Googling for Invisible Words

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Sometimes, Google indexes words from the pages’ source code that do not appear on pages. This includes the alt tag, dd tag, and a few other cases.

Here are some practical search examples. You can utilize the hidden-but-found words well in LinkedIn X-Ray!

  1. Find LinkedIn members by job location. (This is not possible on LinkedIn, even in keywords). “work location * san francisco bay area”
  2. “I accept direct messages and business inquiries by anyone on LinkedIn for free, even if we’re not connected.”
  3. People recommended by Donna “Click here to view Donna Svei, Executive Resume Writer’s profile”
  4. Companies past and present “ibm graphic”; only past – “ibm graphic” -intitle:ibm
  5. Schools – went to Princeton “princeton graphic” -intitle:princeton
  6. Group members “sourcing summit graphic”
  7. Certifications “Google AdWords Search certification graphic”
  8. Associations “women in technology graphic” “director of engineering”
  9. Companies past and present “google logo”
  10. “google logo”

Fun, huh?

Do not forget to sign up for our upcoming advanced X-Ray Webinar!

X-Ray Mastery

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We are lucky that Google keeps supporting its 21 advanced search operators even though most of its users never use the operators (and those who do rarely click on ads).

As it is getting harder to search, particularly for requirements such as Diversity with no search filters provided by Social Networks, scraping and automation are becoming must-have skills for Sourcers. So is advanced Google search, especially X-Ray (the site: operator), for the same reasons – wider data distribution, more outdated (and fake) data, and absence of search filters that are requested. You can also scrape X-Ray results and filter them out in Excel or use them for data cross-referencing (another post).

Yet another reason to master X-Ray was the recent unfortunate change to LinkedIn search. Clearly, based on Facebook posts, those who knew X-Ray felt “safe”. You could not even search for a title like “Developer” – with X-Ray, you can. I am glad to report that they have fixed the weirdness. But there are still multiple ways to X-Ray LinkedIn for values unavailable on LinkedIn or Recruiter:

  • “True” current company and job title
  • Email addresses such as “”
  • Memberships in organizations
  • Certifications
  • Accented characters and emojis
  • Recommendations
  • X-Ray results include profiles out of your network.

And here is a fun search: you can message these people for free.

Combining X-Ray and Image search and “changing your location” will produce a respectable-sized dataset.

The operator site: as well as “X-Ray” as a way to call it among recruiters, is not new. But you can no longer survive with “Boolean strings tip sheets” (behind the thinking of the organization that came up with the “X-Ray” term) because the number of sights to X-Ray is expanding, and sites change fast. Your stored search template will break, and more often as time goes.

An essential Sourcer’s skill is, for a site, to identify:

  • What can be X-Rayed?
  • What are the specific elements for the pages of interest such as:
    1. Keywords in the URLs
    2. Keywords in the page titles
    3. Standard wording on a page
    4. Ways to search for specific data (such as location)
    5. Ways to push specific words (such as email addresses) into snippets?

With that knowledge, you can start constructing search strings for the day. X-Ray knowledge is one of the six skill categories we test.

Join us for a brand-new class Advanced X-Ray Searching on October 6th and become an X-Ray Tiger overnight! As always, the slides, recording, and a month of support are included. Seating is limited. People Search Anti-Improvements

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[Edited] Phew! They have fixed it. It might have happened due to me filing an issue – once my message was communicated to Developers, the behavior went away in a few hours. Interacting with @LinkedInHelp is not for the faint of heart – they asked me whether I know about Boolean search and sent me to read the help article, said that “if many users require the change, we will consider it” (they tweeted it but then deleted the tweet), and the infamous “have you cleaned your cookies?”. It took a couple of dozen messages before they sent it to the development team. Oh well. people search just got more unintuitive and inconvenient. Last week, Jan Bernhart shared on our Facebook group that when you put keywords in the Title field, LinkedIn will find past titles as well.

Here are the changes:

1) Put keywords in the Title, find titles past and present.

For example, you search for Title=developer and find VPs who used to be Developers a long time ago. (How is this helpful?)

This search will find people who have been developers, managers, and VPs in some positions, past or present.

Both past and present is a significant loss since we need to search for current titles. The way around is excluding the wrong titles, but it is cumbersome:

developer NOT manager NOT director NOT vp

2) Put a company name in the Company field – find companies past and present.

