Lorenz-Spreen, P., Lewandowsky,
S., Sunstein, C.R. et al.
Nat Hum Behav (2020).
Public opinion is shaped in significant part by online content, spread via social media and curated algorithmically. The current online ecosystem has been designed predominantly to capture user attention rather than to promote deliberate cognition and autonomous choice; information overload, finely tuned personalization and distorted social cues, in turn, pave the way for manipulation and the spread of false information. How can transparency and autonomy be promoted instead, thus fostering the positive potential of the web? Effective web governance informed by behavioural research is critically needed to empower individuals online. We identify technologically available yet largely untapped cues that can be harnessed to indicate the epistemic quality of online content, the factors underlying algorithmic decisions and the degree of consensus in online debates. We then map out two classes of behavioural interventions—nudging and boosting— that enlist these cues to redesign online environments for informed and autonomous choice.
Here is an excerpt:
Another competence that could be boosted to help users deal more expertly with information they encounter online is the ability to make inferences about the reliability of information based on the social context from which it originates. The structure and details of the entire cascade of individuals who have previously shared an article on social media has been shown to serve as proxies for epistemic quality. More specifically, the sharing cascade contains metrics such as the depth and breadth of dissemination by others, with deep and narrow cascades indicating extreme or niche topics and breadth indicating widely discussed issues. A boosting intervention could provide this information (Fig. 3a) to display the full history of a post, including the original source, the friends and public users who disseminated it, and the timing of the process (showing, for example, if the information is old news that has been repeatedly and artificially amplified). Cascade statistics teaches concepts that may take some practice to read and interpret, and one may need to experience a number of cascades to learn to recognize informative patterns.