Research implications of social network analysis on psychology from 2019 to 2021: a systematic review

Lidia Sandra; Timothy Dillan; Sabar Aritonang

doi:10.29210/020221552

Research implications of social network analysis on psychology from 2019 to 2021: a systematic review

Lidia Sandra

⁽¹⁾, Timothy Dillan ⁽²⁾, Sabar Aritonang ⁽³⁾,
(1) Psychology Department Krida Wacana Christian University

Indonesia
(2) Information Techology Department Universitas Islam Indonesia

Indonesia
(3) Information Techology Department Universitas Islam Indonesia

Indonesia

Corresponding Author

DOI : https://doi.org/10.29210/020221552

Full Text:

Language : en

Abstract

This paper aims to give a systematic review on how Social Network Analysis (SNA) Applied in the psychology field of study. Research questions are used to analyse the dataset of journal articles and conference papers collected from IEEE Xplore. 50 papers gathered after applying the advance search of “Social Network Analysis” AND "Psychology" OR "Behaviour prediction" OR "Emotion prediction" OR "Depression" OR "Self-harm" OR "Happiness" ranging from the year 2019 to 2021. Of the 50 journals and papers collected, one journal is the most cited, namely the journal Armstrong R, Hall BJ, Doyle J, and Waters E in 2011. "Cochrane Update. 'Scoping the scope' of a Cochrane Review". The Journal of Public Health. We found that the discussion and uses of SNA in psychology can be divided as to propose a model of early prediction, to propose a model on how to handle and process the data, also analysing psychological factors and problems using the SNA and social computing itself.

Keywords

Social Computing; Systematic Review; Social Network Analysis; Psychology

References

Ader HJ, Mellenbergh GJ, H. D. (2008). “Methodological quality”. Advising on Research Methods: A consultant’s companion. Johannes van Kessel Publishing. ISBN 978-90-79418-02-2.

Al-Rahmi, W. M., Yahaya, N., Alamri, M. M., Aljarboa, N. A., Kamin, Y. Bin, & Moafa, F. A. (2018). A model of factors affecting cyber bullying behaviors among university students. Ieee Access, 7, 2978–2985.

Alashri, S., & Alalola, T. (2020). Functional analysis of the 2020 US elections on Twitter and Facebook using machine learning. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 586–589.

Albota, S. (2020). Linguistic and Psychological Features of the Reddit News Post. 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), 1, 295–299.

Alghamdi, N. S., Mahmoud, H. A. H., Abraham, A., Alanazi, S. A., & García-Hernández, L. (2020). Predicting depression symptoms in an Arabic psychological forum. IEEE Access, 8, 57317–57334.

Alotaibi, N. B. (2019). Cyber bullying and the expected consequences on the students’ academic achievement. IEEE Access, 7, 153417–153431.

Alsheikh, S. S., Shaalan, K., & Meziane, F. (2019). Exploring the effects of consumers’ trust: a predictive model for satisfying buyers’ expectations based on sellers’ behavior in the marketplace. IEEE Access, 7, 73357–73372.

Armstrong, R., Hall, B. J., Doyle, J., & Waters, E. (2011). ‘Scoping the scope’of a cochrane review. Journal of Public Health, 33(1), 147–150.

Baek, J.-W., & Chung, K. (2020). Context deep neural network model for predicting depression risk using multiple regression. IEEE Access, 8, 18171–18181.

Bai, J., Kong, Q., Li, L., Wang, L., & Zeng, D. (2019). Exploring cognitive dissonance on social media. 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), 143–145.

Bie, Y. (2020). Consumer Psychology Analysis of Sinking Users Based on E-Commerce Platform–Taking Pinduoduo as an Example. 2020 International Conference on E-Commerce and Internet Technology (ECIT), 28–31.

Brennecke, J., & Rank, O. (2017). The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study. Research Policy, 46(4), 768–783.

Cardaioli, M., Kaliyar, P., Capuozzo, P., Conti, M., Sartori, G., & Monaro, M. (2020). Predicting Twitter users’ political orientation: an application to the italian political scenario. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 159–165.

Computing, S. (n.d.-a). in Encyclopedia of Information Science and Technology. Third Edition. IGI Global, 2014, p. 6754.

Computing, S. (n.d.-b). introduction to Social Computing special edition of the Communications of the ACM (P. 28-108 Douglas Schuler, Volume 37, Issue 1 (January 1994) (ed.)).

Fang, J., Wang, T., Li, C., Hu, X., Ngai, E., Seet, B.-C., Cheng, J., Guo, Y., & Jiang, X. (2019). Depression prevalence in postgraduate students and its association with gait abnormality. IEEE Access, 7, 174425–174437.

