Abstract:

The problem of distinguishing senior users from mute users was solved by the tags in their profiles in social media. Finding the mute seniors is helpful for providing suitable user interface to these users and recommending suitable information for these users, and can reduce these senior users’ burden of the social network. We use Word2vec and LDA to extract users’ features to predict whether the user is a senior citizen or not. This paper uses TF-IDF to compute the tag’s popularity in different age groups, finding that there is distinct difference among different age groups. So tags can be used to predict users’ age group. Experiment results demonstrated that the approaches(using Word2vec to extract features and using random forest or logistic regression to predict the age group) can make accurate prediction on whether a user is a senior user. Its accuracy can achieve 66% without any user generated content or network topology.

Key words: Social network, Mute users, Senior user prediction, User tag