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Social status refers to the relative position within the society. It is animportant notion in sociology and related research. The problem of measuringsocial status has been studied for many years. Various indicators are proposedto assess social status of individuals, including educational attainment,occupation, and income/wealth. However, these indicators are sometimesdifficult to collect or measure.  We investigate social networks for alternative measures of social status.Online activities expose certain traits of users in the real world. We areinterested in how these activities are related to social status, and how socialstatus can be predicted with social network data. To the best of our knowledge,this is the first study on connecting online activities with social status inreality.  In particular, we focus on the network structure of microblogs in this study.A user following another implies some kind of status. We cast the predictedsocial status of users to the "status" of real-world entities, e.g.,universities, occupations, and regions, so that we can compare and validatepredicted results with facts in the real world. We propose an efficientalgorithm for this task and evaluate it on a dataset consisting of 3.4 millionusers from Sina Weibo. The result shows that it is possible to predict socialstatus with reasonable accuracy using social network data. We also point outchallenges and limitations of this approach, e.g., inconsistence between onlinepopularity and real-world status for certain users. Our findings provideinsights on analyzing online social status and future designs of rankingschemes for social networks.

Predicting Social Status via Social Networks: A Case Study on University, Occupation, and Region