Database Support for Online Social Network Analysis
Resumo
Online Social Networks (OSNs) have become part of our daily life, contributing to the vast amount of data generated online. The OSNs' data contribute to obtaining information about our society in various areas, such as misinformation, politics, marketing, and engagement metrics. OSN is a comparatively new concept, without a generic model, with problem-specific models used for the studies of it. This article demonstrates the use of logical models to analyze data from different OSNs, based on a generic conceptual model that reflects a general definition of social networks. These examples aim to show the feasibility and advantages of using the generic model as a basis for specific OSN models, to support the analyses performed from the data extracted.
Palavras-chave:
Online Social Networks, Databases, Analysis
Referências
Almabdy, S. (2018). Comparative analysis of relational and graph databases for social networks. In 2018 1st International Conference on Computer Applications & Information Security (ICCAIS), pages 1–4.
Ansari, M. Z., Aziz, M. B., Siddiqui, M. O., Mehra, H., and Singh, K. P. (2020). Analysis of political sentiment orientations on twitter. Procedia Computer Science, 167:1821–1828.
Bamiro, B. and Assayad, I. (2021). Data-based automatic covid-19 rumors detection in social networks. In Proceedings of the 4th International Conference on Networking, Information Systems & Security, NISS2021, New York, NY, USA. Association for Computing Machinery.
Bao, Z., Tay, Y., and Zhou, J. (2013). sonschema: A conceptual schema for social networks. In Conceptual Modeling: 32th International Conference, ER 2013, Hong-Kong, China, November 11-13, 2013. Proceedings 32, pages 197–211. Springer.
Belloir, N., Ouerdane,W., and Pastor, O. (2022). Characterizing fake news: A conceptual modeling-based approach. In Ralyté, J., Chakravarthy, S., Mohania, M., Jeusfeld, M. A., and Karlapalem, K., editors, Conceptual Modeling, pages 115–129, Cham. Springer International Publishing.
Breslin, J. G., Harth, A., Bojars, U., and Decker, S. (2005). Towards semantically-interlinked online communities. In Gómez-Pérez, A. and Euzenat, J., editors, The Semantic Web: Research and Applications, pages 500–514, Berlin, Heidelberg. Springer Berlin Heidelberg.
Burbach, L., Halbach, P., Ziefle, M., and Calero Valdez, A. (2019). Who shares fake news in online social networks? In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’19, page 234–242, New York, NY, USA. Association for Computing Machinery.
Fan, J., Qiu, J., Li, Y., Meng, Q., Zhang, D., Li, G., Tan, K.-L., and Du, X. (2018a). Octopus: An online topic-aware influence analysis system for social networks. In 2018 IEEE 34th International Conference on Data Engineering (ICDE), pages 1569–1572.
Fan, J., Qiu, J., Li, Y., Meng, Q., Zhang, D., Li, G., Tan, K.-L., and Du, X. (2018b). Octopus: An online topic-aware influence analysis system for social networks. In 2018 IEEE 34th International Conference on Data Engineering (ICDE), pages 1569–1572.
Fan, J., Qiu, J., Li, Y., Meng, Q., Zhang, D., Li, G., Tan, K.-L., and Du, X. (2018c). Octopus: An online topic-aware influence analysis system for social networks. In 2018 IEEE 34th International Conference on Data Engineering (ICDE), pages 1569–1572.
Kardara, M., Kalogirou, V., Papaoikonomou, A., Varvarigou, T., and Tserpes, K. (2015). Socios api: A data aggregator for accessing user generated content from online social networks. In WISE Workshops, volume 9051.
Magalhães Firmino, L. (2022). Comunicação Política, métodos computacionais e pandemia: os três primeiros meses da Covid-19 no Brasil e seu processo de enquadramento no Twitter. PhD thesis, Pontifícia Universidade Católica do Rio De Janeiro PUC-Rio.
Matakos, A., Aslay, C., Galbrun, E., and Gionis, A. (2020). Maximizing the diversity of exposure in a social network. IEEE Transactions on Knowledge and Data Engineering, PP:1–1.
