This “collective intelligence” poses several “design risks” that should be considered when creating a visualization.
This study focuses on the role social commentary (ex: blog comments) may play when users interact with a data visualization. Social features are generally added to engage users and encourage social interactions, but they also, as the study concludes, influence a user’s interpretation of the visualization: “The visualization artifact is no longer considered independently of social content as prior members’ responses and observations becomes attached to the visualization.”
This social influence can work both negatively and positively in causing a “information cascade effect.” The study found that an initial error/bias interpretation would result in more errors in the commentary, and conversely, a more accurate/less-bias interpretation would lead to fewer errors in the commentary. The initial social signal (the first commenter) has a large impact on all future responses.
Designers should be aware of this social factor as they decide how to present their visualizations.
Source: The Impact of Social Information on Visual Judgments, Hullman, et al., 2011