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Christopher J. Cameron
Computational Scientist

Christopher Cameron

Christopher joined the Cornell Center for Advanced Computing in 2021 as a member of the Consulting Group. Previously, he contributed analytic and computational social science expertise to a multi-disciplinary team competing in the DARPA "Ground Truth" challenge; performed social network analysis, applied machine learning, and developed data to reveal patterns of inter(social)class communications in online social networks for an NSF-funded project to develop "A new infrastructure for monitoring social class networks"; and developed real-time monitoring tools to test theories of social contagion processes using social media data from Arab Spring for a DOD Minerva grant: "Tracking critical-mass outbreaks in social contagions". Christopher has also worked as a consultant, developing software, creating training materials and educating analysts about statistical methods and software tools for the Centers for Disease Control. His experience includes effective collaboration with researchers from the social and biological sciences, information and computer science, physics, mathemathics, and engineering.

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Areas of Research and Expertise

  • Computational social science
  • Social contagion dynamics
  • Social network analysis
  • Social media analysis
  • Respondent driven sampling (link-tracing sampling)
  • Simulation
  • Scientific computing with Python and R

Publications, Presentations, and Other Writings

  • Sirianni, A. D., Cameron, C. J., Shi, Y., Heckathorn, D. D. (2021) "Bias Decomposition and Estimator Performance in Respondent-Driven Sampling." Social Networks, 64 (January 1, 2021): 109-21. doi:10.1016/j.socnet.2020.08.002.
  • Barash V., Fink C., Cameron, C., Schmidt, A., Dong, W., Macy, M., Kelly J., & Deshpande, A. "A Twitter Social Contagion Monitor." 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), accepted.
  • Berry, G., Cameron C, Park P, Macy M. (2019). "The Opacity Problem in Social Contagion." Social Networks, 56, 93-101. doi:10.1016/j.socnet.2018.09.001
  • Schmidt A., Fink C., Barash V., Cameron C., Macy M. (2018). "Using Spectral Clustering of Hashtag Adoptions to Find Interest-Based Communities" IEEE International Conference on Communications, 2018 doi:10.1109/ICC.2018.8422244
  • Cameron, C. J., & Macy, M. (2017). "The local dynamics of institutional change." Rationality and Society, 29(1), 69-79. doi:10.1177/1043463116685663
  • Heckathorn, D. D., & Cameron, C. J. (2017). "Network sampling." Annual Review of Sociology, 43 (1). doi:10.1146/annurev-soc-060116-053556
  • Shi, Y., Cameron, C. J., & Heckathorn, D. D. (2016). "Model-based and design-based inference reducing bias due to differential recruitment in respondent-driven sampling." Sociological Methods & Research. doi:10.1177/0049124116672682
  • Fink, C., Schmidt, A., Barash, V., Cameron, C., & Macy, M. (2016). "Complex contagions and the diffusion of popular twitter hashtags in Nigeria." Social Network Analysis and Mining, 6(1), 1. https://rdcu.be/7BCh
  • Fink, C., Schmidt, A., Barash, V., Kelly, J., Cameron, C., & Macy, M. (2016). "Investigating the observability of complex contagion in empirical social networks." In ICWSM (pp. 121-130). https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/view/13143
  • Barash, V., Cameron, C. J., Spiller, M. W., & Heckathorn, D. D. (2016). "Respondent-driven sampling-testing assumptions: Sampling with replacement." Journal of Official Statistics, 32(1), 29-73. doi:10.1515/jos-2016-0002
  • Barash, V., Cameron, C. J., & Macy, M. (2012). "Critical phenomena in complex contagions." Social Networks, 34(4), 451-461. doi:10.1016/j.socnet.2012.02.003

Education

Ph.D., Sociology, Cornell University, 2016.
B.A., Sociology with minors in Scientific Computing and Quantitative Research Methods, California State Polytechnic University, Pomona, 2004.

Personal Interests

Ergonomic software design
Scientific workflows
Interpretable machine learning