In recent years, it has become easier for many people to post information online in the form of Web articles due to the popularization of high-performance electronic terminals and social networking services (SNSs) such as Twitter. Opportunities for browsing a wide variety of information have also increased. As a result, users often collect insufficient pieces of information from many articles and need to understand the topics contained in the information. However, it is difficult for them to find articles related to the topics that they are interested in and determine topic transitions in the articles. Therefore, this research is aimed at developing novel support for understanding articles related to a topic that a user is interested in and the topic transitions from articles on SNSs. In this paper, we propose a method for extracting topic words related to an article of interest on the basis of an analysis of timelines on Twitter. Moreover, we propose a method for extracting Web articles related to the progress of topics on the basis of an analysis of parts of speech in Web articles. Furthermore, we conducted experiments in order to evaluate the usefulness of the proposed methods and acquired findings from the experimental results.