Structure and Dynamics of Signed Citation Networks

Citations are important to track and understand the evolution of human knowledge. At the same time, it is widely accepted that all the citations made in a paper are not equal. However, there is no thorough understanding of how citations are created that explicitly criticize or endorse others. In this paper, we do a detailed study of such citations made within the NLP community by differentiating citations into endorsement (positive), criticism (negative) and neutral categories. We analyse this signed network created between papers and between authors for the first time from a social networks perspective. We make many observations – we find that the citations follow a heavy-tailed distribution and they are created in a way that follows weak balance theory and status theories. Moreover, we find that authors do not change their opinion towards others over time and rarely reciprocate the opinion that they receive. Overall, the paper builds the understanding of the structure and dynamics of positive, negative and neutral citations.