| Autor: |
Paletz SBF; College of Information Studies, University of Maryland, College Park, MD, USA., Johns MA; Institute for Systems Research, University of Maryland, College Park, MD, USA., Murauskaite EE; ICONS Project, University of Maryland, College Park, MD, USA., Golonka EM; Applied Research Laboratory for Intelligence and Security, University of Maryland, College Park, MD, USA., Pandža NB; Applied Research Laboratory for Intelligence and Security, University of Maryland, College Park, MD, USA.; Maryland Language Science Center, University of Maryland, College Park, MD, USA., Rytting CA; Applied Research Laboratory for Intelligence and Security, University of Maryland, College Park, MD, USA.; Maryland Language Science Center, University of Maryland, College Park, MD, USA., Buntain C; College of Information Studies, University of Maryland, College Park, MD, USA., Ellis D; Applied Research Laboratory for Intelligence and Security, University of Maryland, College Park, MD, USA. |
| Abstrakt: |
While emotional content predicts social media post sharing, competing theories of emotion imply different predictions about how emotional content will influence the virality of social media posts. We tested and compared these theoretical frameworks. Teams of annotators assessed more than 4000 multimedia posts from Polish and Lithuanian Facebook for more than 20 emotions. We found that, drawing on semantic space theory, modeling discrete emotions independently was superior to models examining valence (positive or negative), activation/arousal (high or low), or clusters of emotions and was on par with but had more explanatory power than a seven basic emotion model. Certain discrete emotions were associated with post sharing, including both positive and negative and relatively lower and higher activation/arousal emotions (e.g., amusement, cute/kama muta, anger, and sadness) even when controlling for number of followers, time up, topic, and Facebook angry reactions. These results provide key insights into better understanding of social media post virality. |
