| Sumario: | 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. Statement of Limitations: Several limitations may affect the generalizability of our findings. First, the external validity is constrained because participants were exposed to fictional news articles without background information about the content creators. In real social media settings, prior knowledge of the content creators can influence users' perceptions, potentially reducing the overestimation observed in our study. Second, the number of responses to each news article was randomized, unlike real social media, where response volume can signal public interest and emotional intensity. Third, the random selection of responses may not reflect real-world scenarios, where emotional responses are often more clustered and aligned with the emotional intensity of the content. This could lead to a more accurate estimation of average emotionality than observed in our study. Fourth, the study measured hypothetical willingness to share, leaving open the question of real-world sharing behavior. Fifth, because all events were fictional, we could not assess how overestimation of emotions might affect participants' real beliefs. Finally, responses always matched the valence of the news article, while in real-world discussions, comments often express disagreement or alternative perspectives. This controlled design helped isolate the role of emotional intensity, but it may not fully capture the complexity of online emotional expression.
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