Detecting the Likely Causes Behind the Emotion Spikes of Influential Twitter Users

Abstract

Understanding the causes of spikes in the emotion flow of influential social media users is a key component when analyzing the diffusion and adoption of opinions and trends. Hence, in this work we focus on detecting the likely reasons or causes of spikes within influential Twitter users’ emotion flow. To achieve this, once an emotion spike is identified we use linguistic and statistical analyses on the tweets surrounding the spike in order to reveal the spike’s likely explanations or causes in the form of keyphrases. Experimental evaluation on emotion flow visualization, emotion spikes identification and likely cause extraction for several influential Twitter users shows that our method is effective for pinpointing interesting insights behind the causes of the emotion fluctuation. Implications of our work are highlighted by relating emotion flow spikes to real-world events and by the transversal application of our technique to other types of timestamped text.