Peek: less skimming, more reading
I read a lot, but not really. Everyday I scan tenths of articles on the web – blog posts, news articles, essays. I may read word-by-word only 1 out of 100, if any. I find myself skipping full paragraphs and scanning for words in articles, specially in long ones. Is not that I’m not interested, it feels more like I don’t know how to read properly anymore.
The same happened with books. I started many last year, I only finished two. I try hard, but I lose focus and my mind starts wandering. I stare at the book and my eyes follow the lines and send stuff to my brain, and my brain is like “sorry I’m busy.” And then I turn the page and I realize I didn’t get anything about what just happened in the story. I look smart in the subway though.
I was curious to know why this is happening, and what to do with it. I found some answers by reading (!) a fantastic book, The Shallows by Nicholas Carr. Nicholas explains how the human brain has been reconfiguring itself, optimizing to the types of input it receives. When books became popular the human brain adjusted to a calm, linear type of input. With internet, our brain is rewiring for a fast, short, sometimes overlapping bits of information. As he puts it:
“I realized my brain wasn’t just drifting. It was hungry. It was demanding to be fed the way the Net fed it”
And not only hungry, our brain is also optimized for problem solving and being time efficient, also when reading. This is the consequence of our brain being trained everyday to navigate the web; finding the right keywords to search for specific information from a vast repository or scanning and filtering our social feed when we have one minute to spare.
There is another interesting article on how users read on the web. It starts with a summary: “They don’t”, and provides recommendations to design your text on the web according to the new readers’ abilities. In general, it suggests the use of ‘scannable text’, and somehow editorial platforms on the web like Medium provide formatting tools that are aligned with this directions. Having the reading time at the beginning of an article is already a good step towards calming down our brain, setting the expectations.
One of my hypotheses about why I scan articles instead of read them is that I’m not sure if I’m interested in the article, thus I don’t want to spend time reading it word-by-word to find out. Because you know, there are a few other articles to read on the web.
I posed myself a question: Is there a way I can better select what I read, without having to scan the articles? Trying to answer this question I sketched a concept for a reader, or maybe it’s just a feature for a reader. I called it Peek – a mobile reader that helps you filter what you read.
Peek gather articles from selected sources and presents them with peeks, 3 to 5 relevant excerpts. It’s your decision to dive into the long read, or fly over the next article. Peek eliminates skimming – long or short, you’re always reading.
In the main screen, a list of articles from different sources:
By swiping left on an article you reveal the peeks for that article, the most relevant* single sentences of that article. If a peek catches your attention, you can tap to access the full article, right where the excerpt is from. This way you can also read the part that is related to that peek that caught your attention without having to go through the whole piece.
The tool has also a highlighting feature, to mark interesting passages of an article, similar to the way you can highlight content on a Kindle. To highlight a sentence simply double tap:
*The highlighting feature has the double function of generating the peeks, by aggregating the highlights from multiple users and selecting the most relevant ones.
There is risk in relying on user’s input to create the peeks, the core of the concept. However, it seems proven that people use this feature on other platforms such as the Kindle, using a more arduous way of highlighting than a double tap. There may be other ways of defining crowdsourced ‘heat maps’ of interest in long pieces of content on the web (articles, talks, etc.) by looking at other parameters, such as comments – I sometimes use that in Medium to see what’s hot on an article.
I’m not planning to develop this in the near future, but still curious if a tool like this could help me read more, and better.
