Research institutions can use Scoopinion to scientifically research how we behave with online stories.
We have several methods for researching how texts are read online. The often used methods include access based poll data from large panels (what people click), eye-tracking, ethnography and interviews. None of the mentioned methods provide scientifically robust complete view on human behavior with online texts. Even when several methods are combined, the full understanding about reading motivations and behaviors cannot be explained in a reliable manner.
Traditionally, the understanding about how we read is based on eye-tracking. This set of methods do indeed provide very accurate data about the test subjects behavior with given tasks. However, the methodology is only valid when the test subjects is given a task by the researcher and the test is conducted in a laboratory environment. Further, testing is expensive.
Access based poll data gives a important horizontal view on the reading choices of a large panel of readers. However, it only provides information about what pieces are accessed to, not about how they are read i.e. how users in general behave with different kind of content.
Interviews will only give us information about the expressed motivations and actions, and are always affected by norms and social pressure.
Media ethnography provides good results to many important research questions regarding the discussed sphere of research, but the results are always merely anecdotal and making the research is very expensive.
With Scoopinion add-on, researchers can establish panels to research statistically eye-tracking like behavior of a large number of readers without pre-given task. The data indicates the real behavior: actual screen position and other HCI actions.
Using results of this kind of research setting, and combining the data with eye-tracking results, it is possible to validate hypothesis regarding the goals and motivations of readers in different types of content. Measuring reading behavior with a browser plugin provides reliable and novel information about how stories are read online. To researchers, using Scoopinion is straightforward. It is easy to install to a large groups of readers (an emailed link is enough taken that users use the Chrome or Firefox); if the readers in the panel agree to give data to researcher, this data can be provided to the researcher after a research period in simple form such as excel readable csv. It is trivial to compare readers, articles, authors and magazines if the research question is clear.
The main image of this blog post is a comparison from some Finnish stories during one day. Yellow, green and red bubbles are, respectively, similar stories in different magazines. The x-axis is the average amount of time in microseconds spent per read word in the story. The y-axis is a comparative traffic information number during the day (the most read story of the day gets 100, the upmost story by YLE got 98). The size of the bubble is the average time spent reading the whole story.
Browser end monitoring is not as precise as eye-tracking data. Nevertheless, with large enough samples it is feasible to use this data for improving user interfaces or content production.