Diversity-Seeking Users and Their Influence on Social News Sites

Social news sites where users actively engage in reading, discussing, and sharing news with their network can serve as a rich dataset for observing and analyzing the behavior of online social news consumption. In this paper, we combine machine learning and network analysis of users’ textual contents and network characteristics to propose metric that measures user’s degree of seeking diversity in a social new site. Our results reveal that the proposed metric serve to identify influential users who span structural holes and promote to create smaller information network. We discuss this result using a dataset of Huffington Post articles from the Politics section containing over 43,000 articles and activities of over 35,000 users.