In the self-serving subculture of retweeting for fame, some universities have been hit with repeated requests for free tuition — if a prospective student can spur a storm of retweets.
Ever been haunted by an online ad for an item you researched or bought? Targeted ads were designed to follow you around everywhere. Here’s how to banish them.
Since tracking people took off in the late ’00s, adtech has grown to become a four-dimensional shell game played by hundreds (or, if you include martech, thousands) of companies, none of which can see the whole mess, or can control the fraud, malware and other forms of bad acting that thrive in the midst of it.
And that’s on top of the main problem: tracking people without their knowledge, approval or a court order is just flat-out wrong. The fact that it can be done is no excuse. Nor is the monstrous sum of money made by it.
There’s a lot to unpack here, but it looks like some tremendously valuable links and resources embedded in this article as well. I’ll have to circle back around to both re-read this and delve more deeply in to these pointers.
Prior work established the benefits of server-recorded user engagement measures (e.g. clickthrough rates) for improving the results of search engines and recommendation systems. Client-side measures of post-click behavior received relatively little attention despite the fact that publishers have now the ability to measure how millions of people interact with their content at a fine resolution using client-side logging. In this study, we examine patterns of user engagement in a large, client-side log dataset of over 7.7 million page views (including both mobile and non-mobile devices) of 66,821 news articles from seven popular news publishers. For each page view we use three summary statistics: dwell time, the furthest position the user reached on the page, and the amount of interaction with the page through any form of input (touch, mouse move, etc.). We show that simple transformations on these summary statistics reveal six prototypical modes of reading that range from scanning to extensive reading and persist across sites. Furthermore, we develop a novel measure of information gain in text to capture the development of ideas within the body of articles and investigate how information gain relates to the engagement with articles. Finally, we show that our new measure of information gain is particularly useful for predicting reading of news articles before publication, and that the measure captures unique information not available otherwise.
New metrics specifically for news articles.
I wonder what it would look/feel like to take each of these modalities and apply them individually for long periods of time to everything one read? Or to use them in rotation regardless of the subject being read? Or other permutations? I suppose in general I like to read how I like to read, but now I’m going to be more conscious of what and how I’m doing it all.
Recorded live Saturday, May 13, 2017. The Gang takes nothing off the table as Doc describes a near future of personal APIs and CustomerTech.
In the last portion of the show, Doc leads with some discussion about identity and privacy from the buyer’s perspective. Companies selling widgets don’t necessarily need to collect massive amounts of data about us to sell widgets. It’s the seller’s perspective and the over-reliance on advertising which has created the capitalism surveillance state we’re sadly living within now.
In the closing minutes of the show Steve re-iterated that the show was a podcast, but that it’s now all about streaming and as such, there is no longer an audio podcast version of the show. I’ll have something to say about this shortly for those looking for alternatives, because this just drives me crazy…
We're building an artificial intelligence-powered dystopia, one click at a time, says techno-sociologist Zeynep Tufekci. In an eye-opening talk, she details how the same algorithms companies like Facebook, Google and Amazon use to get you to click on ads are also used to organize your access to political and social information. And the machines aren't even the real threat. What we need to understand is how the powerful might use AI to control us -- and what we can do in response.