Facebook Research Hacks

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Facebook member’s groups, posts, comments, and events that we are allowed to see per member’s privacy settings can help us identify professional details on potential candidates – as well as find additional candidates. Unfortunately, this information not that easy to search for any longer, ever since Facebook retired its “official” Graph search.

Here are some simple but useful “ex-Graph” searches, that work today. Many of them use the person’s ID, which we can quickly identify by the Facebook URL.

Jim Stroud’s groups https://www.facebook.com/search/100000155790214/groups
Boolean String Group Members https://www.facebook.com/groups/Boolean.Strings/members/
Suzy Tonini’s co-workers https://www.facebook.com/search/536310079/employees/intersect/
Shane McCusker’s events https://www.facebook.com/search/699526870/events
Irina Shamaeva’s past events https://www.facebook.com/search/511207059/events-joined/in-past/date/events/intersect/
Posts by Phil Tusing https://www.facebook.com/search/553250878/stories-by
Posts commented on by Randy Bailey https://www.facebook.com/search/568699739/stories-commented
Pages liked by Suzy Tonini https://www.facebook.com/search/536310079/pages-liked/intersect

The (dangerous!) tool Facebook Scanner provides a few more ways to run Graph searches.

The Facebook Mastery class on Tuesday, January 16th was sold out. Join us for a repeat on Tuesday, February 20th, and learn many more Facebook hacks, tips, and techniques! Seating is limited.

Least Understood Google Operator

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Arguably, the least understood Google operator is inanchor:

Google’s advanced search documentation has lost the level of detail it used to have just a few years ago. It no longer describes inanchor: and quite a few other operators.

What [ inanchor:keyword ] means, is – search for pages, links (anchors) to which from other pages have the keyword (or key phrase, as in [ inanchor:”key phrase” ]).

It’s a tricky operator! Note that while Google responds to the query, it does not tell us which pages have links responsible for the search results. For example, if somewhere is a page, pointing to your LinkedIn profile, and the link says “Top Professional of the Year”, Google will find the profile by searching for [ inanchor:”Top Professional of the Year” site:linkedin.com/in ] but which page said that great thing about you, we won’t know just by looking at the results. Google used to have the operator link: to look for sites linking to the given one, but it never worked well and is now gone.

Sometimes Google finds pages by keywords in “anchors.” If you don’t see your keyword on a results page, that could be the reason behind it: the word was in an anchor on a different page pointing to this results page. (Of course, there may be other reasons).

If we imagine what links to useful pages and sites can say, we can come up with some interesting use cases:

In the middle example, inanchor: finds unique pages with resumes – these pages have no word “resume” in the page title or URL:

You can find additional interesting inanchorexamples in this post.

As with the minus and all other operators (like intitle:, inurl:. etc.) Google searches for the exact word, so we’ll need to run the guesses for synonyms and variations separately.

Our Advanced Google Workshop was sold out (again!). We’ll schedule another repeat; in the meantime, check out a January 2018 class that will explain How To Search Without Boolean.

How To Get Your Google Search History

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Google keeps your search history (and lots of other data), even if you clear the browser’s cache, and allows to download the history.

Here is how to get your Search History for the last few years in a convenient Excel format.

On your Google Take-Out, select (only) “Searches”.

Google will create an archive of your searches, which you can download as JSON files, a file per quarter of searching.

The next step is to merge the files into one Excel file, with all the searches. A genius piece of software that can help here is OpenRefine (formerly Google Refine). After installing the tool, point it to the JSON files, and get all your searches together. It’s pretty straightforward.

The export will contain:

  1. Boolean Strings,
  2. Timestamp for each String.

Now, for example, using Excel filtering, you can review your X-Ray searches by restricting the “String” column to contain the substring site: . Or, go over your OR, intitle:, or inurl:, searches. (Lots of ORs was there is the last sentence!) You can collect strings that seem worth repeating or modifying, and exchange with your peers.

This technique was most helpful while I was preparing the 3rd edition of the Boolean Book.

The above is a simplified process initially described in this article. OpenRefine was an excellent suggestion!

