this is the repo for the task used in: Overestimation in the Aggregation of Emotional Intensity of Social Media Content
Abstract: Users on social media are regularly presented with sequences of emotional content in their newsfeeds, which affects their viewpoints and emotions. Could the way users aggregate and remember emotional content from their feeds contribute to the fact emotions are amplified on social platforms? Across five studies (N = 1,051), using experimentally manipulated social media feeds, we found that participants consistently overestimated the average emotional intensity of the individual responses expressed by other users in a sequence (Study 1a). This overestimation led to stronger emotional reactions to the news content that these responses were reacting to (Study 1b). Investigating the mechanism suggested that while there was stronger memory for more emotional responses within a response sequence, we could not find a direct link between memory and overestimation (Study 2). We showed that overestimation was driven mainly by the salience of emotional intensity of different items in the sequence, by replicating the effect using sequences of emotional words (Study 3). We then turned to the consequences of overestimation, showing that overestimation of emotional sequences was uniquely associated with perceiving more intense emotional responses as more representative of how other people would react (Study 4), and with overestimation of the emotionality of the newsfeed as a whole (Study 5). Overestimation of the average individual emotional intensity ratings of a sequence was also predictive of willingness to share articles. This set of findings sheds light on how sampling from newsfeeds amplifies the perception of emotionality.
Each branch has a either a version of the task used to prepare, run or pilot tasks used in this project.