinkedIn enters keywords for me:

I didn’t type in “Defense And Space” – LinkedIn did it for me. LinkedIn will do it for any industry you choose – try it for yourself.

Being the curious guy that I am, I wanted to see what happened if I deleted the words automatically entered by LinkedIn and searched again:

I got almost 1700 companies. 

That strikes me as more accurate than 50.

Interesting, yes?

No?

Well, it should be - because anyone who uses LinkedIn’s quite robust company search/research functionality (you do, don’t you?) may be getting seriously short-changed in their search results if they don’t delete the auto-populated keywords and re-run their searches when attempting to get comprehensive lists of companies in target industries.

I have reason to believe that at least a couple of LinkedIn employees read my blog. Let’s see how quickly they fix this anomaly. 

Final Thoughts

Hidden Talent Pools (Google the term) are very real. If you’re not careful to stop to think before you search, you can all to easily and unknowingly create pools of candidates that you cannot and do not find. But they’re there.

LinkedIn, Facebook, Twitter – any social network profile consists mainly of free form user generated content (just like resumes), and when people have the ability to enter whatever they think is appropriate to describe their employers and work experience, you’re going to get a wider variety than you might assume. Before you conduct ANY search – take a moment to think about all of the various ways your quarry could possible express what it is that you’re looking for.

Oh – and one last thing: What I’ve detailed in this post also applies to X-Ray searching LinkedIn for people who have worked at specific companies within a target industry as well. 

Special Thanks

Eric Jaquith- thank you for telling my that my site looked like crap on iPhones.

At his suggestion, I installed the WPtouch plugin and now the site is much easier to read and navigate on iPhones, BlackBerries and other smart phones. From this point on, if you ever read my site using your mobile device – you owe your enhanced mobile BBB experience to Eric!

When I deliver presentations on how to leverage LinkedIn to source candidates, I have the opportunity to get a sense of what most people seem to know about using LinkedIn.  Recently I have been making it a point to ask how people tend to sort their search results when searching LinkedIn, and the overwhelming majority leave their results sorting at the default value, which is “relevance.”

I find this especially interesting, because most people do not seem to realize that when you sort your search results by “relevance” on LinkedIn, you are not getting results based solely on the search terms entered – you are getting results ordered by a combination of factors – including your “social graph.” 

LinkedIn’s definition of “relevance” is decidedly different than practically every other searchable source of potential candidates – Monster, Google, Applicant Tracking Systems, Twitter, etc. – and what LinkedIn *thinks* is relevant to you may actually not be based on what you are specifically looking for.

What are Relevant Results?

First, it is important to get to the heart of the term “relevance” when it comes to search results.  

The Merriam Webster Dictionary defines relevance as  ”the ability (as of an information retrieval system) to retrieve material that satisfies the needs of the user.”

When it comes to the philosophical concept of relevance, “relevance is a term used to describe how pertinent, connected, or applicable something is to a given matter,” and can be defined as: ”Something (A) is relevant to a task (T) if it increases the likelihood of accomplishing the goal (G), which is implied by T.”

Therein lies the challenge and the issue - only the person performing the search can actually define what is relevant based on the task they are performing. So how exactly does LinkedIn define what is relevant?

LinkedIn’s Definition of Relevance

Nearly a year ago, LinkedIn announced their new search platform, and they explained that the relevance of search results is affected by each individual’s professional network on LinkedIn:

So, how exactly does LinkedIn determine who is “most likely to be of interest” to you?

I’m not sure if anyone outside of LinkedIn knows the relevance algorithm. We can perhaps safely assume it is determined by some combination of the specific search terms used and the searcher’s personal view on the “social graph” – their 1st, 2nd, and 3rd degree connections - with a strong favoritism towards closer connections. 

That does seem to be a safe assumption, because a Principal Search Engineer at LinkedIn commented to this combination, although without going into specific detail as to *exactly* how LinkedIn determines what is relevant for each search/searcher.