Mccombs, M. (2008). Setting the agenda: The mass media and public opinion.
Passant, A., Bojārs, U., Breslin, J. G., and Decker, S. (2010). The sioc project: Semantically-interlinked online communities, from humans to machines. In Padget, J., Artikis, A., Vasconcelos, W., Stathis, K., da Silva, V. T., Matson, E., and Polleres, A., editors, Coordination, Organizations, Institutions and Norms in Agent Systems V, pages 179–194, Berlin, Heidelberg. Springer Berlin Heidelberg.
Rath, B., Gao, W., and Srivastava, J. (2020). Evaluating vulnerability to fake news in social networks: A community health assessment model. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’19, page 432–435, New York, NY, USA. Association for Computing Machinery.
Taprial, V. and Kanwar, P. (2012). Understanding Social Media. Bookboon.
Thorson, E. (2008). Changing patterns of news consumption and participation. Information, Communication & Society, 11(4):473–489.
Tserpes, K., Papadakis, G., Kardara, M., Papaoikonomou, A., Aisopos, F., Sardis, E., and Varvarigou, T. (2012). Socios: A social media application ontology. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7567 LNCS:574–584.
Vishwakarma, A. and Chugh, M. (2023). Covid-19 vaccination perception and outcome: society sentiment analysis on twitter data in india. 13:84.
Wang, S., Yang, Z., and Chang, Y. (2021). Bringing order to episodes: Mining timeline in social media. Neurocomputing, 450:80–90.
Wycislik, L. and Warchal, L. (2014). A Performance Comparison of Several Common Computation Tasks Used in Social Network Analysis Performed on Graph and Relational Databases, volume 242. Springer Verlag.
Yousefinaghani, S., Dara, R., Mubareka, S., Papadopoulos, A., and Sharif, S. (2021). An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases, 108:256–262.
Yum, S. (2020). Social network analysis for coronavirus (covid-19) in the united states. Social Science Quarterly, 101(4):1642–1647.
Zhang, F., Zhang, Y., Qin, L., Zhang, W., and Lin, X. (2017). When engagement meets similarity: Efficient (k,r)-core computation on social networks. Proc. VLDB Endow., 10(10):998–1009.
Ansari, M. Z., Aziz, M. B., Siddiqui, M. O., Mehra, H., and Singh, K. P. (2020). Analysis of political sentiment orientations on twitter. Procedia Computer Science, 167:1821–1828.
Bamiro, B. and Assayad, I. (2021). Data-based automatic covid-19 rumors detection in social networks. In Proceedings of the 4th International Conference on Networking, Information Systems & Security, NISS2021, New York, NY, USA. Association for Computing Machinery.
Bao, Z., Tay, Y., and Zhou, J. (2013). sonschema: A conceptual schema for social networks. In Conceptual Modeling: 32th International Conference, ER 2013, Hong-Kong, China, November 11-13, 2013. Proceedings 32, pages 197–211. Springer.
Belloir, N., Ouerdane,W., and Pastor, O. (2022). Characterizing fake news: A conceptual modeling-based approach. In Ralyté, J., Chakravarthy, S., Mohania, M., Jeusfeld, M. A., and Karlapalem, K., editors, Conceptual Modeling, pages 115–129, Cham. Springer International Publishing.
Breslin, J. G., Harth, A., Bojars, U., and Decker, S. (2005). Towards semantically-interlinked online communities. In Gómez-Pérez, A. and Euzenat, J., editors, The Semantic Web: Research and Applications, pages 500–514, Berlin, Heidelberg. Springer Berlin Heidelberg.
Burbach, L., Halbach, P., Ziefle, M., and Calero Valdez, A. (2019). Who shares fake news in online social networks? In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’19, page 234–242, New York, NY, USA. Association for Computing Machinery.