People who have worked at Amazon, Google, and Microsoft.

If you select a company, it will search for the current. Compare Amazon selected and in the Company field.

However, searching for a selected company finds fewer results than in the Company field (if it searched for the current company). Also, we can no longer search for the current company, including the word “bank,” for example.

It is a loss.

3) Entering a job title in the keywords, will find the same as searching by Title (i.e., titles past and present). Even a search for a skill like Java results only in searching in job titles. So does a search for the keyword “certified.”

Which keywords will LinkedIn interpret as keywords, and which as job titles? We have no idea.

So, X-Ray has become more important. We can search for present titles and companies since they are in the profile page titles. X-Ray will ignore past “unclosed” positions as well.



The Future of Sourcing Is Technical (Scraping and Automation)

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I think that Talent Sourcing will become more technical. We will have to use scraping and automation to stay productive and competitive. (This is not advice on scraping or automating work on any site in legal terms, of course.)


I anticipate the increased necessity for scraping due to:

1) Growing demand to source for diversity combined with the limitations of “officially” available search filters (notably, on LinkedIn).

Sourcing for Diversity can be challenging since few systems provide “diversity” search filters. We try our best, searching in different ways.

Scraping allows you to be more inclusive because you can search wide(r) and filter results in Excel. You will not need to review each result, only those narrowed by filtering. Results may contain good profiles that you – or others – won’t find with narrow(er) searches or limiting filters.

Any sourcing requiring a “non-existent” filter (not only “diversity”) would benefit from scraping as well. A couple of my recent projects had this challenge:

  • Software Engineers who work for a non-profit (a non-profit is hiring)
  • Business Development Manager in AZ who is Hispanic/Latino (another non-profit, aiming to connect with that community).

2) Professional data being distributed across sites and updated at different times (e.g., Github and LinkedIn)

Scraping can facilitate cross-referencing between sites, combining professional and contact data from each to your benefit.

People aggregators such as AmazingHiring, Entelo, HiringSolved, Hiretual, and others, get professional data in one place and offer some diversity search filters. However, aggregators present two challenges:

  • Data gets outdated fast (it is too expensive to keep updating all of it)
  • Coverage of industries and locations is uneven.

In the end, aggregators can serve as sources but not as a sourcing solution for most projects.


helps in a competitive market (at least) in two areas:

  • Candidate outreach and follow-ups until they respond
  • Recruitment Marketing.

The good news is that you can use “visual” scraping and automation tools that do not require writing code. There is some getting used to UI/UX and understanding each tool’s capacity, but anyone can learn.

How do you find data for scraping? By mastering advanced Google searches and, in particular, X-Ray. You will be finding sites to scrape as well as tweaking X-Ray strings to present scrapable results.

I would be glad to hear what you think – please comment.

Three Ways to X-Ray LinkedIn for Diversity

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You can search for Diversity candidates on LinkedIn using first names, pronouns, organizations, education (including Alumni search), group memberships, associations, employers (for veterans), and other ways.

X-Raying on Google can complement your sourcing process.

Search for ethnic names with accented characters

Google can search for accented characters; LinkedIn ignores them.

As an example, you can Google for Hispanic/Latino names such as María, Lucía, Mía, Sofía, Áxel, Bastián, Sebastián, Benjamín, Nicolás, Tomás, Julián, Jerónimo, Ángel, Álvaro, Álex, and Máximo.

Note that names with accented characters may include multiple countries and languages; spelling variants of Maria include Mária (Hungarian, Slovakian), María (Greek, Icelandic, Spanish), [[Máire]] and Muire (Irish), Marya and Marija (transliterated from Cyrillic). I.e., María may find Greek and Icelandic people in addition to Hispanics. But searching for the accented variations of a name always will add diversity to your searches.


Google can search for emojis; LinkedIn ignores them.

Here are some LGBTQ+ X-Ray searches:


Google searches for everything present on public profiles, including recommendations; LinkedIn does not offer Recommendation searches by keywords.

On LinkedIn, you can search for pronouns added to the last name: she (her OR hers OR them). On Google, you can find additional profiles from keywords in Recommendations: intitle:”hardware engineer” she OR her -intitle:she.