Additionally, exploring LinkedIn’s Learning Center , we can find a brief reference to this combination:

While we see that LinkedIn claims that “relevance” is based on the keywords and the searcher’s network…is it safe to assume that sorting by relevance is a mix of “Relationship” sorting and “Keyword” sorting, as described above?

Is it just me or does anyone else find it odd that only first degree, second degree, and groups are mentioned under the order of results sorted by “Relationship?” What happened to third degree connections? Are group connections ranked higher than third degree connections? 

Scrolling further down the LinkedIn Learning Center page, we find the answer (I hope!):

Okay, so this seems to say that the order of relationships does include 3rd degree connections and that 3rd degree connections are ranked higher than group connections.

However, the verbiage under “Relevance” is confusing – I have to believe that this is simply out of date (pre-launch of the new search platform in 11/08), as this definition of “relevance” makes it sound as if sorting results by relevance is based solely on the keywords used in the search, which conflicts with basically all other documentation I’ve found. 

The $64,000 Relevance Questions

When it comes to sorting results by “relevance,” I would really like to know:

• How are a searcher’s keywords weighed in comparison to the searcher’s connections?
• Could LinkedIn believe the most relevant result is a 1st degree connection but a relatively weak match based on the search terms used (keywords, titles, companies, etc.)?
• Does location have anything to do with the relevance of the results and each person’s “social graph” (e.g., are 1st degree connections in the same metro area ranked as more “relevant” than 1st degree connections elsewhere?)?

The answer to those questions would be quite enlightening!

Sorting by Keyword

As I mentioned earlier, from my own informal research, it seems that a great many people never even touch the “sort by” option list. I believe that this is mainly because they assume “relevance” is returning results based solely on the keywords they’ve entered. Which we now know is wrong.

However, we can’t really blame these folks or judge them too harshly because for just about any other database or system they have access to (job board resume databases, their ATS, Twitter, etc.), “relevance” IS based purely on the keywords used in the search.

I have found that a quite a few people are surprised to find out that LinkedIn’s “relevance” isn’t based purely on their search terms (keywords, titles, companies, etc.). The good news is that LinkedIn does offer the ability to sort results based on the keywords only (see image below).

You can also do this after you’ve executed the search:

Many of you probably already knew that – but you would be surprised at how many people don’t, or don’t even think to change the default results sorting from “relevance” based on their understanding of and experience with sorting results by “relevance” with every other system they search.

As a social network, it is nice to be able to search LinkedIn for people based on a combination of keywords and their relationship to you – when you are looking to identify potential candidates, it can make a great deal of sense to start with people with whom you have closer ties.

However, to many sourcers and recruiters, the most “relevant” people returned from a search are those who most closely match the search criteria they specified through their keywords, titles, and companies, not how closely they are connected to them on LinkedIn. 

The $64,000 Keyword Question

When it comes to sorting results by “keyword,” I would really like to know:

• Are keywords found in certain fields weighed more heavily than others (e.g., titles vs. summaries vs. experience descriptions vs. specialties…), and if so, how?

The Top 4 Keyword Relevant Results in the United States

I was just checking to see how many U.S. profiles there are on LinkedIn (by not entering any search criteria, selecting “located in or near,” selecting “United States,” and leaving the zip code BLANK) and I decided to sort the results by keyword.

Now this is interesting because I didn’t actually enter any keywords – I left every field on the advanced search form blank – the only option selected was the country.  Here is what LinkedIn considers the top 4 most relevant results in the U.S. based on keywords when there aren’t any keywords:

Interesting insights into LinkedIn’s search algorithm, right?  Please try this yourself, and let me know if you get the same top 4 results when you sort by keyword.

Final Thoughts

Please check with your peers and friends in the recruiting and staffing industry – ask them if they ever change the results sorting on LinkedIn from the default of “relevance.” You may be as surprised as I have been lately at how many people don’t realize that what LinkedIn thinks is relevant to them may not actually be.

