Characterizing and Identifying Socially Shared Self-Descriptions in Product Reviews


  • Lu Sun UC San Diego
  • F. Maxwell Harper
  • Chia-Jung Lee
  • Vanessa Murdock
  • Barbara Poblete University of Chile



, Social network analysis; communities identification; expertise and authority discovery


Online e-commerce product reviews can be highly influential in a customer's decision-making processes. Reviews often describe personal experiences with a product and provide candid opinions about a product's pros and cons. In some cases, reviewers choose to share information about themselves, just as they might do in social platforms. These descriptions are a valuable source of information about who finds a product most helpful. Customers benefit from key insights about a product from people with their same interests and sellers might use the information to better serve their customers needs. In this work, we present a comprehensive look into voluntary self-descriptive information found in public customer reviews. We analyzed what people share about themselves and how this contributes to their product opinions. We developed a taxonomy of types of self-descriptions, and a machine-learned classification model of reviews according to this taxonomy. We present new quantitative findings, and a thematic study of the perceived purpose descriptions in reviews.




How to Cite

Sun, L., Harper, F. M., Lee, C.-J., Murdock, V., & Poblete, B. (2023). Characterizing and Identifying Socially Shared Self-Descriptions in Product Reviews. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 808-819.