Identifying Influential Users' Professions via the Microblogs They Forward

For most social media sites, how to find out (influential) users’ professions is an important task. Much work has been conducted to explore this task through mining user-generated textual content or analyzing the social network structure. In this paper, we innovatively solve this task by only examining which microblog messages an influential user has forwarded. First, we define hot microblog messages under two standards and identify them from a large number of candidate messages. Each of the identified messages points to a specific hot event. Next, we group similar hot messages together based on their word similarity, semantic similarity, and forwarders’ similarity. Last, we represent users with the hot messages they forwarded and design an identification method to identify their professions. Moreover, we collect a real-world dataset to conduct experiments and prove that our method performs significantly better than the traditional method.