LinkedIn: The Logic of the Greater Good

September 26, 2022

I have accepted two factoids about life online:

First, the range of topics searched from my computer systems available to my research team is broad, diverse, and traverses the regular Web, the Dark Web, and what we call the “ghost Web.” As a result, recommendation systems like those in use by Facebook, Google, and Microsoft are laughable. One example is YouTube’s suggesting that one of my team would like an inappropriate beach fashion show here, a fire on a cruise ship here, humorous snooker shots here, or sounds heard after someone moved to America here illustrate the ineffectuality of Google’s smart recommendation software. These recommendations make clear that when smart software cannot identify a pattern or an intentional pattern disrupting click stream, data poisoning works like a champ. (OSINT fans take note. Data poisoning works and I am not the only person harboring this factoid.) Key factoid: Recommendation systems don’t work and the outputs can be poisoned… easily.

Second, profile centric systems like Facebook’s properties or the LinkedIn social network struggle to identify information that is relevant. Thus, we ignore the suggestions for who is hiring people with your profile and the requests to be friends. These are amusing. Here are some anonymized examples. A female in Singapore wanted to connect me with an escort when I was next in Singapore. I interpreted this as a solicitation somewhat ill suited to a 77 year old male who no longer flies to Washington, DC. Forget Singapore. What about a person who is a sales person at a cable company? Or what about a person who does land use planning in Ecuador? What about a person with 19 years experience as a Google “partner”? You get the idea. Pimps and resellers of services which could be discontinued without warning. Key factoid: Recommendations don’t match that I am retired, give lectures to law enforcement and intelligence professionals, and stay in my office in rural Kentucky, with my lovable computers, a not so lovable French bulldog, and my main squeeze for the last 53 years. (Sorry, Singapore intermediary for escorts. Sad smile)

I read a write up in the indigestion inducing New York Times. I am never sure if the stories are accurate, motivated by social bias, written by a persistent fame seeker, or just made up by a modern day Jayson Blair. For info, click here. (You will have to pay to view this exciting story about fiction presented as “real” news.

The story catching my attention today (Saturday, September 24, 2022) has the title “LinkedIn Ran Social Experiments on 20 Million Users over Five Years?” Obviously the author is not familiar with the default security and privacy settings in Windows 10 and that outstanding Windows 11. Data collection both explicit and implicit is the tension in in the warp and woof of the operating systems’ fabric.

Since Microsoft owns LinkedIn, it did not take me long to conclude that LinkedIn like its precursor Plaxo had to be approached with caution, great caution. The write up reports that some Ivory Tower types figured out that LinkedIn ran and probably still runs tests to determine what can get more users, more clicks, and more advertising dollars for the Softies. An academic stalking horse is usually a good idea.

I did spot several comments in the write up which struck me as amusing. Let’s look at a three:

First, consider this statement:

LinkedIn, which is owned by Microsoft, did not directly answer a question about how the company had considered the potential long term consequences of its experiments on users’ employment and economic status.

No kidding. A big tech company being looked at for its allegedly monopolistic behaviors not directly answering a New York Times’ reporters questions. Earth shaking. But the killer gag for me is wanting to know if Microsoft LinkedIn “consider the potential long term consequences of its experiments.” Ho ho ho. Long term at a high tech outfit is measured in 12 week chunks. Sure, there may be a five year plan, but it probably still includes references to Microsoft’s network card business, the outlook for Windows Phone and Nokia, and getting the menus and icons in Office 365 to be the same across MSFT applications, and pitching the security of Microsoft Azure and Exchange as bulletproof. (Remember. There is a weapon called the Snipex Alligator, but it is not needed to blast holes through some of Microsoft’s vaunted security systems I have heard.)

Second, what about this passage from the write up:

Professor Aral of MIT said the deeper significance of the study was that it showed the importance of powerful social networking algorithms — not just in amplifying problems like misinformation but also as fundamental indications or economic conditions like employment and unemployment.

I think a few people understand that corrosive, disintermediating impact of social media information delivered quickly can have an effect. Examples range from flash mob riots to teens killing themselves because social media just does such a bang up job of helping adolescents deal with inputs from strangers and algorithms which highlight the thrill of blue screening oneself. The excitement of asking people who won’t help one find a job is probably less of a downer but failing to land an interview via LinkedIn might spark binge watching of “Friends.”