Fu, X., Padmanabhan, M., Kumar, R. G., Basu, S., Dorius, S., & Pavan, A. (2021). Measuring the impact of influence on individuals: roadmap to quantifying attitude. Social Network Analysis and Mining, 11(1), 1–20.

Ghosh, S., Misra, J., Ghosh, S., & Podder, S. (2020). Utilizing social media for identifying drug addiction and recovery intervention. 2020 IEEE International Conference on Big Data (Big Data), 3413–3422.

Giuntini, F. T., de Moraes, K. L., Cazzolato, M. T., de Fátima Kirchner, L., Maria de Jesus, D., Traina, A. J. M., Campbell, A. T., & Ueyama, J. (2021). Modeling and assessing the temporal behavior of emotional and depressive user interactions on social networks. IEEE Access, 9, 93182–93194.

Giuntini, F. T., De Moraes, K. L. P., Cazzolato, M. T., de Fátima Kirchner, L., Maria de Jesus, D., Traina, A. J. M., Campbell, A. T., & Ueyama, J. (2021). Tracing the emotional roadmap of depressive users on social media through sequential pattern mining. IEEE Access, 9, 97621–97635.

Giuntini, F. T., Ruiz, L. P., Kirchner, L. D. F., Passarelli, D. A., Dos Reis, M. D. J. D., Campbell, A. T., & Ueyama, J. (2019). How do I feel? Identifying emotional expressions on Facebook reactions using clustering mechanism. IEEE Access, 7, 53909–53921.

Grandjean, M. (2016). A social network analysis of Twitter: Mapping the digital humanities community. Cogent Arts & Humanities, 3(1), 1171458.

Gupta, P., & Kaushik, B. (2019). Suicidal tendency on social media: a case study. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 273–276.

Hagen, L., Keller, T., Neely, S., DePaula, N., & Robert-Cooperman, C. (2018). Crisis communications in the age of social media: A network analysis of Zika-related tweets. Social Science Computer Review, 36(5), 523–541.

Han, Q., Shi, K., Gu, M., You, L., & Miao, F. (2021). Modeling repeated rumor spreading in coupled social networks. IEEE Access, 9, 89732–89740.

Hockenbury, D. H., & Hockenbury. (2010). psychology. Worth Publisher.

Ishukova, E., Salmanov, V., Kalyabin, A., & Antonenko, A. (2019). Approaches to construct a psychological portrait of users based on analysis of data in open profiles of social networks. 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA), 537–539.

Jadhav, S., Machale, A., Mharnur, P., Munot, P., & Math, S. (2019). Text based stress detection techniques analysis using social media. 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), 1–5.

Jain, V., Chandel, D., Garg, P., & Vishwakarma, D. K. (2020). Depression and Impaired Mental Health Analysis from Social Media Platforms using Predictive Modelling Techniques. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 855–860.

Jiang, C., Li, Y., Tang, Y., & Guan, C. (2021). Enhancing EEG-based classification of depression patients using spatial information. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 566–575.

Kotenko, I., Branitskiy, A., Tishkov, A., & Doynikova, E. (2020). Analysis of Formats of Young People’s Communicative Behavior in Social Network. 2020 24th International Conference on System Theory, Control and Computing (ICSTCC), 439–444.

Lavhare, J. N., & Kulkarni, M. A. (2021). Mental Disorders Detection using Social Networking Sites. 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 113–117.

LD, F. (2008). Psychology: Six perspectives (pp.12–15). Thousand Oaks, CA: Sage Publications.

Li, D., Shen, D., Kou, Y., & Nie, T. (2019). Integrating Sign Prediction With Behavior Prediction for Signed Heterogeneous Information Networks. IEEE Access, 7, 171357–171371.

Ling, C., Zhang, T., & Chen, Y. (2019). Customer purchase intent prediction under online multi-channel promotion: A feature-combined deep learning framework. IEEE Access, 7, 112963–112976.

Liu, D., Fu, Q., Wan, C., Liu, X., Jiang, T., Liao, G., Qiu, X., & Liu, R. (2020). Suicidal ideation cause extraction from social texts. IEEE Access, 8, 169333–169351.

Long, Z., Alharthi, R., & El Saddik, A. (2020). Needfull–A tweet analysis platform to study human needs during the COVID-19 pandemic in new york state. Ieee Access, 8, 136046–136055.

Ma, L., & Wang, Y. (2019). Constructing a semantic graph with depression symptoms extraction from twitter. 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 1–5.

Maryame, N., Najima, D., Hasnae, R., & AJHOUN, R. (2019). Customized data extraction and processing for the prediction of Baby Blues from social media. 2019 1st International Conference on Smart Systems and Data Science (ICSSD), 1–6.

