This dissertation investigates emotional contagion in social movements within social media platforms, such as Twitter. The main research question is: How does a protest behavior spread in social networks? The following sub-questions are: (a) What is the dynamic behind the anger contagion in online social networks? (b) What are the key variables for ensuring emotional spread? We gained access to Twitter data sets on protests in Charlotte, NC (2016) and Charlottesville, VA (2017). Although these two protests differ in their triggering points, they have similarities in their macro behaviors during the peak protest times. To understand the influence of anger spread among users, we extracted user mention networks from the data sets. Most of the mentioned users are influential ones, who have a significant number of followers. This shows that influential users occur as the highest in-degree nodes in the core of the networks, and a change in these nodes affects all connected public users/nodes. Then, we examined modularity measures quite high within users’ own communities. After implementing the networks, we ran experiments on the anger spread according to various theories with two main assumptions: (1) Anger is the triggering emotion for protests and (2) Twitter mentions affect distribution of influence in social networks. We found that user connections with directed links are essential for the spread of influence and anger; i.e., the angriest users are the most isolated ones with less number of followers, which signifies their low impact level in the network.