Third, I loved this passage:

“… If you want to get more jobs, you should be on LinkedIn more.

Yeah, that’s what I call psychological triggering: Be on LinkedIn more. Now. Log on. Just don’t bother to ask me to add you my network of people whom I don’t know because “Stephen E Arnold” on LinkedIn is managed by different members of my team.

Net net: Which is better? The New York Times or Microsoft LinkedIn. You have 10 minutes to craft an answer which you can post on LinkedIn among the self promotions, weird facts, and news about business opportunities like paying some outfit to put you on a company’s Board of Advisors.

Yeah, do it.

Stephen E Arnold, September 26, 2022

Pew Data about Social Media Use: Should I Be Fearful? Answer: Me, No. You? Probably

September 26, 2022

The Pew Research outfit published more data about social media. If you want to look at the factsheet, navigate to this Pew link. I want to focus on one small, probably meaningless item. What interested me was how those in the sample get their news. If I read the snazzy graphics correctly:

  1. 82 percent of those in the sample use YouTube. (Does that make YouTube a monopoly?) Of those YouTube users, 25 percent get their “news” from the Alphabet Google YouTube DeepMind entity.
  2. 30 percent of those in the sample use TikTok, that friendly entity linked with the CCP. Of those TikTok adepts, 10 percent get their news from the Middle Kingdom’s information output and usage intake system.
  3. Other services deliver news, but it is not clear if video is the mechanism. Video interests me because of the Marshall McLuhan hot-cold notion. Video is the digital garden for couch potatoes. Reading is a bit more active, or so the fans of McLuhan would suggest.

Why am I fearful? How about these thoughts, conceived while consuming a cheese sandwich?

  1. Potent mechanisms for injecting shaped or weaponized information into consumers of video news are in the hands of two entities focused on achieving their goals. China is into having the US become subservient to the Middle Kingdom and redress the arrogance Americans have manifested over the years. The AGYD entity wants money and the ability to shape the direction in which it would prefer the users go. My view is their the approach of each entity is the same. The goals are somewhat different.
  2. Most consumers of video and news are unaware of the functionality of weaponized video information. My view is that it is pretty darned good at tearing down and cultivating certain interesting mental frameworks.
  3. Weaponization is trivial, particularly when each AGYD and TikTok can use money to incentivize the individuals and firms producing content for the respective services’ audience.

Net net: Once one pushes into double digit content dependence, a tipping point is something that can cause what appears to be a stable structure to collapse. Can digital information break the camel’s back? For sure. Am I fearful? Nah. Others? Probably not and that increases my concern.

Stephen E Arnold, September 26, 2022

False Expertise: Just Share and Feel Empowered in Intellect

September 15, 2022

I read “Share on Social Media Makes Us Overconfident in Our Knowledge.” The write up states:

Social media sharers believe that they are knowledgeable about the content they share, even if they have not read it or have only glanced at a headline. Sharing can create this rise in confidence because by putting information online, sharers publicly commit to an expert identity. Doing so shapes their sense of self, helping them to feel just as knowledgeable as their post makes them seem.

If the source were a hippy dippy online marketing outfit, I would have ignored the write up. But the research comes from a cow town university. I believe the write up. Would those cowpokes steer me wrong, pilgrim?

I wonder if the researchers will take time out after a Cowboy Kent Rollins cook out to explore the correlation between the boundless expertise of the Silicon Valley “real news” crowd and this group’s dependence on Twitter and similar output channels?

That would make an interesting study because some of the messaging is wild and crazy like a college professor lost in a college bar on dollar beer night.

Stephen E Arnold, September 15, 2022

Site Rot Quantified

July 20, 2022

There’s weird page rot. That was a feature of MySpace and GeoCities. Then there was link rot. That was a feature of my original Web site when I retired. I just stopped remediating dead links. I did not want to do the work myself and I allowed the majority of my team to find their future elsewhere. Ergo, dead links. Too bad, Google.

Now there is site rot.