Curious about advanced Google Search? Please join me for a repeat of the sold-out webinar on Advanced Google, scheduled for January 9, 2018. Seating is strictly limited; register early.

Get Ten Boolean Strings Free

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To Boolean Strings Enthusiasts:

I will publish the 3rd edition of the e-book “300 Best Boolean Strings” in January 2018. In the meantime, you can preorder the book at http://booleanbook.com. (If you were wondering, by now, over six hundred of your colleagues have purchased the book and seem quite happy with it!)

I’ve rolled ten (10) new interesting Boolean Strings from the upcoming edition into a tip sheet that you can preview today. Sign up for the Boolean Strings Network Newsletter, to receive industry news and announcements from SourcingCertification.com, and we’ll email you the tip sheet “10 Fun Strings to Try Today”.


Custom Search for Recently Updated Profiles

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Googling for recently updated profiles is a tricky business. However, Custom Search Engines, with their fascinating capabilities, allow us to set up sorting by date in the settings, making convenient UI to searching for pages that have been recently updated.

If you were wondering, search results for the same query, 1) sorted by relevance and 2) sorted by date, as a rule, will not be the same. It’s not that “sorting by date” just changes the results order – the results from the two searches may overlap but will be, for the most part, different. With the maximum number of results being 100, we can, for example, expect to get a total of 180 results from the same LinkedIn X-Ray sorted both ways.’

Here is a new CSE:

LinkedIn – Recently Updated Profiles

Its default (and the only option) is results sorted by date. Try to run searches on this CSE, compare with the relevance-sorted results on a LinkedIn X-Ray CSE, and see the difference!



X Marks the Spot

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The Twitter Advanced Search Dialog allows to search by city, for example, “San Francisco, CA” – but did you know you can search for a (Latitude, Longitude) location with a radius as small as 0.01 miles?

To find the (Latitude, Longitude) for a spot on the map, right-mouse-click in Google Maps and choose “What’s There?”. As an example, I have copied the numbers for a popular conference and trade-show site in San Francisco, Moscone Center. They are: 37.783256, -122.403952.

If, in the Advanced Twitter Search, we look for locations “near San Francisco, with a radius of 0.005 miles, we will see this syntax:

[ near:”San Francisco, CA” within:0.05mi ]

Now I can replace the City name in this Twitter search string by the (Latitude, Longitude). Let’s also add a keyword, Apple, to look for events involving Apple. Here is the resulting search:

This search is looking for tweets from Moscone Center with the keyword Apple – naturally, results are from Apple’s major developer conference held there, WWDC.

Want to get ready to Source in 2018? Check out our Tools presentation.

2017 Changes to Four Major Tools

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Long ago, in a previous life, I was interviewing at a start-up, and a tired interviewer, noticing that my degree was in Math, sighed and said: “Mathematics is great! The fundamentals stay the same, always. You can count on them not to change”. True! (Obviously, the amount of change at that company was overwhelming at the time). Axioms are true and stay the same, by definition – what we make computers do, on the contrary, is in constant flux. Not only our tools constantly change. Some software tools machine-learn from experience and rewrite their own code to adjust!

I’ll write a post about the new tool concepts, Machine Learning, and Artificial Intelligence in application to Sourcing soon, but this post is not it. Here I’ll run a quick overview of the recent changes in a few familiar tools and sites. I’ll be also updating the Tools page. A lot more Sourcing Tools, their assessments, and “how-to’s”, we will discuss at the once-a-year Productivity Tools Webinar next week (you shouldn’t miss it!)

Here are some notes for hands-on practitioners.

ZoomInfo (which was acquired this year) has redesigned its public profiles, leaving much less “stuff” to X-Ray. Still, we can “catch” email formats for larger companies by X-Raying or via the mentioned Custom Search Engine. With the new ZoomInfo UI, we all can see the names on the previewed records (which wasn’t the case before), but without a paid subscription, the search is too rudimentary.

Conclusion: Too bad about X-Ray – some may consider a subscription.

