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Opinion dynamics with backfire effect and biased assimilation

Xi Chen (UGent) , Panayiotis Tsaparas, Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)
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Abstract
The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the question of how individual opinions evolve in such a networked environment without grounding in a known reality. The dominant approach to studying this problem uses simple models from the social sciences on how individuals change their opinions when exposed to their social neighborhood, and applies them on large social networks. We propose a novel model that incorporates two known social phenomena: (i) Biased Assimilation: the tendency of individuals to adopt other opinions if they are similar to their own; (ii) Backfire Effect: the fact that an opposite opinion may further entrench someone in their stance, making their opinion more extreme instead of moderating it. To the best of our knowledge this is the first DeGroot-type opinion formation model that captures the Backfire Effect. A thorough theoretical and empirical analysis of the proposed model reveals intuitive conditions for polarization and consensus to exist, as well as the properties of the resulting opinions.
Keywords
Opinion dynamics, polarization, backfire effect, opinion formation model, biased assimilation

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Citation

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MLA
Chen, Xi, et al. “Opinion Dynamics with Backfire Effect and Biased Assimilation.” MVG 2019, 15th International Workshop on Mining and Learning with Graphs, Proceedings, 2019.
APA
Chen, X., Tsaparas, P., Lijffijt, J., & De Bie, T. (2019). Opinion dynamics with backfire effect and biased assimilation. In MVG 2019, 15th International Workshop on Mining and Learning with Graphs, Proceedings. Anchorage, Alaska - US.
Chicago author-date
Chen, Xi, Panayiotis Tsaparas, Jefrey Lijffijt, and Tijl De Bie. 2019. “Opinion Dynamics with Backfire Effect and Biased Assimilation.” In MVG 2019, 15th International Workshop on Mining and Learning with Graphs, Proceedings.
Chicago author-date (all authors)
Chen, Xi, Panayiotis Tsaparas, Jefrey Lijffijt, and Tijl De Bie. 2019. “Opinion Dynamics with Backfire Effect and Biased Assimilation.” In MVG 2019, 15th International Workshop on Mining and Learning with Graphs, Proceedings.
Vancouver
1.
Chen X, Tsaparas P, Lijffijt J, De Bie T. Opinion dynamics with backfire effect and biased assimilation. In: MVG 2019, 15th International Workshop on Mining and Learning with Graphs, Proceedings. 2019.
IEEE
[1]
X. Chen, P. Tsaparas, J. Lijffijt, and T. De Bie, “Opinion dynamics with backfire effect and biased assimilation,” in MVG 2019, 15th International Workshop on Mining and Learning with Graphs, Proceedings, Anchorage, Alaska - US, 2019.
@inproceedings{8620712,
  abstract     = {The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the question of how individual opinions evolve in such a networked environment without grounding in a known reality. The dominant approach to studying this problem uses simple models from the social sciences on how individuals change their opinions when exposed to their social neighborhood, and applies them on large social networks.
We propose a novel model that incorporates two known social phenomena: (i) Biased Assimilation: the tendency of individuals to adopt other opinions if they are similar to their own; (ii) Backfire Effect: the fact that an opposite opinion may further entrench someone in their stance, making their opinion more extreme instead of moderating it. To the best of our knowledge this is the first DeGroot-type opinion formation model that captures the Backfire Effect. A thorough theoretical and empirical analysis of the proposed model reveals intuitive conditions for polarization and consensus to exist, as well as the properties of the resulting opinions.},
  author       = {Chen, Xi and Tsaparas, Panayiotis and Lijffijt, Jefrey and De Bie, Tijl},
  booktitle    = {MVG 2019, 15th International Workshop on Mining and Learning with Graphs, Proceedings},
  keywords     = {Opinion dynamics,polarization,backfire effect,opinion formation model,biased assimilation},
  language     = {eng},
  location     = {Anchorage, Alaska - US},
  pages        = {7},
  title        = {Opinion dynamics with backfire effect and biased assimilation},
  url          = {http://www.mlgworkshop.org/2019/papers/MLG2019_paper_4.pdf},
  year         = {2019},
}