Join us in September for the Diversity Sourcing Training and Certification for Recruiters and Teams #CDSP – Our August session, as well as the previous sessions since last October, was sold out. The August attendees are going through the exams this week.
An August session participant writes: “I’m working on completing the CDSP program, which has SO much valuable content, by the way!!  Anyway, I work on the Diversity Sourcing team at Microsoft and I was wondering if it’s ok to share some of these tips at a high level with my teammates? (I wouldn’t share the actual slides)” (We said “yes”).

The Program‘s advantage is that it is interactive, and participants have to practice to pass the exam. But if you are short on time, we have a recording.


Google Strings vs. Boolean Strings

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(Can I please ask you to read to the end?)

It has eventually become such a mismatch in terminology. Most people in our industry refer to search strings on Google as “Boolean Strings”. However, the term “Boolean”, meaning AND, OR, and NOT, no longer applies to Google search best practices, and less so every day. Practical Google search strings do not use ORs and rarely use the minus .

This similarly-styled string (submitted yesterday to our NING site as a “favorite”) is also full of syntax errors – and finds no results:

I started this blog in 2008 and “Boolean” was different back then. Should I feel sad? We search about 40% of the time and our clients like the results, but the last time any of us used a single OR on Google was years ago. We now use the term “Boolean Strings” meaning “advanced search.”

Do not take me wrong, Google is the best search engine! Using advanced operators, especially, X-Ray (site:), is a must for any Sourcer, Researcher, or OSINT practitioner. Note that Google no longer documents all the operators, most likely because a minority of us uses them. Its search currently shows the list on my blog as a “featured snippet”; I do my best to keep the page up-to-date. Dan Russell’s document is another source you can trust (he works at Google!) Many other sites – even search engine-oriented publications – list the wrong operators and copy from each other, unfortunately. It is your responsibility to apply an inquisitive mind and stay in the loop.

If you continue with ORs, especially long OR strings on Google, your results will be mediocre. Why? When Google sees ORs, it stops bringing up relevant terms. Instead of trying to take control, it is more practical to trust Google’s interpretations. As a popular (but not popular enough!) example,

  • If you search for “software engineer” OR developer, Google will do exactly what you asked
  • If you search for software engineer, you will find Coders, Programmers, etc. – results you would miss if you use OR.

Since Google has learned to interpret long quieries,  ORs have become even less productive. The semantic component in Google search is AI-based, meaning that it improves all the time, making results relevant – unless you “turn it off” with ORs and NOTs.

Clair (Milligan) Mohamed’s share brilliantly summarizes the situation:

(“Boolean Search” OR Boolean Search)

I was asked yesterday if Boolean searching works in an X-Ray search on Google.

A timely post by expert sourcer Irina Shamaeva in her Facebook group flagged up how many recruiters are still using Boolean Strings in their Google Searches when better results will come from more simple searches.

Google uses Semantic Search and coined the phrase “things not strings” as far back as 2012.

Since then their search has evolved to include NLP, machine learning, and BERT (super-advanced tech that interprets search in a more conversational way).

The old idea behind long ORs was that a clever search will “cover it all”. It puts “the” “Boolean String” as the goal. Indeed, a one-click solution sounds attractive! (We continue to get requests for “the” string for someone’s job requisition, sometimes as a matter of urgency.) The goal is results, not a string.

It is best to keep Google search simple 95% of the time – and search (anywhere) an iterative process. Every string leads to a new one and uncovers information to take into account. If someone “builds” a “string”, runs it on Google, gets the results, and stops, they are using the best search engine not in its best way. It is difficult and unnecessary (like eating soup with toothpicks. )

As a practical example, when you write senior OR sr OR snr OR principal engineer OR developer OR lead OR coder on Google – that is 16 separate searches with no interpretation, results are somehow mixed up – not what you want. Just software engineer is better!

A Facebook discussion is here.

And here are the shortest strings ever! (Dan says it is a bug) 🙂 🙁


The Advantages of the “Wrong Password” on LinkedIn #OSINT

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A more descriptive title of this post could be “Hack: Check Whether an Email Address or Phone Number is Registered on LinkedIn in 5 Seconds or Less”.

