Finding Influencers on Twitter with Using Machine Learning Classification Algorithms

Microblog sites are environments where people follow people. With this feature, a microblog site is a convenient environment for spreading an opinion or introducing a new product. The key point is determination of individuals who maximize the spreading. This problem is known as Influence Maximization (IM) and has attracted attention of many researchers. Many studies in the literature have modeled IM problem on graphs for different propagation models such as Independent Cascade (IC) and Linear Threshold (LT). However, microblogs like Twitter have their own features. Many works on IM in Twitter derive new metrics from user and tweet features; apply a greedy approach for selecting influencers. In this study, we adopted different approach for IM problem, and we dealt it as a classification problem. Firstly, we collected data on International Women Day 2018; empirically we labeled the users as either influencer candidates or non-influencers; then we applied classification methods for classifying users into one class with using features of users. By this way, we obtained an influencer candidates set, which is very smaller than entire dataset. Experimental results show that making selection with using same heuristic (namely MF) from the reduced influencer candidates set outperforms making selection from entire dataset