Your friends and your topics of interests are intuitively related – people form friendships through mutual interests and at the same time people discover new interests through friends.
Your friends and your topics of interests are intuitively related – people form friendships through mutual interests and at the same time people discover new interests through friends. We are interested in exploring the ways in which social and topical structures can predict each other. We ask two basic questions:How well can a person’s topical interests predict who her friends are?How well can the social connections among the people interested in a topic predict the future popularity of that topic?
In order to answer these questions we study 5 million Twitter users. We study their hashtag usage to identify topical interests and their follower/@-messages to identify two different kinds of social relationships.
To predict whether two users have a social relationship based on their hashtags, we use logistic regression models trained on a wide range of distance measures, measuring topical similarity. Interestingly, one of the most predictive measures is also one of the simplest ones to compute: the size of the smallest hashtag shared by the two users.
Our full model has an accuracy of 77% when predicting follower relationships and 86% when predicting @-message relationships. We also find that predicting strong ties is much easier that predicting weak ties. Our model achieves an accuracy of up to 98% when predicting the strongest pairs, which exchanged more than 20 @-messages.