Indeed has redesigned public profiles, leaving people’s names out. You can only see the names when you are logged in. The change, of course, affects X-Raying. However, after a close study of the information that Indeed shares with Google, we don’t feel it’s important. Indeed.com (surprisingly, and quite differently from LinkedIn) doesn’t make a big effort to make public profiles indexed by the search engines. Some pages, such as jobs posted on Indeed, for example, are not indexed at all – by design!

Conclusion: do use Indeed (and its own search, which is excellent) as one of your sources.

(Side Note: another site that is “unfriendly” to X-Raying is Craigslist).

Starting sometime in 2018, we’ll need to message on Indeed via a subscription. Sounds fine. We can also source the contact info if necessary. It looks like the resume search remains free.

Github has removed public email addresses from logged-out profiles. (That presents a bit of a challenge for tools that scrape the addresses, such as People Aggregators – their data will be harder to update).  If you want to see public emails, log in; you will need to create an account just for that, but any “account” activity is non-existent (unless you write code). Google still “remembers” quite a few public emails that used to be on profiles but their number is diminishing.

Conclusion: combine X-Ray and the logged-in search.

This year, we got a zillion Github-related tools, including one that shows the “dominant” language.

LinkedIn is now Microsoft, but we are not sure yet what this means. And, LinkedIn is a site where pinpointing change is not easy and reporting once a year would be far not enough! Some of my posts this year examine how search algorithms worked at various given points, differently from one month to another. “Higher” paid products such as Recruiter, use a flawed design (“Boolean or company”, etc.) that is hard to digest for most users, myself included.

Conclusion: While searching on LinkedIn, just use Boolean where possible, to avoid confusion. Keep an eye on unexpected LinkedIn interpretations.

LinkedIn continues to expand its data – not just in new memberships. With the introduction of the “open to new opportunity” flag and data, posted content, job applications, etc., LinkedIn continues to acquire data and remains at the core of professional searches, globally. No way it can be “replaced” by another tool.

With (even paid) LinkedIn search powers, unfortunately, diminishing this year, we’ve figured out precise ways to X-Ray LinkedIn – and created the new tool Social List, that we are taking to a new level in January 2018.

What changes have YOU noticed in the tools and sites, as of December 2017, that would be useful to know about?

For tons of information about productivity tools old and new, please check out the Tools Class in our Training Library.




Fascinating: Custom Headline Search

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I must admit that until I started using the search technique that I am about to describe, I did not realize that a significant number of LinkedIn members customize their Headlines. I had expected most members to stay with the default Headline, which is <Job Title> at <Company>. Not true. It won’t be easy to estimate the percentage of customized Headlines but they are quite common.
What members put in Headlines often falls into one of these categories:

  • “I am hiring” <…>
  • “I am open to opportunities” <…>
  • <identifying the person’s desired – rather than actual – role>
  • <identifying the person’s skills – rather than just a job title>; here is an example:

On the captured profile, the job title is a plain “Software Engineer”, while the Headline tells us the person’s skills that stand out.

Clearly, it would be to our advantage if we could search for keywords in LinkedIn Headlines only. We would be finding additional leads via the Headline search. However, LinkedIn does not provide this type of search to any of its account holders – LinkedIn Recruiter included. Perhaps they haven’t thought of that.

X-Raying for profiles on Google may be an approximation of this capability since Google will give higher search results ranking to profiles where keywords appear in Headlines. Still, as we all know, X-Raying is way imprecise.

Here is where a Google Custom Search Engine and some special operators can shine. It turns out that we can search for keywords precisely in a LinkedIn profile Headline any time we use a Custom Search Engine. To achieve that:

1) Create or find an “X-Ray” CSE. Here is one I have just created: LinkedIn Smart X-Ray.
2) Use special Boolean operators, unavailable in google.com, narrowing the search to Headlines only.

Here are example searches that demonstrate the “search in headlines only”operator format.

