Connecting Mutually Influencing Bloggers


  • Aditya Pal University of Minnesota
  • Jaya Kawale University of Minnesota


The blogosphere shows the characteristics of a power law distribution where a small set of the bloggers (influentials) get the majority of readership and the vast majority receives little traffic. Blogger recommendation algorithms aim at finding influentials for recommendation, putting bloggers with limited readership at further disadvantage. These bloggers could benefit from mutual endorsement of each other with the eventual goal of forming strong local communities with broader readership. In this paper, we propose a recommendation algorithm to connect blogger pairs with the intent that once connected the bloggers would share a mutually influencing relationship between them. In particular, we compute bloggers' influence profile based on how much she influences her blog friends and recommend bloggers with similar influence profiles. We characterize bloggers into four different groups: global leaders, connectors, local leaders, isolates. Our result shows marginal benefit for isolates and significant benefit for local leaders. Our approach can be instructive in building intelligent recommendation engine for bloggers with limited readership to build strong local communities.




How to Cite

Pal, A., & Kawale, J. (2021). Connecting Mutually Influencing Bloggers. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 574-577. Retrieved from