10% of the Top One Million Sites Are Dead” explains the process of figuring out this number. There are rah rahs for tools and scripts. Good stuff, but my interest is a single number:

892,013

Several early morning thoughts (July 16, 2022):

  • The idea that a million is not a million illustrates the inherent ageing and concomitant deterioration of Internet “things”; namely, Web sites. Why are sites not sites as defined in the write up? Money, laziness, inconsistencies engineered into the information superhighway, or some other reason?
  • Locating sites on the Wayback Machine or whatever it is now called is an exercise in frustration. With sites rotting and Wayback delivering zero content, the data void is significant.
  • The moniker “million” when the count is smaller is another example of the close-enough-for-horse-shoes approach which is popular among some high-tech outfits.

Just remember. I don’t care, and I wonder how many others share my mind set. Good enough.

Stephen E Arnold, July 20, 2022

A Modern Believe It or Not: Phones, Autos, and Safety

June 24, 2022

Auto insurance firm Jerry recently put out a study purporting to prove Android users are safer drivers than those who use iPhones. It almost looks like a desperate, shadow PR move from Google; is the company so insecure it feels compelled to reshape data to “prove” its quantum supremacy? If so, The Next Web thwarts its efforts in the analysis, “Sorry Android Users, You’re Actually NOT the Safest Drivers.” Writer Cate Lawrence examines Jerry’s research then proceeds to poke holes in its conclusions. She writes:

“In its research, Jerry analyzed data collected from 20,000 drivers during 13 million kilometers of driving over 14 days. The data generated an overall driving score and sub-scores for acceleration, speed, braking, turning, and distraction. Then it grouped the results by smartphone operating system and various demographic characteristics. Specifically, the research found that Android users scored an overall 75, trumping iPhone users’ score of 69 in terms of safe driving overall. Sure, they scored higher, but there’s not much of a difference between 69 and 75. And even less between 82 and 84 for accelerating, or 78 and 80 for braking. Overall, I’m not sure these are significant enough differences to instigate any kind of action or triumph. Look, I get it. You number crunch, and you want to make a big assertion to prove a hypothesis, or whatever. … But these numbers are more nice than assertive. The only one that really interested me was distracted driving. This category had the biggest difference, with Android users scoring 74 over iPhone users’ 68, seven points higher. I would have liked some insights on this.”

For example, she suggests, perhaps the iPhone’s apps are more distracting or its users more absorbed in selecting audio material. Alas, the Jerry report is more about pushing its main assertion than in exploring insights.

The study also looked at disparities by educational levels and credit ratings, reporting Android users on the low end of both scales outperformed iPhone users at all levels. Though it failed to explore reasons that may be, Lawrence suggested a couple: Those with less education and with lower credit scores are likely to have lower income levels, and Android phones tend to be more affordable than iPhones. Perhaps lower-income folks have more driving experience, or they are more careful because they cannot afford a ticket. We simply do not know, and neither does Jerry. Instead, the study asserts it comes down to differences in personality between Android and iPhone users. Though it can point to a couple of sources that could be seen to back it up, we agree with the write-up that the connection is a “bit of a stretch.” Sorry Google, your PR arm will have to try harder. Or you could just focus on making a better OS.

Cynthia Murrell, June 24, 2022

Three Facts: Staffing, Old Media, and Zuck Math

April 19, 2022

I spotted three factoids in my newsfeed this morning (April 13, 2022, 0700 am US Eastern). Interesting items reveal the shift taking place in the swirling worlds of digital information.

The first factoid comes from “Cybersecurity at a Crossroads.” I noted:

Security talent shortages are especially pronounced in Germany – 53% of organizations in that country reported that struggles with recruiting and retention resulted in multiple project delays over the past 12 months, compared to 43% across other countries.

The second factoid is derived from two separate articles. The first items comes from the real news source CNBC and its write up “CNN+ Struggles to Lure Viewers in Its Early Days, Drawing Fewer Than 10,000 Daily Users.” The fact may be slushy but it is interesting because 10,000 seems modest in terms of the alleged $300 million spent for the for fee service. The second factoid comes from the paywalled “The TikTok War Didn’t Cause the TikTok Boom.” Here’s the fact: The service had attracted about 1.6 billion users in a recent month. Let’s assume these data are close enough for horseshoes. It’s clear that there is a audience pull difference between old media and new media.

