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International Journal of Frontiers in Sociology, 2022, 4(2); doi: 10.25236/IJFS.2022.040215.

Design and Implementation of Data Analysis System of Social Network

Author(s)

Jinglin Bai

Corresponding Author:
Jinglin Bai
Affiliation(s)

Shandong University, Qingdao, China

Abstract

To solve the problem that data collection efficiency of social network analysis technology based on single-mode network structure is low and cannot meet the problem of data information processing of large-scale social network, the social network data analysis system based on the network node centrality theory is proposed. The naive Bayesian classification algorithm is used to improve the traditional data mining mode. The traditional social network data analysis technology is compared with the social network data analysis system proposed in this research from four aspects: multi-platform network data processing response time, data acquisition and processing accuracy, social network data feature condition introduction rate, and data feature condition introduction accuracy. The accuracy of data collection and processing in the traditional social network data analysis system is 89.26%, the response time of multi-platform network data processing is 7.32s, the introduction rate of data feature conditions is 82.65%, and the accuracy rate of data feature conditions is 78.88%. The data processing accuracy of the data analysis system proposed in this research based on the network node centrality theory is 98.99%, the response time of multi-platform network data processing is 1.35s, the data feature condition introduction rate is 97.91%, and the data feature condition introduction accuracy rate is 97.39%. The social network data analysis system proposed in this research is significantly better than the traditional social network data analysis system in all aspects, and its data processing performance is very good especially in multi-platform social network. The results show that the social network data analysis system based on network centrality theory proposed in this research is feasible and can meet the daily application and research requirements of social network data collection, storage, analysis and visual display, which has the advantages of fast data processing speed, high accuracy and wide range of data storage.

Keywords

Naive Bayesian classification algorithm; Network node centrality; Data analysis; Data storage; The social network

Cite This Paper

Jinglin Bai. Design and Implementation of Data Analysis System of Social Network. International Journal of Frontiers in Sociology (2022), Vol. 4, Issue 2: 88-94. https://doi.org/10.25236/IJFS.2022.040215.

References

[1] Zengler K, Zaramela L S. The social network of microorganisms-how auxotrophies shape complex communities. Nature Reviews Microbiology, 2018, 16(6), pp. 383-390.

[2] Kim J, Hastak M. Social network analysis: Characteristics of online social networks after a disaster. International Journal of Information Management, 2018, 38(1), pp. 86-96.

[3] Sijtsema J J, Lindenberg S M. Peer influence in the development of adolescent antisocial behavior: Advances from dynamic social network studies. Developmental Review, 2018, 50, pp. 140-154.

[4] Hagen L, Keller T, Neely S, et al. Crisis communications in the age of social media: A network analysis of Zika-related tweets. Social Science Computer Review, 2018, 36(5), pp. 523-541.

[5] Shen L, Wang S, Dai W, et al. Detecting the Interdisciplinary Nature and Topic Hotspots of Robotics in Surgery: Social Network Analysis and Bibliometric Study. Journal of medical Internet research, 2019, 21(3), pp. e12625. 

[6] Jorgensen T D, Forney K J, Hall J A, et al. Using modern methods for missing data analysis with the social relations model: A bridge to social network analysis. Social networks, 2018, 54, pp. 26-40.

[7] Chang V. A proposed social network analysis platform for big data analytics. Technological Forecasting and Social Change, 2018, 130, pp. 57-68.

[8] Jarvie D. Do Long-time Team-mates Lead to Better Team Performance? A Social Network Analysis of Data from Major League Baseball. Sports Medicine, 2018, 48(11), pp. 2659-2669.

[9] Sun D, Peverill M R, Swanson C S, et al. Structural covariance network centrality in maltreated youth with posttraumatic stress disorder. Journal of psychiatric research, 2018, 98, pp. 70-77.

[10] Akbari, E., Naderi, A., Simons, R.-J., & Pilot, A. (2016). Student engagement and foreign language learning through online social networks. Asian-Pacifc Journal of Second and Foreign Language Education, 1(1), 4. https://doi.org/10.1186/s40862-016-0006-7

[11] Asterhan, C. S. C., & Bouton, E. (2017). Teenage peer-to-peer knowledge sharing through social network sites in secondary schools. Computers & Education, 110, 16–34. https://doi.org/10.1016/j.compedu.2017.03.007

[12] Benson, V., & Filippaios, F. (2015). Collaborative competencies in professional social networking: Are students short changed by curriculum in business education? Computers in Human Behavior, 51, 1331–1339. https://doi.org/10.1016/j.chb.2014.11.031.

