Influence Factorization for identifying authorities in Twitter

Prevalent usage of social media attracted companies and researchers to analyze its dynamics and effects on user behavior. One of the most intriguing aspects of social networks is to identify influencers who are experts on a specific topic. With the identification of these users within the network, many applications can be built for user recommendation, information diffusion modeling, viral marketing, user modeling and many more. In this paper, we aim to identify topic-based experts using a large dataset collected from Twitter. Our proposed approach has three phases: (1) identification of topics on social media posts (more specifically, tweets), (2) user modeling, based on a group of user specific features, and (3) Influence Factorization to identify topical influencers. The main advantage of the proposed method is to identify future influencers as well as current ones on Twitter. Moreover, it is an easy to implement algorithm using Spark MLlib, which can be easily extended to include other user specific features, and compare with other methodologies. The effectiveness of the proposed method is tested on a large dataset that contains tweets of 180K user over 70 day period. The experimental results show that our proposed method identifies influencers successfully when used with a hybrid user specific feature that contains follower count and authenticity information, and is a highly scalable and extensible algorithm.