Mapping Netizen Perception on COVID-19 Pandemic: A Preliminary Study of Policy Integration for Pandemic Response in Bandung City
Abstract
Social media is a product of technological advances that can influence how problems are formulated and act as a trigger for social change. Social media can provide data and act as a tool for policymakers to determine the effectiveness and acceptability of the current policy model, including the policy for handling COVID-19. Thus, this research analyzes the netizens’ opinions and responses regarding the policies for handling COVID-19 in Bandung City. Data was collected through the interaction of network data between users on social media to describe the interaction pattern between netizens on Twitter and YouTube about handling COVID-19 in Bandung City. The results revealed that most netizens highlighted the emerging policies in the health sector before moving to other sectors. The highlighted result was proven by the most frequently used word on social media that is vaccines. The research results also had limitations because they did not compare how other cities handled COVID-19 with Bandung City while collecting the data online through interactions and discussions.
Keywords: policy integration, Netizen perception, COVID-19, social media, network visualization
References
[1] Ytre-Arne B. Media use in changing everyday life: how biographical disruption could destabilize media repertoires and public connection. Eur J Commun. 2019;34(5):488–502.
[2] Jansson A, Lovheim M, Paasonen S, Sumiala J. Social media: Implications for everyday life, politics and human agency. Approaching Relig. 2013;3(2):26–37.
[3] Irfan A, Rasli A, Sulaiman Z, Sami A, Qureshi MI. ”The influence of social media on public value: A systematic review of past decade.” Journal of Public Value and Administration Insights. 2019;2(1):1-6.
[4] Jain N, Malviya P, Singh P, Mukherjee S. Twitter Mediated Sociopolitical Communication During the COVID-19 Pandemic Crisis in India. Front Psychol. 2021 Dec;12:784907.
[5] Saqr M, Alamro A. The role of social network analysis as a learning analytics tool in online problem based learning. BMC Med Educ. 2019 May;19(1):160.
[6] Putera PB, Widianingsih I, Ningrum S, Suryanto S, Rianto Y. Overcoming the COVID- 19 pandemic in Indonesia: A science, technology, and innovation (STI) policy perspective. Health Policy Technol. 2022 Sep;11(3):100650.
[7] Sharifi A, Khavarian-Garmsir AR. The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management. Sci Total Environ. 2020 Dec;749:142391.
[8] Valentini C, Romenti S, Kruckeberg D. Language and discourse in social media relational dynamics: A communicative constitution perspective. Int J Commun. 2016;10:4055–4073.
[9] Han E, Tan MM, Turk E, Sridhar D, Leung GM, Shibuya K, et al. Lessons learnt from easing COVID-19 restrictions: An analysis of countries and regions in Asia Pacific and Europe. Lancet. 2020 Nov;396(10261):1525–1534.
[10] Mei C. Policy style, consistency and the effectiveness of the policy mix in China’s fight against COVID-19. Policy Soc. 2020 Jun;39(3):309–325.
[11] Sharifi A, Khavarian-Garmsir AR. The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management. Sci Total Environ. 2020 Dec;749:142391.
[12] Kusumasari B, Munajat ME, Fauzi FZ. ”Measuring global pandemic governance: How countries respond to COVID-19.” J Manag Govern. vol. 0123456789, https://doi.org/10.1007/s10997-022-09647-4
[13] Biesbroek R, Candel JJ. Mechanisms for policy (dis)integration: explaining food policy and climate change adaptation policy in the Netherlands. Policy Sci. 2020;53(1):61– 84.
[14] Mickwitz P, Kivimaa P. Evaluating policy integration: The case of policies for environmentally friendlier technological innovations. Evaluation. 2007;13(1):68–86.
[15] Tosun J, Lang A. ”Coordinating and integrating cross-sectoral policies: A Theoretical Approach.” Paper Prepared for Presentation at the 7th ECPR General Conference, Bordeaux, pp. 1-19, 2013.
[16] Trein P, Meyer I, Maggetti M. The integration and coordination of public policies: A systematic comparative review. J Comp Policy Anal. 2019;21(4):332–349.
[17] Cairo A. “Graphics lies, misleading visuals,” in new challenges for data design. London: Springer; 2015. pp. 103–16.
[18] Engledowl C, Weiland T. Data (Mis)representation and COVID-19: Leveraging misleading data visualizations for developing statistical literacy across grades 6– 16. J Stat Data Sci Edu. 2021;0(0):1–6.
[19] Rabetti Giannella J, Velho L. Data visualization in the time of coronavirus. Strateg Des Res J. 2021;14(1):275–288.
[20] Taher A. The journey of policies during the COVID-19 pandemic in Indonesia: A need of evidence-informed policy. Med J Indones. 2022;31(1):3–6.
[21] Grubmuller V, Götsch K, Krieger B. Social media analytics for future oriented policy making. Eur J Futures Res. 2013;1(1):1–9.
[22] Yeung D. Social media as a catalyst for policy action and social change for health and well-being: Viewpoint. J Med Internet Res. 2018 Mar;20(3):e94.
[23] Bekkers V, Edwards A, de Kool D. Social media monitoring: Responsive governance in the shadow of surveillance? Gov Inf Q. 2013;30(4):335–42.
[24] Mergel I. Building holistic evidence for social media impact. Public Adm Rev. 2017;77(4):489–495.
[25] Zhang B, Vos M. Social media monitoring: Aims, methods, and challenges for international companies. Corp Commun. 2014;19(4):371–383.
[26] Debus M, Tosun J. Political ideology and vaccination willingness: Implications for policy design. Policy Sci. 2021;54(3):477–491.
[27] Choi YJ, Lee J, Paek SY. Public awareness and sentiment toward COVID-19 vaccination in South Korea: Findings from Big Data Analytics. Int J Environ Res Public Health. 2022 Aug;19(16):9914.
[28] Biswas R, Alam T, Househ M, Shah Z. Public sentiment towards vaccination after COVID-19 outbreak in the Arab World. Stud Health Technol Inform. 2022 Jan;289:57–60.
[29] Hill M, Hupe P. Implementing public policy: Governance in theory and in practice. SAGE Publications, Inc; 2002.
[30] Peters BG, Pierre J. Developments in intergovernmental relations: Towards multilevel governance. Policy Polit. 2001;29(2):131–135.
[31] Lo C. ”Between government and governance: Opening the black box of the transformation thesis.” Int J Pub Admin. 2018;41(8):650- https://doi.org/10.1080/01900692.2017.1295261
[32] Candel JJ, Biesbroek R. Toward a processual understanding of policy integration. Policy Sci. 2016;49(3):211–231.
[33] Koliba C, Meek JW, Zia A. Governance networks in public administration and public policy. Volume 349. Public Administration and Public Policy; 2010.
[34] Jordan A, Lenschow A. Policy paper environmental policy integration: A state of the art review. Environ Policy Gov. 2010;20(3):147–158.
[35] Aggrawal N, Arora A. ”Visualization, analysis and structural pattern infusion of DBLP co-Authorship network using Gephi.” In Proceedings on 2016 2nd International Conference on Next Generation Computing Technologies, NGCT, 2017.
[36] Heymann S, Le Grand B. ”Visual analysis of complex networks for business intelligence with gephi.” In Proceedings of the International Conference on Information Visualisation, https://doi.org/10.1109/IV.2013.39
[37] Saqr M, Fors U, Nouri J. Using social network analysis to understand online Problem- Based Learning and predict performance. PLoS One. 2018 Sep;13(9):e0203590.