  1. “Open to new opportunities” – more:p:person-role:open*to*new open to new
  2. “I am hiring” – more:p:person-role:i*am*hiring am hiring
  3. Lists a Gmail address – more:p:person-role:gmail “gmail.com”
  4. Self-identified top skills example (lists the skills, not a job title) – more:p:person-role:django*python django python

Now, you can try your own searches – just use the search engine and follow the format.

Isn’t that cool?


(And, as some of you may have guessed, the above technique is at the foundation of our new tool Social List – which you should try if you haven’t! It offers a 48 hours free trial.)

X-Raying for NOT Job Hoppers

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Recruiters who place highly qualified full-time employees always scan resumes and profiles to see if the person is a “job-hopper”. Most employers assume we won’t be bringing people for interviews if they changed jobs too often in the past for no good reason. We do, too.

However – not too many search systems offer a chance to search for non-job-hoppers. LinkedIn is not an exception. Paid accounts can show the length of the current role and stay at the current company (potentially changing roles), but we can’t query the lengths of past jobs.

X-Raying LinkedIn is tricky, but we can search for any words on a public profile. The profiles have job lengths phrased as “xxx years yyy months“. We can take an advantage of that!

Here is an example search for people who stayed at least 3 years at each job: Example search. I am using a template: [ -year -“2 years” months “years” ].

Google numrange operator comes in handy in searches involving years of experience. Two periods (..) stands for numrange on Google:

  • 3..7 means any number between 3 and 7
  • 8.. means any number that is 8 or larger

Here is a search for job hoppers (not sure why someone will search for those, but someone has recently asked this question on one of Facebook Recruiter groups):

site:linkedin.com/in OR site:linkedin.com/pub -pub.dir “year” months -“2.. years”

Turning this search logic around – we can look for people who have a demonstrated job stability – their jobs lasted 3 years or more – but haven’t stayed at any job longer than 8 years:  Example.

I have used the template: [-year -“2 years” “3..7 years” -“8.. Years”].

Our presentation on Overcoming LinkedIn limitations was sold out. You can get a recording at the Training Library. We’ll repeat the webinar as soon as our schedule allows; stay tuned!

And here is a question for you: how would you X-Ray for people who do not have a current job? (Hint: it’s easy).




Hidden LinkedIn Interpretations

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LinkedIn’s Big Data puts the company in a unique position to create a system of organizations, job titles, skills, and the term relationships – which it used to have ambitious plans to do. I hope they will pick it up! But unfortunately, in the last few years, we are seeing somewhat weak and inconsistent attempts to figure out the data and provide intelligent, semantic search and browsing.

There are apparent LinkedIn limitations, such as:

  1. Commercial search limit for those with a free account – that is quite serious. (We know of a “hack” to overcome that, but it’s not available to everyone).
  2. Inability to search by a group membership and by a zip code and radius in premium accounts. (We know ways around that and will be teaching it shortly).

But I would say that the “worst” LinkedIn limitation, depriving us of matching search results or showing false positives, is its ongoing half-baked interpretation of our search terms.

If we search for vice president, should we expect LinkedIn to find VP and V.P. as titles? Let’s take a look at a few test searches.

The strange numbers of results above come from interpreting.

* a quote by the Python software language creator, Guido van Rossum.

Clumsy term interpretations that we are experiencing on LinkedIn happen because of the

“hidden limitations of underlying abstractions.”

that Guido is talking about. The software attempts to make sense of professional data and provide semantic search – at least a semantic “flavor.” But the interpretation is rarely obvious, has pretty much never been documented in LinkedIn’s Help, and the algorithms change a lot (they changed three times in the last month by my count – each time altering the search results for some queries).

To make its users even more confused, LinkedIn interprets our search terms differently, depending on the account – personal, Sales Navigator, or LinkedIn Recruiter. That results in mismatching numbers of search results across accounts. Sometimes, Recruiter gets more results (but not necessarily the results we want); at other times, the personal account (OR Boolean search) “wins.”

I do hope things will improve. In the meantime,

Changes to back-end algorithms affect all of us, while the changes are hidden from us.

Enough confusion! We’ll go over the hidden limits and straighten it out in the double-webinar “Overcoming LinkedIn Limitations” next Wednesday.