More and more users have been taking various actions to diverse resources referred to by URLs such as news, web pages, images, products, movies as a result of the growth of social media. They are annotating, tweeting in Twitter, reblogging in Tumblr, and Liking in Facebook, etc. Analyses about these diverse actions will be useful for aggregating or integrating diverse resources. In this paper, we view users’ actions to resources as expressing their some interests, and by investigating how their interests are expressed in social media, we get suggestions for aggregations. Our results show that a certain kind of action (such as tagging on Delicious) can be used to make predictions on a different kind of action (such as favorite on Twitter). These analyses will be useful for aggregating or integrating diverse contents on multiple sources. In addition to some experimental analyses, we propose a novel method to predict users’ interests in social media, using time-evolving, multinomial relational data. Our experimental results show that the proposed method significantly outperforms standard tensor analysis and an existing state-of-the-art method (LDA) in prediction tasks.