The “hack” does not reveal which member it is – it only returns a Yes/No answer. But it is a validation pointer for the contact phone number or email address you are looking to verify, or, in a different scenario, trying to guess the email or the missing phone digits. It is a “good to know” piece in OSINT research.

Since the death of the Sales Navigator URL hack, there is still – even improved – way to mass-cross-reference long lists of emails (as long as 9K+). But there is nothing as quick and simple as a login attempt to verify whether a single email address or phone is registered. That is, if the site “cooperates”. 😉

“Wrong password” – seen on two out of the four screenshots below – is a welcome response that reliably validates the contact data you have.

There is nothing new about the “research using login dialogs” OSINT approach. This post is an alert that it works well on LinkedIn right now. 🙂 These checks do not trigger any notifications either (but avoid entering the same existing email just in case). Enjoy them before things change again. (With LinkedIn, imminent change is the only thing we can be sure about, lol).

Looking for someone?

Looking for professionals whose social online footprint is minimal (common for  essential workers), or perhaps an old classmate or your backpacking buddy?

It is often hard to find people on LinkedIn:

  • with “shallow” (barely filled out) profiles
  • with outdated professional history or education
  • spelling their name in different ways (or changing the name)

But if you have a guess at at the email address or found an old address, seeing “wrong password” means that the person likely uses the email – or phone – in question, (Gmail-based emails have the highest chance of being kept.) You can then find the person by email, see more info, and email them if relevant. Of course, not finding a LinkedIn member by phone or email is far from guaranteeing that the contact data is outdated – it can be either way – but it is also a data point.

To perform the hack:

Log out of LinkedIn. The rest is best explained by the four screenshots below.

This is how registered emails and phone numbers show up vs. unregistered, on the login page. (Type something random and long as a password and press “sign in”.)

Tip: Pace yourself (if you know what I mean)! If you start seeing cows or sheep to rotate, changing your IP address would help.
Automating the hack to run over a list of contacts would be a next-to-impossible task.

That’s it.

(To my readers: I do not know if there is a way to reliably identify someone by phone on LinkedIn – if you do, I would love to hear!)

“Hacks” make monotonous parts of research work quicker, add discoverability to data, and fun to work. 🙂

As a piece of news, David Galley and I are finalizing the 4th fully updated edition of the popular eBook, “Sourcing Hacks” (available for pre-order).

Join us for an interactive preview of an expanded set of Sourcing Hacks in the upcoming webinar this Thursday. Expect to learn a good number of new sourcing hacks we have uncovered. Attendance includes:

  • the slides complementing the ebook’s examples
  • video recording for you to keep
  • get the ebook (as soon as it is ready) at a discount
  • one month of online support.

If you learn from video, audio, or interaction and practice better than from reading books, this webinar is for you!


How (and Why) to Grow Your LinkedIn Network Overnight

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In 2006, Christian Mayaud came up with a genius idea of LIONs – LinkedIn Open Networks. There was no connection limit; you could see the exact numbers of connections on profiles and the 4th level connections. But then, as now, people outside of your network were “invisible,” shown in search as “LinkedIn Member” with no details – and you were invisible to them as well. Imagine a business gathering where you can only see people you know or people who know them – it does not make sense!

“LION” means that the person would not “IDK” – say “I don’t know you” to your invitation – which hurts your membership. LIONs have made the network much more connected.

It is everyone’s individual decision whether to add “LION” to your last name and commit to accepting all invites. (I am no longer one since my connections are almost at the maximum level of 30K).

But it makes sense to have at least 500-1,000 connections and include connections with LIONs (who are well connected themselves) for your visibility to others and being able to see more members, no matter what your profession is. It helps to do business and find employment.

The straightforward way is to invite LIONs. However, the number of connection requests you can send per week is now limited.

Here is how to connect with as many LIONs as you wish in one day. If you upload an email list (as long as 9K) to Contacts and invite from that upload, the number of invitations is unlimited. And all you need is a list of emails; names do not matter.

How do you get the email addresses? Many LIONs have them publicly visible. LinkedIn won’t search for “” to find emails, but Google will. A search like intitle:LION “” – plus any keywords you could vary to get more results – will produce lists fast. Go for “,” “,” etc., if you wish as well, or search for distinct company email domains like “,” “”. Use Julia’s Email Extractor, which can collect emails from every page you visit (set the option in the extension) to get a long list in no time. (See David’s video demonstrating the technique).