The third factoid is from “Meta Plans to Take Nearly 50% of Creator’s Earnings in ‘Horizon Worlds’.” Zuckbook (formerly Facebook) will take 50 percent of money earned by a person who sells digital artifacts inside the virtual world “Horizon Worlds.” Seems fair to the Zuckbook team I assume.

Observations:

  • Cyber security is morphing into cyber insecurity with no easy fix at this time.
  • Old media lacks the magnetism of the China-linked new media from TikTok.
  • Apple’s changes to app behavior appear to cause Zuckbook to charge fees once associated with a money lender in Florence in the 11th century.

Stephen E Arnold, April 19, 2022

Teams Tracking: Are You Working at Triple Peak?

April 14, 2022

I installed a new version of Microsoft Office. I had to spend some time disabling the Microsoft Cloud, Outlook, and Teams, plus a number of other odds and ends. Who in my office uses Publisher? Sorry, not me. In fact, I knew only one client who used Publisher and that was years ago. We converted that lucky person to an easier to use and more stable product.

We have tried to participate in Teams meetings. Unfortunately the system crashes on my Mac Mini, my Intel workstation, and my AMD workstation. I know the problem is obviously the fault of Apple, Intel, and AMD, but it would be nice if the Teams software would allow me to participate in a meeting. The workaround in my office is to use Zoom. It plays nice with my machines, my mostly secure set up, and the clumsy finger of my 77 year old self.

I provide the context so that you will understand my reaction to “Microsoft Discovers Triple Peak Work Day for Its Remote Employees.” As you may know, Microsoft has been adding features to Teams since the pandemic lit a fire under what was once a software service reserved for financial meetings and some companies that wanted everyone no matter what to be in a digital face to face meeting. Those were super. I did some work for an early video conferencing player. I think it was called Databeam. Yep, perfect for kids who wanted to take a virtual class, not a presentation about the turbine problems at Lockheed Martin.

Microsoft’s featuritis has embraced surveillance. I won’t run down the tools available to an “administrator” with appropriate access to a Teams’ set up for a company. I want to highlight the fact that Microsoft shared with ExtremeTech some information I find fascinating; to wit:

… when employees were in the office, it found “knowledge workers” usually had two periods of peak productivity: before lunch and after lunch. However, with everyone working from home there’s now a third period: late at night, right before bedtime.

My workday has for years begun about 6 am. I chug along until lunch. I then chug along until dinner. Then I chug along until I go to sleep at 10 pm. I like to think that my peak times are from 6 am to 9 am, from 10 am to noon, from 1 30 pm to 3 pm, and from 330 to 6 pm. I have been working for more than 50 years, and I am happy to admit that I am an old fashioned Type A person. Obviously Microsoft does not have many people like me in its sample. The morning, as I recall from my Booz, Allen & Hamilton days, the productive in the morning crowd was a large cohort, thousands in fact. But not in the MSFT sample. These are lazy dogs its seems.

Let’s imagine your are a Type A manager. You have some employees who work from home or from a remote location like a client’s office in Transnistia which you may know as the Pridnestrovian Moldavian Republic. How do you know your remotes are working at their peak times? You monitor the wily creatures: Before lunch, after lunch, and before bed or maybe to a disco in downtown Tiraspol.

How does this finding connect with Teams? With everyone plugged in from morning to night, the Type A manager can look at meeting attendance, participation, side talks, and other detritus sucked up by Teams’ log files. Match up the work with the times. Check to see if there are three ringing bells for each employee. Bingo. Another HR metric to use to reward or marginalize a human personnel asset.

I will just use Zoom and forget about people who do not work when I do.

Stephen E Arnold, April 14, 2022

A Question about Robot Scientist Methods

April 13, 2022

I read “Robot Scientist Eve Finds That Less Than One Third of Scientific Results Are Reproducible.” The write up makes a big deal that Eve (he, her, it, them) examined in a semi automated way 12,000 research papers. From that set 74 were “found” to be super special. Now of the 74, 22 were “found” to be reproducible. I think I am supposed to say, “Wow, that’s amazing.”

I am not ready to be amazed because one question arose:

Can Eve’s (her, her, it, them) results be replicated. What about papers about Shakespeare, what about high energy physics, and what about SAIL Snorkel papers?

