This week we look at IP targeting and geofencing while traveling.

This past week was a vacation where I had a chance to catch up on reading and test a what if scenario. While the reading was informative the what if scenario only served to compound my assumptions and generate new questions.

While reading, the topic of Twitter (drama?) kept creeping into my reading because of a variety of topics. So, this week I tested a what if scenario for basic web browsing to see if websites like Twitter might render different recommendations based on where I was spatially in a fixed temporal window.

This issue is thick with opinions (mine) but I’m also including several links to primary sources along the way. So, let’s get started.

Perhaps you’ve had a super tracker experience. A simple example of a common super tracker experience is one where you search for a product online and find similar or directly related advertising for the product you searched for that seems to follows you around well after the search.

Perhaps you recall my blog post from 2017 about low leveling my Twitter account(s) after a decade. Now, looking back 5 years later, I’m still not sure how I really use Twitter or gain utility.

I don’t actively participate in Twitter in 2022. However, if you had told me the Eternal September Bots / Trolls and NFT reply spam would be this bad, I’d have assumed you were talking about 2000s era email and never looked back again or doubled down on Block Party (I used it successfully in 2021).

You might also recall that I’ve worked in telecom and online everything since the late 1990s. So, I have some opinions as would many others if not most others.

What I’m not, is a candidate for extolling the virtues of strong opinions loosely held aka SOLH pigeonhole or similar neologisms or quips that permeate echo chamber enclaves – indeed the very pigeonholes themselves. So, with that in mind I’m comfortable saying that this newsletter issue and blog posts will (ideally!) not age well over time.

The point?

The point I hope to make is that things around us are in motion and in the narrowly defined scenario that I share below there are deep barrels of widely ignored things and plump juicy assumptions. Do not take my word(s) for it.

Before we get too far into the weeds, check out these links as a deeper dive before we get to the scenario:

Okay. Let’s get to it.

Back in January 2022, I wrote in Belatedly Beckoning Bespoke Beacons that my Twitter archives included this gem just over a decade ago:

Okay, let’s test that one in a very limited way.

Scenario approach:

First, we test from an IP address that’s close to a metropolitan area.

Twitter recommended following these accounts with 2M Followers, 32M Followers, and 25.1M Followers respectively. So, I have no further commentary other than while these are massive following numbers none of these suggestions are in the Top 50 followed accounts on Twitter.

👀

😳

For perspective, to be in the Top 50 would require +37.2M Followers as of this issue of the newsletter.

Second, we test from an IP address that’s +100 miles from a city center.

Twitter recommended following these accounts with 2.6M Followers, 2.7M Followers, and 2.7M Followers respectively. Again, I have no further commentary other than I was nowhere near a city in Ohio or the state of Ohio at the time of this screenshot.

👀

😳

Again, for perspective, to be in the Top 50 would require +37.2M Followers as of this issue of the newsletter.

Okay. Now that I went through this scenario – I wish I had done tests across different mobile devices, different OS devices, and generally fuzzing the browser environment variables as endpoints too. Some questions came to mind…

But I digress.

Trends have been a part of Twitter for well over a decade. The difference now is that Twitter may not make it to the decade mark of being a public company.

So, it’s important to consider how a service you use is making money – not just from the advertising which is common – but as a function of the simulacrum for recommending what you should engage with now or next. Because, the alignment of incentives might not be anymore nefarious than a machine learning model that maps engagement based on the most benign basic banal assumptions when there is scant information or limited input for a so-called recommendation to present as now or next actions to take.

This week I had hoped to link to https://github.com/twitter/the-algorithm but that’s not possible as of this issue since the empty repository has either been moved to private or deleted or it was an internal signal of artistic protest or none of the above – I have no insider information to share.

Instead, I’m embedding a tweet that is as lyrical as it was informative since it linked to a repository that was empty – and a great read from exactly a decade ago on the algorithms in practice at Twitter Trends circa May 2012.

Moments later…

Moments later…

In other words, do not take my word(s) for it. Or, said another way, do not take the surfaced suggestion and suggested word(s) from a service for it either.

My recommendation is to look for primary sources that are less about opinions (like the opinions I’ve shared in this issue) and seek out multiple critiques that rely on widely vetted information. that is anchored in data-driven journalism which also seek to combine primary sources with repeatable research outcomes.

So, of course, this is easier said (1x) than done (10x)? And perhaps this means we are all living in the early stages of a period that can be described or summarized by Brandolini’s law.

As a reminder, I work at Faction. What’s Faction you ask?🤔🤔🤔🤔

Faction provides clientele with cloud data services across hyperscale providers to maximize innovation and multicloud outcomes.🤓☁️📊🚀

We’re hiring at Faction!🎉🤓☁️🚀

To see our current openings click here.⬅️🤓☁️🚀

Or, bookmark unicornjockey.com 🦄🤓☁️🚀

I am linking to my disclosure.