Retrospective Relatedness Reconstruction: Applications to Adaptive Social Networks and Social Sentiment

Shelley D. Dionne, Jin Akaishi, Xiujian Chen, Alka Gupta, Hiroki Sayama, Francis J. Yammarino, Andra Serban, Chanyu Hao, Hadassah J. Head, Benjamin James Bush

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Examination of temporally changing adaptive social networks has been difficult given the need for extensive and usually real-time data collection. Building from interdisciplinary advances, the authors propose a web search engine-based method (called retrospective relatedness reconstruction or 3R) for collecting approximated historical data of temporally changing adaptive social networks. As quantifying relatedness among people in social networks leads to difficulty in assigning proper weights to relationship ties, 3R offers a means for assessing relatedness between people over time. Additionally, 3R can be applied beyond people relatedness to include word associations. To illustrate these two novel contributions, the authors reconstructed the temporal evolution of a social network from 2005 to 2009 of 92 individuals (key leaders) related to the U.S. financial crisis and also examined the temporal evolution of social sentiment (i.e., fear, shame, blame, confidence) related to the same 92 individuals. We found several illustrative cases where temporal changes in centrality and/or sentiment captured actual events related to these individuals during this time period.

Original languageEnglish
Pages (from-to)663-692
Number of pages30
JournalOrganizational Research Methods
Issue number4
Publication statusPublished - 2012 Dec 1
Externally publishedYes


  • adaptive social network
  • temporal evolution

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Strategy and Management
  • Management of Technology and Innovation


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