Abstract
The ubiquity of internet and permanent growth in popularity of Microblogging Social Networks over the past years, has also led to a significant increase in the data uploaded by users to these Microblog sites. However the generated data is dynamic by nature, tied to temporal conditions and the subjectivity of its users. Everyday life experiences, discussions or events have a direct impact on the behaviors reflected in social networks. It is therefore of great importance to asses the impact these interactions are having over a social group. An alternative to answer this is determining how influential a topic is according to the behavior presented on a social network over time. It is then necessary to find and develop methods that can leverage towards this task. This work combines three fields relevant to this kind of data utilization: topic identification, emotion classification and influence determination. Once a topic is identified we first classify it as time specific or long term, then posts relevant to the topic are collected and each one is assigned an emotion label. After processing the stream of posts to favor a time based analysis we propose an Influence Value score which will be given to each topic based on its lifespan, emotion transition and reach in order to quantify how influential a topic is over a social group, specifically from events detected on twitter. In other words we summarize the emotional response towards an event and combine it with temporal variables to determine how influential it is over a social group.