That’s it 😊

Ideal Candidate and LinkedIn Recruiter

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Recruiters are aware that most job descriptions lack some information necessary to source potential candidates. A job description is written to attract talent, and it is only one piece of data for Recruiters. We gain the extra info and Hiring Manager’s preferences through intake write-ups and meetings.

We have designed our sourcing intake form Brain Gain Recruiting Sourcing Checklist to solicit that extra information. I find an “ideal candidate’s profile” (or two, or three) to be especially helpful.

LinkedIn Recruiter (LIR) offers searching by an ideal candidate. The idea is admirable, but have you noticed how it is implemented?

When you search by an ideal candidate, LIR does exactly the following:

  • Sets an OR of the person’s job titles.

This means that you will be searching for past job titles, which could be more junior or even totally different from what you need.
I have a friend who once was an oboe player, then a database admin, and now is a speech therapist. If you are searching for a speech therapist, do you want to see oboe players?

  • Adds an OR of the person’s companies.

Do you want to hire from the same companies? Most often, not – especially if the “ideal” person has worked at small companies or already works at the company in a similar role.

  • Adds an OR of the person’s skills.

LIR’s skill search now looks (way) beyond self-entered skills; it finds people who have the skill keywords on the profiles. Therefore, skills search can only be separating good prospects from non-matching if you use an AND of skills.

  • Adds an OR of the person’s industries – picked from the employers and jobs past and present.

But “Industry” is a tricky filter. For the majority of professions, choosing one industry does not cover the type of work people do. Then, some members enter their “role” industry (like “Staffing and Recruiting”) while others put the employer’s industry (like “Healthcare”).
For some suggested industry selections, you will not even find that ideal candidate. For example, a good friend worked in music production years ago, before becoming a Sourcer. If you search by her profile, a suggested industry is “Entertainment” – but you will not find her when searching for it!

“Search by ideal candidate” and other features of LIR require understanding how the search “really” works and how to overcome the limiting factors. The LIR Help does not explain that.

Join us for the LinkedIn Recruiter Mastery class next Thursday, August 5th, to get clarity on its search and learn to find promising profiles that may be hidden if you just follow LIR’s prompts. As always, you can follow up with questions for a month.



Dimensions of Reverse Image Search on Google

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Reverse Image Search is a favorite way to find online traces of someone by their social profile photo.

But if you search by image on Google, you may be missing some results. Here is why and how to overcome that.

#1. When you upload an image, Google (annoyingly) shows its “explanations” – sometimes even offensive – like “hair loss.” For my friend @Infosourcer‘s Twitter profile picture in two different dimensions, it decides on “wildlife biologist” on one and “leisure” on the other. (Who needs this feature as it is?)
The “explanation” keywords is not an “innocent” addition, though – they affect your reverse image search.

Change the automatically appeared keywords to something meaningless – just “a” would work – to stop the results from being filtered. Or change them to the person’s name (or other terms) to “guide” the search.

#2. Did you notice how, in the example above, Google’s “approach” to searching by the same image in different sizes is inconsistent – it interprets the images differently? (Also note that “Find other sizes of this image” that you may see, as in the screenshot above, usually finds little.)

Searching by the same image from different sites or in different sizes often produces complementary results.

Let me demonstrate that using my business partner David’s LinkedIn profile photo as an example.
If I search by the profile photo, I find LinkedIn and a few other sites, including our CSE book on Amazon – but not the Twitter profile (which has the same picture).

But if I resize David’s LinkedIn photo to 200×200 pixels (as above) using a simple image editor, new results show up – including Twitter! If I search by the 200×200 image from Twitter, results vary again.

Summary for reverse image search on Google:

1. Remove or change Google’s guess
2. Use different image dimensions and the same image from different sites to uncover more results.

Bonus point –

How to see a LinkedIn photo without a frame

Some LinkedIn profile photos have #opentowork or #hiring frames. For reverse image search, these “stand in the way.” Images on public profiles have no frames, but LinkedIn often would not let you open the profiles without logging in. There are fun ways to see an incognito profile, such as view it with Google Mobile-Friendly Test.

But to find the image only, you can X-Ray the person’s profile in images, like so: imagesize:200×200.