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LUKUMON Gafari
Understanding the dissemination of information (and disinformation) within groups
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My project is broadly interested in understanding and improving the spread of information (and misinformation) within groups. Taking the perspective that the dynamics determining information spread in social groups emerge from aggregated individual processes, preferences, and decisions. Interventions to improve the quantity and quality of information flow can happen at individual or aggregate levels.
The first stage of this project (Axis 1) attempts to understand the relationship between individual-level biases and their impact on the aggregate spread of information by adopting a computational modeling approach. We simulate both organizational settings as well as social media. Axis 2 focuses on "crowd-based idea filtering," a novel technique meant to assist collective innovation. This axis uses behavioral experimentation to explore what types of ideas people are best at spotting (good vs. bad), and secondly, to explore the parameters (and analytic approaches) that might make crowd-based idea filtering more effective. Axis 3 focuses on fighting misinformation on social media by carrying out "in vivo" psychological experimentation on a new Open Source platform that simulates existing platforms such as Twitter, Facebook, and LinkedIn. We will carry out our experiment over (paid) Moroccan users of the platform specifically assessing the impact of "accuracy nudges" which force posters to provide supporting evidence that can inform other platform users about the news items’ accuracy. We are interested in both (a) the extent to which the nudge will decrease sharing of fake or misleading news items and (b) the extent to which the nudge will impact overall user engagement.
These axes are chosen because they are an active area of research and offer intervention solutions to problems facing individuals, organizations, and decision-makers.