Answers, anyone.

I have zero doubt that peer reviewed, often wild and crazy research results were from one of these categories:

  1. Statistics 101 filtered through the sampling, analytic, and shaping methods embraced by the researcher or researchers.
  2. A blend of some real life data with synthetic data generated by a method prized at a prestigious research university.
  3. A collection of disparate data smoothed until suitable for a senior researcher to output a useful research finding.

Why are data from researchers off the track? I believe the quest for grants, tenure, pay back to advisors, or just a desire to be famous at a conference attended by people who are into the arcane research field for which the studies are generated.

I want to point out that one third being sort of reproducible is a much better score than the data output from blue chip and mid tier consulting firms about mobile phone usage, cyber crime systems, and the number of computers sold in the last three month period. Much of that information is from the University of the Imagination. My hunch is that quite a few super duper scholars have a degree in marketing or maybe an MBA.

Stephen E Arnold, April 13, 2022

Online Advertising: A Trigger Warning May Be Needed

March 18, 2022

I read “How Can We Know If Paid Search Advertising Works?” The write up is about Google but it is not about Google in my opinion. A number of outfits selling messages may be following a well worn path: Statistical mumbo jumbo and fear of missing out on a big sale.

Advertising executives once relied on the mostly entertaining methods captured in “Mad Men.” In the digital era, the suits have been exchanged for khakis, shorts, and hoodies. But the objective is the same: Find an advertiser, invoke fear of missing out on a sale, and hauling off the cash. Will a sale happen? Yeah, but one never really knows if it was advertising, marketing, or the wife’s brother in law helping out an very odd younger brother who played video games during the Thanksgiving dinner.

The approach in the article is a mix of common sense and selective statistical analysis. The selective part is okay because the online advertisers engage in selective statistical behavior 24×7.

Here’s a statement from the article I found interesting:

It was almost like people were using the paid links, not to learn about products, but to navigate to the site. In other words, it appeared like selection bias with respect to paid click advertising and arrival at the site was probably baked into their data.

The observation that search sucks or that people use ads because they are lazy are equally valid. The point is that online advertisers a fearful of missing a sale. These lucky professionals will, therefore, buy online ads and believe that sales are a direct result. But there may be some doubt enhanced by the incantations of the Web marketing faction of the organization who say, “Ads are great, but we have to do more search engine optimization.”

A two-fer. The Web site and our products/services are advertised and people buy or “know” about our brand or us. By promoting the Web site we get the bonus sales from the regular, non paid search findability. This argument makes many people happy, particularly the online ad sales team and probably the SEO consulting experts. The real payoff is that the top dog’s anxiety level decreases. He/she/them is/are happier campers.

Identifying causal effects does not happen with wishes.

I am no expert in online advertising. I think the write up suggests that the data used to prove the value of online advertising is shaped. Wow, what a surprise? Why would the leaders in selling online advertising craft a message which may not be anchored in much more than “wishes”.

Money? Yep, money.

Stephen E Arnold, March 18, 2022

What Google Knows about the Honest You

December 10, 2021

I read this quote in a Kleenex story about Google’s lists of popular searches:

“You’re never as honest as you are with your search engine. You get a sense of what people genuinely care about and genuinely want to know — and not just how they’re presenting themselves to the rest of the world.”

The alleged Googler crafting this statement is a data editor. You can read more about the highly selective and unverified Google search trends in “What Google’s Trending Searches Say about America in 2021.”

For me, the statement allows several observations:

  1. A person acting in an unguarded way reveals information not usually disseminated in “guarded” settings; for example, a job interview
  2. The word “honest” implies an unvarnished look at the psycho-social factors within a single person
  3. A collection of data points about the psycho-social aspects of a single person makes it possible to tag, classify, and relate that individual to others. Numerical procedures allow a person or system with access to those data to predict certain behaviors, predispositions, or actions.

Thus, the collection of searches, clicks, and items created by an individual using Google services such as Gmail and YouTube create a palette of color from which a data maestro can paint a picture.

Predestination has never been easier, more automatable, or cheaper to convert into an actionable knowledgebase for smart software. Yep, just simple queries. Useful indeed.

Stephen E Arnold, December 10, 2021

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