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Social Network Analysis and Mining

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01-12-2021 | Original Article

Studying leaders & their concerns using online social media during the times of crisis - A COVID case study

Authors: Rahul Goel, Rajesh Sharma

Published in: Social Network Analysis and Mining | Issue 1/2021

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Abstract

Online social media (OSM) has emerged as a prominent platform for debate on a wide range of issues. Even celebrities and public figures often share their opinions on a variety of topics through OSM platforms. One such subject that has gained a lot of coverage on Twitter is the Novel Coronavirus, officially known as COVID-19, which has become a pandemic and has sparked a crisis in human history. In this study, we examine 29 million tweets over three months to study highly influential users, whom we refer to as leaders. We recognize these leaders through social network techniques and analyse their tweets using text analysis. Using a community detection algorithm, we categorize these leaders into four clusters: research, news, health, and politics, with each cluster containing Twitter handles (accounts) of individual users or organizations. e.g., the health cluster includes the World Health Organization (@WHO), the Director-General of WHO (@DrTedros), and so on. The emotion analysis reveals that (i) all clusters show an equal amount of fear in their tweets, (ii) research and news clusters display more sadness than others, and (iii) health and politics clusters are attempting to win public trust. According to the text analysis, the (i) research cluster is more concerned with recognizing symptoms and the development of vaccination; (ii) news and politics clusters are mostly concerned with travel. We then show that we can use our findings to classify tweets into clusters with a score of 96% AUC ROC.

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https://www.tweetbinder.com/blog/covid-19-coronavirus-twitter/

Note: In this work, “leader” represents a Twitter handle, and “cluster” represents a group of Twitter handles.

https://www.tweetbinder.com/blog/covid-19-coronavirus-twitter/

https://developer.twitter.com/en/docs/basics/counting-characters

https://www.tumblr.com/

https://foursquare.com/

http://plus.google.com

http://linkedin.com/

https://www.tweepy.org/

https://github.com/mjockers/syuzhet

https://www.cdc.gov/coronavirus/2019-ncov/about/steps-when-sick.html

https://www.cdc.gov/coronavirus/2019-ncov/travelers/index.html

https://www.cdc.gov/quarantine/air/managing-sick-travelers/ncov-airlines.html

Achrekar H, Gandhe A, Lazarus R, Yu SH, Liu B (2011) Predicting flu trends using twitter data. In: 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS). IEEE, pp 702–707

Alom Z, Carminati B, Ferrari E (2020) A deep learning model for twitter spam detection. Online Soc Netw Med 18:100079CrossRef

Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan), 993–1022

Brammer S, Clark T (2020) Covid-19 and management education: Reflections on challenges, opportunities, and potential futures. Bri J Manag 31(3):453

Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRef

Bruns A, Highfield T, Burgess J (2013) The arab spring and social media audiences: English and arabic twitter users and their networks. Am Behavl Sci 57(7):871–898CrossRef

Carter, M (2014) How twitter may have helped nigeria contain ebola. BMJ: Br Med J (Online) 349

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357CrossRef

Chew C, Eysenbach G (2010) Pandemics in the age of twitter: content analysis of tweets during the 2009 h1n1 outbreak. PloS one 5(11)

COVID C, Global cases by Johns Hopkins Center for Systems Science and Engineering (CSSE). https://coronavirus.jhu.edu/map.html

CSSE J (2020) Coronavirus covid-19 global cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)

Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves. In: Proceedings of the 23rd international conference on Machine learning, pp 233–240

Dein S, Loewenthal K, Lewis CA, Pargament KI (2020) Covid-19, mental health and religion: An agenda for future research

Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

Fernandez AA, Shaw GP (2020) Academic leadership in a time of crisis: The coronavirus and covid-19. J Leadersh Stud 14(1):39–45CrossRef

Flach P (2004) The many faces of roc analysis in machine learning. ICML Tutorial

Frei-Landau R (2020) when the going gets tough, the tough get-creative: Israeli jewish religious leaders find religiously innovative ways to preserve community members’ sense of belonging and resilience during the covid-19 pandemic. Psychological Trauma: Theory, Research, Practice, and Policy 12(S1):S258

Giachanou A, Crestani F (2016) Like it or not: A survey of twitter sentiment analysis methods. ACM Comput Surv (CSUR) 49(2):1–41CrossRef

Goel, R., Ford, L.J., Obrizan, M., Sharma, R.: Covid-19 and the stock market: evidence from twitter. arXiv preprint arXiv:2011.0717 (2020)

Goel, R., Sharma, R.(2020) Mobility based sir model for pandemics - with case study of covid-19. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 110–117 . https://doi.org/10.1109/ASONAM49781.2020.9381457

Goel, R., Sharma, R. (2021) Studying leaders during times of crisis using online social media–a covid case study. arXiv preprint arXiv:2101.03002

Goel R, Singh A, Ghanbarnejad F (2019) Modeling competitive marketing strategies in social networks. Physica A: Statis Mech Appl 518:50–70CrossRef

Goleman D (2004) What makes a leader? Harvard Bus Rev 82(1):82–91

Greene T, Bloomfield MA, Billings J (2020) Psychological trauma and moral injury in religious leaders during covid-19. Theory, Research, Practice, and Policy, Psychological TraumaCrossRef

Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans on pattern Anal Mach Intell 12(10):993–1001CrossRef

