Sentiment Flow Through Hyperlink Networks


  • Mahalia Miller Stanford University
  • Conal Sathi Stanford University
  • Daniel Wiesenthal Stanford University
  • Jure Leskovec Stanford University
  • Christopher Potts Stanford University


How does sentiment flow through hyperlink networks? Earlier work on hyperlink networks has focused on the structure of the network, often modeling posts as nodes in a directed graph in which edges represent hyperlinks. At the same time, sentiment analysis has largely focused on classifying texts in isolation. Here we analyze a large hyperlinked network of mass media and weblog posts to determine how sentiment features of a post affect the sentiment of connected posts and the structure of the network itself. We explore the phenomena of sentiment flow through experiments on a graph containing nearly 8 million nodes and 15 million edges. Our analysis indicates that (1) nodes are strongly influenced by their immediate neighbors, (2) deep cascades lead complex but predictable lives, (3) shallow cascades tend to be objective, and (4) sentiment becomes more polarized as depth increases.




How to Cite

Miller, M., Sathi, C., Wiesenthal, D., Leskovec, J., & Potts, C. (2021). Sentiment Flow Through Hyperlink Networks. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 550-553. Retrieved from