I personally prefer to sort my LinkedIn search results by keyword, not relevance. This is because I want to see the best matches LinkedIn can offer based upon my search criteria (keywords, titles, and companies, etc.), regardless of how they are or are not connected to me. If they’re not connected to me, they are only an X-Ray away.

For anyone without premium access to LinkedIn, which means they are limited to viewing only the first 100 results of any given search, it is important to know that sorting by “relevance” may prevent you from seeing the people who may actually be the most relevant based on your search terms. This can happen any time when the people who best match your search criteria who are not in your LinkedIn network are pushed to the bottom end of the results – perhaps past result #100 – and become unviewable without premium access.

Regarding LinkedIn SEO, I’ve recently seen a presentation detailing how LinkedIn users can dramatically affect their profile’s search rank - I would advise you to thoroughly research any such claims and the information presented (such as which fields make a difference to search rankings and the specific impact of recommendations on keywords) - a good amount of the information may in fact not be accurate (at least not according to sources at LinkedIn). If you feel you have accurate info with regard to whether or not/how certain fields (e.g., titles vs. summaries vs. experience descriptions vs. specialties…) are weighted more heavily, please let me know!

And for those who’ve read all the way down here – is it just me, or did LinkedIn remove the ability to sort results by # of connections a while ago, only to sneak it back in? When did this happen?

I literally just noticed it while writing this post – they did remove sorting results by # of connections as an option for a period of time – I am not imagining that, am I?

Think you know all there is to know about Google search?

I was recently asked a question regarding the use of the asterisk when searching on Google, specifically in conjunction with certain punctuation.

This person was under the impression that if you used the equal sign on either side of an asterisk when searching Google, it would function as a multiple word wildcard operator. For example, searching for [linux=*=administrator] should return results of linux system administrator, linux systems administrator, linux network administrator, linux server administrator, etc.

The short answer is that Google ignores most punctuation, and that there is no need to combine the asterisk with any other punctuation or symbols for it to perform as a single or multiple word wildcard.

The long answer is much more interesting. I decided to perform some experiments with Google’s wildcard asterisk and I uncovered a few oddities and unsolved mysteries. I’m curious if you might be able to shed some light on them. But first, I will show you exactly how you can make good use of Google’s asterisk when searching for resumes on the Internet, as well as when X-Ray searching LinkedIn and Twitter. 

The Asterisk Operator on Google

Google treats the asterisk (*) as a placeholder for 1 or more words – it can also be referred to as a single or multiple word wildcard operator, because Google treats the asterisk as a placeholder for any unknown term(s) for which it tries to find the best match(es).  Essentially, Google “fills in the blanks” wherever there is an asterisk.

According to Google, here is an example of proper syntax when leveraging the asterisk: [ Obama voted * on the * bill ] – notice how there is a space on either side of each asterisk.

Searching for Candidates using Google’s Asterisk Operator 

Judicious use of the asterisk on Google when searching for candidates can yield great results and can increase relevance.

For example, if you are looking for someone who has experience administering linux, you could search for rigid phrases such as “administered linux,” or perhaps “linux systems administrator.” However, utilizing the asterisk on Google, you can add greater flexibility in your search and capture a wider variety and a larger number of results. 

(intitle:resume OR inurl:resume) “~administer * linux” -job -jobs

Notice the variety of the results of this search:

The variety of relevant phrases that Google’s wildcard operator returns from just these 6 results is eye-opening. No sourcer or recruiter would sit down and be able to think of every conceivable phrase a candidate could say to represent their linux administration experience. With the proper use of Google’s asterisk search operator, there’s no need to, because the asterisk “fills in the blanks.”

When it comes to leveraging the asterisk in a Google search, don’t think in terms of single keywords – think about sentences and phrases that candidates might use to express their responsibilities.

Using Google’s Asterisk in a LinkedIn X-Ray Search

You can make use of Google’s wildcard operator to target current titles when performing an X-Ray search.

