Predicting and Understanding News Social Popularity with Emotional Salience Features

This paper studies the properties of socially popular news with a focused interest on the emotions conveyed through their headlines. We delve deeply into the notion of emotional salience in news values and extract the emotion intensities features across the valence, joy, anger, fear and sadness dimensions. A novel dataset consisting of 47,611 English news headlines from six publishers that received more than 17 million shares and likes were retrieved using Facebook APIs over ten consecutive months in 2018. In contrast with the conventional knowledge that only high-arousal, negative emotions are associated with viral news, the data revealed that headlines with higher intensities across all five emotion dimensions (including positive, joyful news) are significantly associated with social popularity, though the emotion-popularity correlation patterns differ for different publishers (e.g., daily broadcast vs. politics-slanted publishers). From the predictive experiments, we found that the emotion features had complimentary benefits to existing features, which included strong baselines features and word embedding. The final hybrid model achieved the highest predictive performance (R-2 = .54, tau = .53; F-1 = .44, AUC = .85). Using two additional publishers' data, robustness tests further showed the advantage of the proposed model against a state-of-the-art method: The Guardian (tau = .45 vs. .37) and The New York Times (tau = .46 vs. .32).