Advertisement

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)

Abstract

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.

Keywords

Online community Spatial permutation Text mining Spatial social network Neural network 

Notes

Acknowledgement

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.

References

  1. 1.
    Dang, L., Chen, Z., Lee, J., Tsou, M.H., Ye, X.: Simulating the spatial diffusion of memes on social media networks. Int. J. Geograph. Inf. Sci. 33, 1–24 (2019)CrossRefGoogle Scholar
  2. 2.
    Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)CrossRefGoogle Scholar
  3. 3.
    Hu, Y., Ye, X., Shaw, S.L.: Extracting and analyzing semantic relatedness between cities using news articles. Int. J. Geograp. Inf. Sci. 31(12), 2427–2451 (2017)CrossRefGoogle Scholar
  4. 4.
    Ioannidis, J.P., Trikalinos, T.A.: An exploratory test for an excess of significant findings. Clin. Trials 4(3), 245–253 (2007)CrossRefGoogle Scholar
  5. 5.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  6. 6.
    Kumar, S., Hamilton, W.L., Leskovec, J., Jurafsky, D.: Community interaction and conflict on the web. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web. pp. 933–943. International World Wide Web Conferences Steering Committee (2018)Google Scholar
  7. 7.
    Odén, A., Wedel, H., et al.: Arguments for Fisher’s permutation test. Ann. Stat. 3(2), 518–520 (1975)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2007).  https://doi.org/10.1561/1500000011CrossRefGoogle Scholar
  9. 9.
    Peters, M.E., et al.: Deep contextualized word representations. In: Proceedings of NAACL (2018)Google Scholar
  10. 10.
    Saxe, A.M., McClelland, J.L., Ganguli, S.: Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint arXiv:1312.6120 (2013)
  11. 11.
    Shi, X., et al.: Detecting events from the social media through exemplar-enhanced supervised learning. Int. J. Digit. Earth 12(9), 1083–1097 (2019)CrossRefGoogle Scholar
  12. 12.
    Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422–1432 (2015)Google Scholar
  13. 13.
    Wang, F., Lu, C.-T., Qu, Y., Yu, P.S.: Collective geographical embedding for geolocating social network users. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 599–611. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57454-7_47CrossRefGoogle Scholar
  14. 14.
    Wang, Y.D., Fu, X.K., Jiang, W., Wang, T., Tsou, M.H., Ye, X.Y.: Inferring urban air quality based on social media. Comput. Environ. Urban Syst. 66, 110–116 (2017)CrossRefGoogle Scholar
  15. 15.
    Wang, Z., Ye, X., Lee, J., Chang, X., Liu, H., Li, Q.: A spatial econometric modeling of online social interactions using microblogs. Comput. Environ. Urban Syst. 70, 53–58 (2018)CrossRefGoogle Scholar
  16. 16.
    Ye, X., Lee, J.: Integrating geographic activity space and social network space to promote healthy lifestyles. SIGSPATIAL Spec. 8(1), 20–33 (2016)CrossRefGoogle Scholar
  17. 17.
    Ye, X., Liu, X.: Integrating social networks and spatial analyses of the built environment. Environ. Plan. B Urban Anal. City Sci. 45, 395–399 (2018) CrossRefGoogle Scholar
  18. 18.
    Ye, X., Sharag-Eldin, A., Spitzberg, B., Wu, L.: Analyzing public opinions on death penalty abolishment. Chin. Sociol. Dialogue 3(1), 53–75 (2018)CrossRefGoogle Scholar
  19. 19.
    Yue, Y., Dong, K., Zhao, X., Ye, X.: Assessing wild fire risk in the united states using social media data. J. Risk Res. 1–15 (2019).  https://doi.org/10.1080/13669877.2019.1569098

Copyright information

© 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

Personalised recommendations