It doesn’t take a scientist to know that a Facebook post bursting with party popper emojis or angry face symbols gets more reactions and shares than a flat, factual account of a child’s graduation or an airline’s villainous customer service.
But, University of Maryland researchers trying to understand why posts go viral on social media—including ones with misinformation and conspiracy theories—would like to understand the best methods for tracking emotions on platforms to create the most accurate predictive models.
In a new article published in Science Advances, the team uncovered that when a post expresses highly specific emotions—from anger and love all the way to “kama muta” (Sanskrit for “being moved” or “heartwarming”), wonder, pride and amusement—there is a significant and predictable impact on whether it gets shared. However, tracking broader characteristics of emotions within posts, like the degree to which they were positive or negative, led to less accurate predictions of post sharing.
“There has always been concern about the spread of social media posts,” said lead author Susannah Paletz, associate professor at the College of Information Studies and an affiliate at UMD’s Applied Research Laboratory for Intelligence and Security (ARLIS). “The purpose of this study was to understand the emotion theories that play into social media sharing.”
Co-authors included Ewa M. Golonka, Nick Pandža, C. Anton Rytting and Devin Ellis of ARLIS, Michael Johns of the Institute for Systems Research, Egle E. Murauskaite of the ICONS Project and Cody Buntain of the College of Information Studies.
2023-10-13 11:48:04
Post from phys.org