Abstract:
This research tests the hypothesis that emotional language drives viewer sharing of online content within news platforms. Text analysis software (LIWC) will be used to analyze the emotion aspects of the top 5 (weekly), most shared, online articles (N = 71) published on the New York Times website. Five emotion language markers will be used as predictors: anger, anxiety, sadness, disgust, and positive emotions. Viewer sharing will be tracked with four measures of top-five ranking: popularity, sustaining, bouncing and linearity. Popularity will be measured with three indicators of sharing: mean ranking, positive skew of ranking (upward trend), and kurtosis of ranking (surging). Sustaining is indicated by the N (number of hours the articles appear on top-five ranking) standard deviation of the ranking, and midimax (upward parabolic function shape). Bouncing is indicated by midimin (downward parabolic) and cubic function shape. Linear is indicated by linearity (rate of change) itself which is the rise and run shape of the chart. These four measures of viewer sharing will be regressed on the five emotional language markers. The result indicated that positive emotion correlate with popularity and linear measure. Anger and anxiety significantly correlate with bouncing, while disgust, with popularity. Sadness does not found to be correlated with any of the measures