For example:  

site:linkedin.com (inurl:in OR inurl:pub) -intitle:directory (“current * project manager” OR “current * program manager”)

This is how the asterisk is put to work – it “bridges the gap” across the word “current” to the current title – in this case – “Program Manager.”

Please note, however, that this technique is not flawless. In my testing, while this approach does find many profiles with the target current title, it does not actually find EVERY profile with the target current title. You can test this for yourself by running back-to-back external X-Ray and internal LinkedIn searches.

LinkedIn Phrase Searching

Of course you can also use the asterisk to search for flexible phrases just as we did with the Linux admin search above.

For example:

site:linkedin.com (inurl:in OR inurl:pub) -intitle:directory engineer “* cisco routers”

Instead of just searching for “cisco” and “router” or “routers” and simply matching keywords, we’re actually trying to target PHRASES that communicate responsibility. As sourcers and recruiters – you should not be looking just for keywords, you should really be looking for what people have DONE, not just mentions of search terms. Below you can see how using the asterisk has yielded results of people talking about configuring and implementing routers:

Google essentially “filled in the blank” of the asterisk preceding the two words of “cisco routers.” Clicking on a “cached” result shows exactly how our use of the asterisk pulled a phrase on this LinkedIn profile of someone who has been responsible for configuring Cisco routers.

Using Google’s Asterisk in a Twitter X-Ray Search

When X-Ray searching Twitter, you can use Google’s asterisk to search specifically for words mentioned in the “Bio” field. This is especially helpful because this is the place where you can more reliably find titles and professional skills.

Let’s try looking for people who mention the word “accountant” in their Twitter bio:

site:twitter.com “bio * accountant”

Click here for the 579 results.

Clicking into a result, we can see how this worked beautifully:

You can go a step further and try using two asterisks to reach further into the bio field:

site:twitter.com “bio * * accountant”

Click here for the 468 results.  Notice the lack of overlap in the results with the single asterisk search above.

Remember that Google treats the asterisk as a single or multiple word wildcard. Exactly how many words? I am not sure, and Google’s documentation does not appear to say. Using 2 asterisks in the example above essentially extends the distance between the word “bio” and the word “accountant” – inserting more “blanks” for Google to fill in.

And you can keep adding more. For example, let’s try 3 asterisks:

site:twitter.com “bio * * * accountant”

Click here for the 350 results.

Punctuation in Google Search Strings

Now let’s get back to the initial question about combining the asterisk with punctuation.

Google’s basic help page USED to explain that “with some exceptions, punctuation is ignored (that is, you can’t search for @#$%^&*()=+[]\ and other special characters).” I say “USED” to because I can no longer find that specific statement on Google’s basic or advanced help pages, although it can be found quoted in the web search help forum.

Interestingly, if you search Google’s web search help for “punctuation,” the second result is this:

There’s the phrase I’m looking for – however, when you click on the result, it takes you to this page, which doesn’t actually contain the phrase “with some exceptions, punctuation is ignored (that is, you can’t search for @#$%^&*()=+[]\ and other special characters).”

Weird. I wonder why it’s been removed. No doubt due to my Google-thinks-I’m-not-human search experiments.

In any event, Google is supposed to ignore the equal sign, along with @#$%^&*()+[]\. Remember that list – I’m going to show you that Google doesn’t actually ignore all of those symbols/punctuation marks.

Experimenting with the Asterisk

While Google states that you can’t search FOR the equal sign, I decided to do a little testing to see exactly what Google makes of it if you do use it on either side of an asterisk in a query, just like the person who asked me the initial question apparently did (e.g. linux=*=administrator).

I also tried several different searches using some of the other supposedly ignored punctuation in combination with the asterisk, as well as one scenario where I didn’t use any spaces on either side of the asterisk – just to see what would happen. Yeah – this is what I do in my spare time. I’m that guy.

Search #1 =*=

(inurl:resume | intitle:resume) linux=*=administrator (301 OR 703)  -job -jobs

1 result.