Fan, J., Qiu, J., Li, Y., Meng, Q., Zhang, D., Li, G., Tan, K.-L., and Du, X. (2018a). Octopus: An online topic-aware influence analysis system for social networks. In 2018 IEEE 34th International Conference on Data Engineering (ICDE), pages 1569–1572.
Fan, J., Qiu, J., Li, Y., Meng, Q., Zhang, D., Li, G., Tan, K.-L., and Du, X. (2018b). Octopus: An online topic-aware influence analysis system for social networks. In 2018 IEEE 34th International Conference on Data Engineering (ICDE), pages 1569–1572.
Fan, J., Qiu, J., Li, Y., Meng, Q., Zhang, D., Li, G., Tan, K.-L., and Du, X. (2018c). Octopus: An online topic-aware influence analysis system for social networks. In 2018 IEEE 34th International Conference on Data Engineering (ICDE), pages 1569–1572.
Kardara, M., Kalogirou, V., Papaoikonomou, A., Varvarigou, T., and Tserpes, K. (2015). Socios api: A data aggregator for accessing user generated content from online social networks. In WISE Workshops, volume 9051.
Magalhães Firmino, L. (2022). Comunicação Política, métodos computacionais e pandemia: os três primeiros meses da Covid-19 no Brasil e seu processo de enquadramento no Twitter. PhD thesis, Pontifícia Universidade Católica do Rio De Janeiro PUC-Rio.
Matakos, A., Aslay, C., Galbrun, E., and Gionis, A. (2020). Maximizing the diversity of exposure in a social network. IEEE Transactions on Knowledge and Data Engineering, PP:1–1.
Mccombs, M. (2008). Setting the agenda: The mass media and public opinion.
Passant, A., Bojārs, U., Breslin, J. G., and Decker, S. (2010). The sioc project: Semantically-interlinked online communities, from humans to machines. In Padget, J., Artikis, A., Vasconcelos, W., Stathis, K., da Silva, V. T., Matson, E., and Polleres, A., editors, Coordination, Organizations, Institutions and Norms in Agent Systems V, pages 179–194, Berlin, Heidelberg. Springer Berlin Heidelberg.
Rath, B., Gao, W., and Srivastava, J. (2020). Evaluating vulnerability to fake news in social networks: A community health assessment model. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’19, page 432–435, New York, NY, USA. Association for Computing Machinery.
Taprial, V. and Kanwar, P. (2012). Understanding Social Media. Bookboon.
Thorson, E. (2008). Changing patterns of news consumption and participation. Information, Communication & Society, 11(4):473–489.
Tserpes, K., Papadakis, G., Kardara, M., Papaoikonomou, A., Aisopos, F., Sardis, E., and Varvarigou, T. (2012). Socios: A social media application ontology. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7567 LNCS:574–584.
Vishwakarma, A. and Chugh, M. (2023). Covid-19 vaccination perception and outcome: society sentiment analysis on twitter data in india. 13:84.
Wang, S., Yang, Z., and Chang, Y. (2021). Bringing order to episodes: Mining timeline in social media. Neurocomputing, 450:80–90.
Wycislik, L. and Warchal, L. (2014). A Performance Comparison of Several Common Computation Tasks Used in Social Network Analysis Performed on Graph and Relational Databases, volume 242. Springer Verlag.
Yousefinaghani, S., Dara, R., Mubareka, S., Papadopoulos, A., and Sharif, S. (2021). An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases, 108:256–262.
Yum, S. (2020). Social network analysis for coronavirus (covid-19) in the united states. Social Science Quarterly, 101(4):1642–1647.
Zhang, F., Zhang, Y., Qin, L., Zhang, W., and Lin, X. (2017). When engagement meets similarity: Efficient (k,r)-core computation on social networks. Proc. VLDB Endow., 10(10):998–1009.
Publicado
25/09/2023
Como Citar
PINTOR, Paulo; SALGUEIRO, Mariana D. A.; LIFSCHITZ, Sérgio; SCHWABE, Daniel; MOREIRA, José; COSTA, Rogério.
Database Support for Online Social Network Analysis. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 38. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 14-25.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2023.232539.
