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Violence Identification in Social Media

  • Julio VizcarraEmail author
  • Ken Fukuda
  • Kouji Kozaki
Conference paper
  • 24 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12032)

Abstract

A knowledge-based methodology is proposed for the identification of type and level of violence presented implicitly in shared comments on social media. The work was focused on the semantic processing taking into account the content and handling comments as excerpts of knowledge. Our approach implements similarity measures, conceptual distances, graph theory algorithms, knowledge graphs and disambiguation processes.

The methodology is composed for four stages. In the (1) “knowledge base construction” the types and levels of violence are described as well as the knowledge graphs’ administration. Mechanisms of inclusion and extraction were developed for the knowledge base’s handling and content understanding. The (2) “social media data collection” retrieves comments and maps the social graph’s structure. In the (3) “knowledge processing stage” the comments are transformed to formal representations as extracts of knowledge (graphs). Finally in the (4) “violence domain identification” the comments are classified by their type and level of violence. The evaluation was carried out comparing our methodology with the baselines: (1) a dataset with comments labeled by crowdFlower users, (2) news from social network Twitter, (3) a similar research and (4) typical lexical matching.

Keywords

Knowledge engineering Conceptual similarity DBpedia Topic identification Violence Social media 

Notes

Acknowledgments

This work was supported in part by Council for Science, Technology and Innovation, “Cross-ministerial Strategic Innovation Promotion Program (SIP), Big-data and AI-enabled Cyberspace Technologies”. (funding agency: NEDO), JSPS KAKENHI Grant Number JP17H01789 and CONACYT.

References

  1. 1.
    Princeton university “about wordnet.” wordnet. Princeton university (2010). http://wordnet.princeton.edu
  2. 2.
    Assembly, G.: Sustainable development goals. SDGs), Transforming our world: the 2030 (2015)Google Scholar
  3. 3.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76298-0_52CrossRefGoogle Scholar
  4. 4.
    Birjali, M., Beni-Hssane, A., Erritali, M.: Machine learning and semantic sentiment analysis based algorithms for suicide sentiment prediction in social networks. Procedia Comput. Sci. 113, 65–72 (2017)CrossRefGoogle Scholar
  5. 5.
    Bond, F., Baldwin, T., Fothergill, R., Uchimoto, K.: Japanese SemCor: a sense-tagged corpus of Japanese. In: Proceedings of the 6th Global WordNet Conference (GWC 2012), pp. 56–63 (2012)Google Scholar
  6. 6.
    Bond, F., Foster, R.: Linking and extending an open multilingual wordnet. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1352–1362 (2013)Google Scholar
  7. 7.
    Cheng, Q., Li, T.M., Kwok, C.L., Zhu, T., Yip, P.S.: Assessing suicide risk and emotional distress in chinese social media: a text mining and machine learning study. J. Med. Internet Res. 19(7), e243 (2017)CrossRefGoogle Scholar
  8. 8.
    Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: Proceedings of the 11th International AAAI Conference on Web and Social Media, ICWSM 2017, pp. 512–515 (2017)Google Scholar
  9. 9.
    Davis, D., Figueroa, G., Chen, Y.S.: SociRank: identifying and ranking prevalent news topics using social media factors. IEEE Trans. Syst. Man Cybern. Syst. 47(6), 979–994 (2016)CrossRefGoogle Scholar
  10. 10.
    Dokuz, A.S., Celik, M.: Discovering socially important locations of social media users. Expert Syst. Appl. 86, 113–124 (2017)CrossRefGoogle Scholar
  11. 11.
    Garimella, K., Morales, G.D.F., Gionis, A., Mathioudakis, M.: Quantifying controversy on social media. ACM Trans. Soc. Comput. 1(1), 3 (2018)CrossRefGoogle Scholar
  12. 12.
    Georgiou, T., El Abbadi, A., Yan, X.: Extracting topics with focused communities for social content recommendation. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 1432–1443. ACM (2017)Google Scholar
  13. 13.
    Isahara, H., Bond, F., Uchimoto, K., Utiyama, M., Kanzaki, K.: Development of the Japanese wordnet (2008)Google Scholar
  14. 14.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014). http://www.aclweb.org/anthology/P/P14/P14-5010
  15. 15.
    Nguyen, T., ODea, B., Larsen, M., Phung, D., Venkatesh, S., Christensen, H.: Using linguistic and topic analysis to classify sub-groups of online depression communities. Multimed. Tools Appl. 76(8), 10653–106762 (2017)CrossRefGoogle Scholar
  16. 16.
    World Health Organization: World health statistics 2015. World Health Organization (2015)Google Scholar
  17. 17.
    Vizcarra, J., Kozaki, K., Ruiz, M.T., Quintero, R.: Content-based visualization system for sentiment analysis on social networks. In: JIST (2018)Google Scholar
  18. 18.
    Xiong, F., Liu, Y., Wang, L., Wang, X.: Analysis and application of opinion model with multiple topic interactions. Chaos Interdisc. J. Nonlinear Sci. 27(8), 083113 (2017)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Yao, H., Xiong, M., Zeng, D., Gong, J.: Mining multiple spatial-temporal paths from social media data. Future Gener. Comput. Syst. 87, 782–791 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Human Augmentation Research CenterNational Institute of Advanced Industrial Science and TechnologyTokyoJapan
  2. 2.Department of Engineering Informatics, Faculty of Information and Communication EngineeringOsaka Electro-Communication UniversityOsakaJapan

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