Emotions in Facebook: Analysing Emotions’ Distribution, Diffusion and Agenda Effect on VG’s Facebook Page

This study examined emotions of news and comments from a famous Norwegian newspaper called Verdens Gang’s (VG) Facebook public page. The data set for analysis contains 84 news items and 7876 comments collected from the new posted on VG’s Facebook page in the last three days in August 2018. Emotions of textual content (news titles and comments) were detected by Senpy which is automatic emotion detector and extract emotions in a detailed level (output specific types of emotion e.g.: happiness, sadness, fear, anger and disgust) rather than polarity level (positive, negative and neutral). After analysing the reactions expressed by public and emotions of comments and news titles, findings suggest that: the main emotion on VG’s Facebook page is happiness, and the emotional strength (total number of emotions in comments of each news) is highly positive correlated with happiness. Findings also suggest that people are more likely to express happiness when the engagement of the news is large. News with the emotion of anger could reach the highest number of users, whereas news with the emotion of fear reach the smallest number of audiences and have the lowest intensity of diffusion. Moreover, anger news gets a comment faster and spread longer than news with other emotions, while happy news will take the longest time to get a feedback from public and has the shortest spreading time span. In addition, more than half part of the news’ emotional agenda corresponds with the public’s emotions; happiness and anger has a stronger agenda affect than fear and sadness