Search #2 /*/

(inurl:resume | intitle:resume) linux/*/administrator 301 -job -jobs

1 result- the same as the one from search #1

Search #3 Single space on either side of the asterisk, using quotes

In addition, I added quotation marks around the “linux * administrator” phrase to more closely approximate searches #1 and #2 above, as there is technically no space on either side of the asterisk, keeping it a single phrase.

(inurl:resume | intitle:resume) “linux * administrator” 301 -job -jobs

1 result again, same as before.

So this shows that Google does in fact ignore the equal sign and the slash - it doesn’t have any effect on the asterisk/wild card operator over a space.

However – things get a little interesting when you try the question mark.

Search #4 ?*?

(inurl:resume | intitle:resume) linux?*?administrator 301 -job -jobs

We get 357 results.

Okay – we go from 1 result with the = and the /, as well as spaces enclosed by quotation marks, to 357 results simply by using a question mark? Something is going on with the question mark, but I am not sure exactly what. However -let’s remember that Google doesn’t explicitly mention the question mark in their list of ignored punctuation: @#$%^&*()=+[]\.

So the question mark really is a question mark. Yeah, I went there.

Do you have any insight as to why Google treats the question mark (?) any differently than an equal sign or a slash?

Search #5 A single space on either side of the asterisk, without quotes

(inurl:resume | intitle:resume) linux * administrator 301 -job -jobs

357 results again.

These results provide SOME insight, because they return the same number of results as search #4 that used the question marks. This leads me to believe that the question mark is actually ignored, because it returns the same number of results as the string that simply has spaces on either side of the asterisk.

However, if the question mark is ignored, then how can Google return fewer/different results in searches #1 and #2 that use the = and the /, which should also be ignored?

More questions than answers here. Have any insight?

Search #6 No space on either side of the asterisk

(inurl:resume | intitle:resume) linux*administrator 301 -job -jobs

499 results this time. Interesting, yes?

This reveals another mystery, because I can’t explain exactly why linux*administrator (no spaces) and linux * administrator (single space on either side of the asterisk) return different results, let alone why the search with no spaces returns broader results, which is a little counterintuitive considering all we did was eliminate the spaces around the asterisk. If anything, one might assume the results should tighten?

I attached a capture of 3 results from page 10 – notice how far apart the words administrator/administration and Linux are from each other. 

Search #7 &*&

It gets even more interesting. As we’ve seen, Google claims to ignore most punctuation, including the ampersand. However, it certainly does not ignore the &, as evidenced by the fact that this search returns 0 results:

(inurl:resume | intitle:resume) linux&*&administrator 301 -job -jobs

Zero results.

Conclusion

The single/multiple word wildcard operator on Google has many uses – it can help you target current titles with LinkedIn X-Ray searches, search for terms and titles in Twitter bios, and move beyond simply searching for keywords and step into the realm of searching for phrases that suggest actual responsibilities.

As for the combination of punctuation and the asterisk operator, it appears that the = sign (as well as a few other supposedly ignored symbols) actually seem to “bridge the gap” and effectively convert strings employing the asterisk (*) to something very similar to a phrase search using quotation marks (e.g., “linux * administrator”). While the asterisk can represent 1 or more words, when used in conjunction with a phrase search using quotation marks (or, as we have seen with = or / punctuation on either side of a single asterisk), Google returns results where the words on the left of the asterisk are always very close to those on the right of the asterisk – in most cases they are separated by only 1 word.

However, as we have seen – not all symbols are created equal. The linux?*?administrator is not processed the same way as linux=*=administrator. It appears that the ? does not “bridge the gap” of the words on either side of the asterisk as the = sign does, and the results are much looser – resembling the results of linux * administrator without quotations. Without the quotations, Google expands the proximity/distance between the words on the left and the right of the asterisk, in many cases well beyond 3 words.

Do you have any answers to the mysteries revealed in this post? If so – please let me know. Thanks!