Online Community Conflict Decomposition with Pseudo Spatial Permutation

  • Yunmo Chen
  • Xinyue YeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11917)


Online communities are composed of individuals sharing similar opinions or behavior in the virtual world. Facilitated by the fast development of social media platforms, the expansion of online communities have raised many attentions among the researchers, business analysts, and decision makers, leading to a growing list of literature studying the interactions especially conflicts in the online communities. A conflict is often initiated by one community which then attacks the other, leading to an adversarial relationship and worse social impacts. Many studies have examined the origins and process of online community conflict while failing to address the possible spatial effects in their models. In this paper, we explore the prediction of online community conflict by decomposing and analyzing its prediction error taking geography into accounts. Grounding on the previous natural language processing based model, we introduce pseudo spatial permutation to test the model expressiveness with geographical factors. Pseudo spatial permutation employs different geographical distributions to sample from and perturbs the model using the pseudo geographical information to examine the relationship between online community conflict and spatial distribution. Our analysis shows that the pseudo spatial permutation is an efficient approach to robustly test the conflict relation learned by the prediction model, and also reveals the necessity to incorporate geographical information into the prediction. In conclusion, this work provides a different aspect of analyzing the community conflict that does not solely rely on the textual communication.


Online community Spatial permutation Text mining Spatial social network Neural network 



This material is partially based upon work supported by the National Science Foundation under Grant No. 1416509. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceThe Johns Hopkins UniversityBaltimoreUSA
  2. 2.Department of InformaticsNew Jersey Institute of TechnologyNewarkUSA

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