How are users reading your online content?

How little do users read? This has been a concern for most online publishers and the subject of a study performed by the Nielsen Norman Group, a leading voice in the user experience filed. According to their latest study, on the average Web page, users have time to read at most 28% of the words during an average visit; 20% is more likely.

This means that your users are basically overwhelmed by the huge amount of content they need to filter every day, looking for useful information. This situation has resulted in high bounce rates, users “skimming” web pages and jumping from page to page. This is a turning point for online media and especially for publishers, who need to give users the exact content they are looking for by building online profiles, generating abstracts and semantic maps that help users understand “in a glance” if the web page they have just opened contains the information they are actually looking for.

The common way to make readers get all the information from a webpage in the easiest and fastest way possible is for publishers to add meaningful excerpts, summaries or headlines at the beginning of the web content. Categories and tags are also a good way to make the user understand the content even before reading it. But when dealing with large data sets and pieces of content generated from various sources, maintaining consistency and providing readers with the best user experience needs an automated and intelligent web engine.

More and more professional content owners, especially in the finance space, are using Natural Language Processing techniques to automatically extract categories, tags and generate summaries. The latest trend is adding semantic networks to news articles, so that users can really understand “at a glance” the subject, meaning and relationships described in the full webpage content. MediaServista – The Intelligent Media Platform – provides publishers and users with all the functionality described above. The end goal: making your users understand more with less and engaging them to read more with meaningful meta-data that adds richness to the raw text content.