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.
Some interesting thought and analysis here on the pending death of adtech with the dawn of GDPR in the EU. I’m hoping that this might help bring about a more humanistic internet as a result.
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.
Bookmarked to read as result of reading.
New metrics specifically for news articles.
I love that there’s research1 going on in this area and it portends some potentially great things for reading, but the devil’s advocate in me can also see a lot of adtech people salivating over the potential dark patterns lurking in such research. I can almost guarantee that Facebook is salivating over this, though to be honest, they’ve really pioneered the field haven’t they, just in a much smaller area of use. Of course I’m also curious if they did or are planning any research in how people read content on social media?
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.
Blame Google, for a start.
Nothing great or new here. Also no real solutions, though knowing some of the history and the problems, does help suggest possible solutions.
h/t to @ajzaleski, bookmarked on April 19, 2018 at 01:17PM
— Andrew Zaleski (@ajzaleski) April 19, 2018