Matsumoto, H., & Ishii, A. (2020). An Analysis Approach of Messaging Mechanism on Social Networking Services. 2020 IEEE International Conference on Big Data (Big Data), 5772–5773.

Mattick, K., Johnston, J., & de la Croix, A. (2018). How to… write a good research question. The Clinical Teacher, 15(2), 104–108.

Mounika, S. N., Kanumuri, P. K., & Manne, S. (2019). Detection of stress levels in students using social media feed. 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 1178–1183.

Mourad, A., Srour, A., Harmanani, H., Jenainati, C., & Arafeh, M. (2020). Critical impact of social networks infodemic on defeating coronavirus COVID-19 pandemic: Twitter-based study and research directions. IEEE Transactions on Network and Service Management, 17(4), 2145–2155.

Narayan, A., & Das, A. (2019). Understanding the Level of Trust in Twitter based on the three Psychological Facets-Predictability, Dependability and Faith. 2019 11th International Conference on Communication Systems & Networks (COMSNETS), 399–402.

Oliseenko, V. D., & Tulupyeva, T. V. (2021). Neural network approach in the task of multi-label classification of user posts in online social networks. 2021 XXIV International Conference on Soft Computing and Measurements (SCM), 46–48.

Otte, E. (n.d.). & Rousseau, R.(2002). Social Network Analysis: A Powerful Strategy, Also for the Information Sciences. Journal of Information Science, 28(6), 443–455.

Oyebode, O., Lomotey, R., & Orji, R. (2021). “I Tried to Breastfeed but…”: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis. IEEE Access, 9, 61074–61089.

Rao, G., Zhang, Y., Zhang, L., Cong, Q., & Feng, Z. (2020). MGL-CNN: a hierarchical posts representations model for identifying depressed individuals in online forums. IEEE Access, 8, 32395–32403.

Samreen, A., Ahmad, A., Zeshan, F., Ahmad, F., Ahmed, S., & Khan, Z. A. (n.d.). A collaborative method for protecting teens against online predators over social networks: A behavioral analysis. 2020.

Shaikh, F. B., Rehman, M., Amin, A., Shamim, A., & Hashmani, M. A. (2021). Cyberbullying behaviour: a study of undergraduate university students. IEEE Access, 9, 92715–92734.

Shvetsova, A. A., & Antropova, M. A. (2020). Content Analysis of Messages in Social Networks, Identification of Suicidal Types. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 81–83.

Sousa, A. M., Almeida, J. M., & Figueiredo, F. (2019). Analyzing and modeling user curiosity in online content consumption: a LastFM case study. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 426–431.

Tao, J., & Xu, H. (2019). Discovering domain-invariant subspace for depression recognition by jointly exploiting appearance and dynamics feature representations. IEEE Access, 7, 186417–186436.

Tareaf, R. Bin, Alhosseini, S. A., & Meinel, C. (2020). Does Personality Evolve? A Ten-Years Longitudinal Study from Social Media Platforms. 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 1205–1213.

Tian, Y., Fan, R., Ding, X., Zhang, X., & Gan, T. (2020). Predicting rumor retweeting behavior of social media users in public emergencies. IEEE Access, 8, 87121–87132.

Uddin, A. H., Bapery, D., & Arif, A. S. M. (2019). Depression analysis of bangla social media data using gated recurrent neural network. 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 1–6.

Wadekar, A. S. (2020). A psychosocial approach to predicting substance use disorder (SUD) among adolescents. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 819–826.

Wang, P., Yan, M., Zhan, X., Tian, M., Si, Y., Sun, Y., Jiao, L., & Wu, X. (2021). Predicting Self-Reported Proactive Personality Classification With Weibo Text and Short Answer Text. IEEE Access, 9, 77203–77211.

Wei, X., Xu, G., Wang, H., He, Y., Han, Z., & Wang, W. (2019). Sensing users’ emotional intelligence in social networks. IEEE Transactions on Computational Social Systems, 7(1), 103–112.

Wilde, M. (2016). Ieee xplore digital library. The Charleston Advisor, 17(4), 24–30.

Xu, L., Li, L., Jiang, Z., Sun, Z., Wen, X., Shi, J., Sun, R., & Qian, X. (2020). A novel emotion lexicon for chinese emotional expression analysis on weibo: using grounded theory and semi-automatic methods. IEEE Access, 9, 92757–92768.

Zhang, H.-L., Jin, R., Zhang, Y., & Tian, Z. (2020). A Public psychological pressure index for social networks. IEEE Access, 8, 23457–23469.

Article Metrics

Abstract Views : 657 times

PDF Downloaded : 0 times

Refbacks

There are currently no refbacks.