[13] Benson, V., Saridakis, G., & Tennakoon, H. (2015). Purpose of social networking use and victimisation: Are there any diferences between university students and those not in HE? Computers in Human Behavior, 51, 867–872. https://doi.org/10.1016/j.chb.2014.11.034

[14] Borrero, D. J., Yousafzai, Y. S., Javed, U., & Page, L. K. (2014). Perceived value of social networking sites (SNS) in students’ expressive participation in social movements. Journal of Research in Interactive Marketing, 8(1), 56–78. https://doi.org/10.1108/JRIM-03-2013-0015.

[15] Borrero, J. D., Yousafzai, S. Y., Javed, U., & Page, K. L. (2014). Expressive participation in Internet social movements: Testing the moderating efect of technology readiness and sex on student SNS use. Computers in Human Behavior, 30, 39–49. https://doi.org/10.1016/j.chb.2013.07.032.

[16] Boyd, D. M., & Ellison, N. B. (2008). Social network sites: Defnition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.x.

[17] Cheung, C. M. K., Chiu, P. Y., & Lee, M. K. O. (2011). Online social networks: Why do students use facebook? Computers in Human Behavior, 27(4), 1337–1343.

[18] Chung, J. E. (2014). Social networking in online support groups for health: How online social networking benefts patients. Journal of Health Communication, 19(6), 639–659. https://doi.org/10.1080/10810730.2012.757396.

[19] Doleck, T., Bazelais, P., & Lemay, D. J. (2017). Examining the antecedents of social networking sites use among CEGEP students. Education and Information Technologies, 22(5), 2103–2123. https://doi.org/10.1007/s10639-016-9535-4.

[20] Eid, M. I. M., & Al-Jabri, I. M. (2016). Social networking, knowledge sharing, and student learning: The case of university students. Computers and Education, 99, 14–27. https://doi.org/10.1016/j.compedu.2016.04.007.

[21] Elphinston, R. A., & Noller, P. (2011). Time to Face It! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. 

[22] Nwagwu, W. E. (2017). Social networking, identity and sexual behaviour of undergraduate students in Nigerian universities. The Electronic Library, 35(3), 534–558. https://doi.org/10.1108/EL-01-2015-0014.

[21] Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17(10), 652–657. https://doi.org/10.1089/cyber.2014.0070.

[22] Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009). The infuence of website characteristics on a consumer’s urge to buy impulsively. Information Systems Research, 20(1), 60–78. https://doi.org/10.1287/isre.1070.0157.

[23] Park, N., Song, H., & Lee, K. M. (2014). Social networking sites and other media use, acculturation stress, and psychological well-being among East Asian college students in the United States. Computers in Human Behavior, 36, 138–146. https://doi.org/10.1016/j.chb.2014.03.037.

[24] Tang, J. H., Chen, M. C., Yang, C. Y., Chung, T. Y., & Lee, Y. A. (2016). Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telematics and Informatics, 33(1), 102–108. https://doi.org/10.1016/j.tele.2015.06.003.

[25] Tashakkori, A., & Teddlie, C. (2003). Issues and dilemmas in teaching research methods courses in social and behavioural sciences: US perspective. International Journal of Social Research Methodology: Theory and Practice, 6(1), 61–77. https://doi.org/10.1080/13645570305055.

[26] Teo, T., Doleck, T., & Bazelais, P. (2017). The role of attachment in Facebook usage: a study of Canadian college students. Interactive Learning Environments, 4820(April), 1–17. https://doi.org/10.1080/10494820.2017.1315602.

[27] Tokunaga, R. S. (2010). Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2009.11.014.

[28] Tower, M., Latimer, S., & Hewitt, J. (2014). Social networking as a learning tool: Nursing students’ perception of efcacy. Nurse Education Today, 34(6), 1012–1017. https://doi.org/10.1016/j.nedt.2013.11.006.

[29] Van Hoof, J. J., Bekkers, J., & Van Vuuren, M. (2014). Son, you’re smoking on Facebook! College students’ disclosures on social networking sites as indicators of real-life risk behaviors. Computers in Human Behavior, 34, 249–257. https://doi.org/10.1016/j.chb.2014.02.008.