In times when the trust in the news media is sunken, the comment section is often the way to add and foremost to get additional information on a topic.
The quality of the comments is varying a lot. It costs time scanning through the less interesting comments to find those pearls that really add an interesting point of view, contain valuable background information or link to sources.
This is where CRAWIER steps in. It is a multi-device, multi media platform that provides algorithm based collections of worthwhile news website comments. The platform itself present those dossiers in a distinctive and transparent way in order to provide users with broad and substantially informed insights on the current happenings in the world.
The logo shape references the typical speech bubble that is often used as a symbol for comments in general. It gives the user a quick idea what CRAWIER is all about. The name CRAWIER is a composing out of “crawling” and “dossier”. A reference of the basic concept of crawling news articles and collecting those in dossiers.
The Lekton is used in contrast to the Open Sans. It is also a small reminiscence to the old typewriter journalism. The trispaced glyphs allow a vertical alignment of single letters. Together with a bigger spacing, it evokes that distinctive look for the Headline Tags and the brand itself. The typeface Open Sans is highly legible on screen and at small sizes – an ideal running text font.
CONTENT FOCUSED EXPERIENCE
The basic idea is to make it the interface design simple and usable as possible. No fancy tools, focusing on the necessary features only. Nothing impedes a viewer’s experience of the content. At the same time the goal is to create a distinctive visual impression and to use state of the art interaction design i.e. guiding transitions and bigger white space for the content.
“Everything should be made as simple as possible, but not simpler.”
─ Albert Einstein
An Indicator Tag gives the user a quick idea about why the rank was awarded. The Indicator Tags show only positive results and no rating scale to make it quick and easy to grasp at a glance. This is especially because there are shown several categories as i.e usage of links, commentator’s verification or the writing style.
The browser extension is an upcoming feature. It allows the reader to “support” the algorithm in finding worthwhile comments. Readers can award or simply suggest comments to the algorithm directly. Through that the algorithm will check these comments with a higher priority. At the same time the algorithm is learning through this input and becomes better in finding worthwhile comments.
POINTS OF INTEREST
This feature enables the user to quickly recognise certain points of interest in comments. The highlighted parts of the text are based on different algorithm analysis methods i.e. text mining. Together with the browser extension it will be possible to combine these results with the input of engaged users.
CRAWL — ANALYSE — RANK
The basic principle of the algorithm involves three steps. First task is to scan certain news websites, and look out for the comment section and collect comments with the related information (i.e. user data, comment rating and text phrases). Then, certain parts of each comment are being analysed by using different methods (i.e. text mining, natural language processing and sentiment analysis). The last step is to rank the comments and to cluster them into meaningful dossiers.