Social Network Analysis: Identification of Communication and Information Dissemination (Case Study of Holywings)

Umar Aditiawarman; Mega Lumbia; Teddy Mantoro; Adamu Abubakar Ibrahim

doi:10.15575/join.v8i1.911

Authors

Umar Aditiawarman Department of Computer Science, Universitas Nusa Putra, Indonesia
Mega Lumbia Department of Computer Science, Universitas Nusa Putra, Indonesia
Teddy Mantoro Department of Computer Science, Sampoerna University, Indonesia
Adamu Abubakar Ibrahim Information and Communication Technology, International Islamic University Malaysia, Indonesia

DOI:

https://doi.org/10.15575/join.v8i1.911

Keywords:

Cluster Analysis, Sentiment Analysis, Social Media, Social Network Analysis

Abstract

Social media especially Twitter has been used by corporation or organization as an effective tool to interact and communicate with the consumers. Holywings is one of the popular restaurants in Indonesia that use social media as a tool to promote and disseminate information regarding their products and services. However, one of their promotional items has gone viral and invited public protests which turned into a trending topic on Twitter for a couple of weeks. Holywings allegedly improperly promoted their products by using the most honorable names, “Muhammad” and “Maria”. Social network analysis of Twitter data is conducted to identify and examine information circulating among the users, which leads to wider public attention and law enforcement. In this study, we focused on the conversation about Holywings on Twitter from 24 June to 31 July 2022. The analysis was carried out using Python to retrieve data and Gephi software to visualize the interactions and the intensity of the network group in viewing the spread of information. The findings reveal the centrality account that caused the news to go viral are the CNN Indonesia (@CNNIndonesia) news media account and Haris Pertama (@knpiharis), with a centrality of 0.161 and 0.282, respectively. There are also 121 groups involved in the conversation with modularity of 0.821.

References

M. Reveilhac, “SSM - Population Health The deployment of social media by political authorities and health experts to enhance public information during the COVID-19 pandemic,” SSM - Popul. heal. , vol. 19, no. April, p. 101165, 2022, doi:10.1016/j.ssmph.2022.101165.

MT Bahri and DS Widhyharto, “The Procedural Face of the Jakarta PSBB Policy Phase II Based on Social Network Analysis (SNA),” J. Policy. Public, no. September, pp. 1–10, 2020.

E. Nurhazizah, RN Ichsan, and S. Widiyanesti, “Analysis of Sentiment and Social Networks on the Dissemination of Vaccination Information on Twitter,” Swabumi, vol. 10, no. 1, pp. 24–35, 2022, doi:10.31294/swabumi.v10i1.12474.

MRA Nasution and M. Hayaty, "Comparison of Accuracy and Processing Time of K-NN and SVM Algorithms in Twitter Sentiment Analysis," J. Inform. , vol. 6, no. 2, pp. 226–235, 2019, doi:10.31311/ji.v6i2.5129.

H. Tuhuteru and U. Moluccan Indonesian Christians Jl Ot Pattimaipauw, "Analysis of Community Sentiment Against Large-Scale Social Restrictions Using the Support Vector Machine Algorithm," Inf. syst. Dev. , vol. 5, no. 2, pp. 7–13, 2020.

D. De Stefano and F. Santelli, “Combining sentiment analysis and social network analysis to explore twitter opinion spreading,” Proc. - Int. conf. Comput. comm. Networks, ICCCN , vol. 2019-July, no. November, 2019, doi:10.1109/ICCCN.2019.8846911.

D. Röchert, G. Neubaum, B. Ross, F. Brachten, and S. Stieglitz, “Opinion-based Homogeneity on YouTube,” Comput. comm. res. , vol. 2, no. 1, pp. 81–108, 2020, doi: 10.5117/ccr2020.1.004.roch.

P. Pascual-Ferrá, N. Alperstein, and DJ Barnett, “Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication,” Disaster Med. Public Health Prep. , pp. 561–569, 2020, doi:10.1017/dmp.2020.347.

S. Bhatnagar and N. Choubey, “Making sense of tweets using sentiment analysis on closely related topics,” Soc. net. anal. Min. , vol. 11, no. 1, pp. 1–11, 2021, doi:10.1007/s13278-021-00752-0.

D. Inayah and FL Purba, "Implementation of Social Network Analysis in Disseminating Corona Virus (Covid-19) Information on Twitter," Semin. Nas. Off. stats. , vol. 2020, no. 1, pp. 292–299, 2021, doi:10.34123/semnasoffstat.v2020i1.573.

BJ Timur, “Catalog: 9302021.35 CENTER FOR STATISTICS OF EAST JAVA PROVINCE,” vol. 14, no. 1, pp. 52–58, 2021.

S. Yum, “Social Network Analysis for Coronavirus (COVID-19) in the United States,” Soc. science. Q. , vol. 101, no. 4, pp. 1642–1647, 2020, doi:10.1111/ssqu.12808.

B. Dahal, SAP Kumar, and Z. Li, “Topic modeling and sentiment analysis of global climate change tweets,” Soc. net. anal. Min. , vol. 9, no. 1, 2019, doi:10.1007/s13278-019-0568-8.

T. Mantoro, MA Ayu, and RT Handayanto, “Machine Learning Approach for Sentiment Analysis in Crime Information Retrieval,” 2020 3rd Int. conf. Comput. Informatics Eng. IC2IE 2020 , pp. 96–100, 2020, doi:10.109/IC2IE50715.2020.9274607.

T. Mantoro, M. Merdianti, and MA Ayu, “Sentiment Analysis of the Papuan Movement on Twitter Using Naïve Bayes Algorithm,” 7th Int. conf. Comput. eng. Dec. ICCED 2021 , pp. 1–5, 2021, doi:10.109/ICCED53389.2021.9664868.

Purnia Silvi Dini, “Indonesian Journal of Computer Science,” STMIK Indonesia. Field , vol. 6, no. 1, p. 62, 2020.

D. Sa'adillah Maylawati, MN Mudyawati, MH Wahisyam, and RA Maulana, “Comparison of Classification Algorithms for Sentiment Analysis on Movie Comments,” Gunung Djati Conf. Ser. , vol. 3, no. April, pp. 71–77, 2021.

MA Ayu, SS Wijaya, and T. Mantoro, “An automatic lexicon generation for Indonesian news sentiment analysis: A case on governor elections in Indonesia,” Indonesia . J. Electr. eng. Comput. science. , vol. 16, no. 3, pp. 1555–1561, 2019, doi:10.11591/ijeecs.v16.i3.pp1555-1561.

H. Suharsa and A. Miftahuddin, "PUBLIC PERCEPTIONS ON ONLINE LEARNING FROM TWITTER'S DIGITAL TRACK: ANALYSIS OF POSITIVE, NEUTRAL, AND SENTIMENTS."

F. Teknik, U. Udayana, B. Jimbaran, and P. Figure, “JITE (Journal of Informatics and Telecommunication Engineering) Classification of Public Figures Sentiment on Twitter using Big Data,” vol. 6, no. July, pp. 157–169, 2022.