Hashmi FK, Iqbal Q, Haque N, Saleem F (2020) Religious cliché and stigma: A brief response to overlooked barriers in covid-19 management. J Religion Health 59(6):2697–2700CrossRef

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X et al (2020) Clinical features of patients infected with 2019 novel coronavirus in wuhan, china. The lancet 395(10223):497–506CrossRef

Jain VK, Kumar S (2015) An effective approach to track levels of influenza-a (h1n1) pandemic in india using twitter. Procedia Computer Science 70:801–807CrossRef

Jockers, ML (2015) Syuzhet Extract sentiment and plot arcs from text. Retrieved October 21, 2015

Kumar AA, Kumar AA (2018) Deadly nipah outbreak in kerala: Lessons learned for the future. Indian journal of critical care medicine: peer-reviewed, official publication of Indian Society of Critical Care Medicine 22(7):475CrossRef

Lee D, Lee J (2007) Domain described support vector classifier for multi-classification problems. Pattern Recognition 40(1):41–51CrossRef

Lee S, Hwang C, Moon MJ (2020) Policy learning and crisis policy-making: quadruple-loop learning and covid-19 responses in south korea. Policy Soc 39(3):363–381CrossRef

Littlefield RS, Quenette AM (2007) Crisis leadership and hurricane katrina: The portrayal of authority by the media in natural disasters. J Appl Commun Res 35(1):26–47CrossRef

Maas B, Grogan KE, Chirango Y, Harris N, Liévano-Latorre LF, McGuire KL, Moore AC, Ocampo-Ariza C, Palta MM, Perfecto I et al (2020) Academic leaders must support inclusive scientific communities during covid-19. Nat Ecol Evol 4(8):997–998CrossRef

McNeill A, Harris PR, Briggs P (2016) Twitter influence on uk vaccination and antiviral uptake during the 2009 h1n1 pandemic. Frontiers in public health 4:26CrossRef

Organization, WH (2020) Novel coronavirus–china

Organization, WH (2020) Statement on the second meeting of the international health regulations (2005) emergency committee regarding the outbreak of novel coronavirus (2019-ncov)

Organization WH et al (2020) Practical considerations and recommendations for religious leaders and faith-based communities in the context of covid-19: interim guidance, 7 April 2020. World Health Organization, Tech. rep

Oyeyemi SO, Gabarron E, Wynn R (2014) Ebola, twitter, and misinformation: a dangerous combination? Bmj 349:g6178CrossRef

Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: Bringing order to the web. Tech. rep, Stanford InfoLab

Pastor-Escuredo D, Tarazona, C (2020) Characterizing information leaders in twitter during covid-19 crisis. arXiv preprint arXiv:2005.07266

Porteous, I., Newman, D., Ihler, A., Asuncion, A., Smyth, P, Welling, M (2008) Fast collapsed gibbs sampling for latent dirichlet allocation. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 569–577

Ritterman, J., Osborne, M., Klein, E.(2009) Using prediction markets and twitter to predict a swine flu pandemic. In: 1st international workshop on mining social media, vol. 9, pp. 9–17

Rothan, HA., Byrareddy, SN (2020) The epidemiology and pathogenesis of coronavirus disease (covid-19) outbreak. J Autoimmunity p. 102433

Rothwell, J, Makridis, C (2020) Politics is wrecking america’s pandemic response. Brookings Institution. September 17, 2020

Rufai SR, Bunce C (2020) World leaders’ usage of twitter in response to the covid-19 pandemic: a content analysis. Journal of Public Health 42(3):510–516CrossRef

Samaras L, García-Barriocanal E, Sicilia MA (2020) comparing social media and google to detect and predict severe epidemics. Sci Rep 10(1):1–11CrossRef

Schroeder R, Everton S, Shepherd R (2012) Mining twitter data from the arab spring. Combat Terrorism Exch 2(4):54–64

Signorini A, Segre AM, Polgreen PM (2011) The use of twitter to track levels of disease activity and public concern in the us during the influenza a h1n1 pandemic. PloS one 6(5)

Smith, TO (2010) A little birdie told me: H1n1 information and misinformation exchange on twitter. UALR-William H. Bowen School of Law Legal Studies Research Paper (11-03)

Tsuda K (1999) Support vector classifier with asymmetric kernel functions. In: in European Symposium on Artificial Neural Networks (ESANN. Citeseer

Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) Predicting elections with twitter: What 140 characters reveal about political sentiment. In: Fourth international AAAI conference on weblogs and social media

Varela DG, Fedynich L (2020) Leading schools from a social distance: Surveying south texas school district leadership during the covid-19 pandemic. In: National Forum of Educational Administration and Supervision Journal, vol. 38

Yezli S, Khan A (2021) Covid-19 pandemic: it is time to temporarily close places of worship and to suspend religious gatherings. J Travel Med 28(2), taaa065

Zhang ML, Li YK, Liu XY (2015) Towards class-imbalance aware multi-label learning. In: Twenty-Fourth International Joint Conference on Artificial Intelligence

Title: Studying leaders & their concerns using online social media during the times of crisis - A COVID case study
Authors: Rahul Goel
Rajesh Sharma
Publication date: 01-12-2021
Publisher: Springer Vienna
Published in: Social Network Analysis and Mining / Issue 1/2021
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-021-00756-w

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