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Impact of COVID-19 on Indian politics: analyzing political leaders interactions and sentiments on Twitter

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Abstract

Amidst the persistent COVID-19 pandemic, there has been a profound disruption in political, economic, and social disruption in the entire world. India has emerged as one of the most affected countries by this pandemic globally. The government has taken extensive measures to combat the disease and is disseminating essential information regarding the same on social media, particularly Twitter. Restricted or polarized interactions and diverging opinions among the politicians may hinder the formulation of important policies and measures for managing this crucial situation. This paper, therefore, aims to perform an in-depth investigation on the Twitter activities of Indian political leaders in response to COVID-19. The study presents an analysis of their tweet sentiments and formation of networks during political discussions. The analysis has been done on three different topics pertaining to COVID-19: preventive measures, lockdown, and vaccination separately. Firstly, the communication ties formed between the politicians during discussions on the respective topics are investigated based on network analysis of their mentions and retweets. The communities formed in the interaction networks and the extent of polarization between the communities is then examined. Secondly, sentiment analysis of the tweets have been performed using some well-known machine learning classifiers to identify the sentiment leaning of the politicians and the communities toward the issue. This combined approach of network and sentiment based analysis provides better characterization of political communities and their leanings regarding the pandemic. The findings revealed the presence of polarized communication during retweets while high level of cross-party interactions during mentions. The politicians have been identified to have overall positive response toward preventive measures and vaccination while majority have shown negative sentiments toward lockdown.

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Data availability

The datasets generated and analyzed during the current study are not publicly available due to sensitivity of information but are available from the corresponding author on reasonable request.

References

  • Bhat M, Qadri M, Noor-ul Asrar Beg MK, Ahanger N, Agarwal B (2020) Sentiment analysis of social media response on the covid19 outbreak. Brain Behav Immun 87:136

    Article  Google Scholar 

  • Bhattacharya C, Chowdhury D, Ahmed N, Özgür S, Bhattacharya B, Mridha SK, Bhattacharyya M (2021) The nature, cause and consequence of covid-19 panic among social media users in india. Soc Netw Anal Min 11(1):53

    Article  Google Scholar 

  • Chehal D, Gupta P, Gulati P (2020) Covid-19 pandemic lockdown: an emotional health perspective of indians on twitter. Int J Soc Psychiatry, 0020764020940741

  • Gupta P, Kumar S, Suman R, Kumar V (2020) Sentiment analysis of lockdown in India during covid-19: a case study on twitter. IEEE Trans Comput Soc Syst

  • Gupta V, Jain N, Katariya P, Kumar A, Mohan S, Ahmadian A, Ferrara M (2021) An emotion care model using multimodal textual analysis on covid-19. Chaos Solitons Fract 144:110708

    Article  Google Scholar 

  • Gupta V, Jain N, Virmani D, Mohan S, Ahmadian A, Ferrara M (2022) Air and water health: industrial footprints of covid-19 imposed lockdown. Arab J Geosci 15(8):687

    Article  Google Scholar 

  • Gupta V, Santosh K, Arora R, Ciano T, Kalid KS, Mohan S (2022) Socioeconomic impact due to covid-19: an empirical assessment. Inf Process Manage 59(2):102810

    Article  Google Scholar 

  • Haman M (2020) The use of twitter by state leaders and its impact on the public during the covid-19 pandemic. Heliyon 6(11):e05540

    Article  Google Scholar 

  • Haupt MR, **ich-Diamant A, Li J, Nali M, Mackey TK (2021) Characterizing twitter user topics and communication network dynamics of the “liberate’’ movement during covid-19 using unsupervised machine learning and social network analysis. Online Soc Netw Med 21:100114

    Article  Google Scholar 

  • Jain S, Sinha A (2020) Identification of influential users on twitter: a novel weighted correlated influence measure for covid-19. Chaos Solitons Fract 139:110037

    Article  MathSciNet  Google Scholar 

  • Kaur H, Ahsaan SU, Alankar B, Chang V (2021) A proposed sentiment analysis deep learning algorithm for analyzing covid-19 tweets. Inf Syst Front, 1–13

  • Krackhardt D, Stern RN (1988) Informal networks and organizational crises: An experimental simulation. Soc Psychol Quart 123–140

  • Kumar N, Udah H, Francis A, Singh S, Wilson A (2022) Indian migrant workers’ experience during the covid-19 pandemic nationwide lockdown. J Asian Afr Stud 57(5):911–931

    Article  Google Scholar 

  • Kumar S, Choudhury S (2021) Migrant workers and human rights: a critical study on india’s covid-19 lockdown policy. Soc Sci Humanit Open 3(1):100130

    Google Scholar 

  • Li S, Wang Y, Xue J, Zhao N, Zhu T (2020) The impact of covid-19 epidemic declaration on psychological consequences: a study on active weibo users. Int J Environ Res Public Health 17(6):2032

    Article  Google Scholar 

  • Medford RJ, Saleh SN, Sumarsono A, Perl TM, Lehmann CU (2020) An “infodemic”: leveraging high-volume twitter data to understand early public sentiment for the coronavirus disease 2019 outbreak. In: Open forum infectious diseases, vol 7. Oxford University Press, p ofaa258

  • Mittal R, Mittal A, Aggarwal I (2021) Identification of affective valence of twitter generated sentiments during the covid-19 outbreak. Soc Netw Anal Min 11(1):108

    Article  Google Scholar 

  • Naseem U, Razzak I, Khushi M, Eklund PW, Kim J (2021) Covidsenti: a large-scale benchmark twitter data set for covid-19 sentiment analysis. IEEE Trans Comput Soc Syst 8(4):1003–1015

    Article  Google Scholar 

  • Newman ME (2004) Detecting community structure in networks. Eur Phys J B 38(2):321–330

    Article  Google Scholar 

  • Pandey R, Gautam V, Pal R, Bandhey H, Dhingra LS, Sharma H, Jain C, Bhagat K, Patel L, Agarwal M, et al (2020) A machine learning application for raising wash awareness in the times of covid-19 pandemic. ar**v preprint ar**v:2003.07074

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

    Article  Google Scholar 

  • Shoaei MD, Dastani M et al (2020) The role of twitter during the covid-19 crisis: a systematic literature review. Acta Inf Prag 9(2):154–169

    Google Scholar 

  • Singh M, Jakhar AK, Pandey S (2021) Sentiment analysis on the impact of coronavirus in social life using the bert model. Soc Netw Anal Min 11(1):33

    Article  Google Scholar 

  • Sudhir P, Suresh VD (2021) Comparative study of various approaches, applications and classifiers for sentiment analysis. Glob Trans Proc 2(2):205–211

    Article  Google Scholar 

  • Tejedor S, Cervi L, Tusa F, Portales M, Zabotina M (2020) Information on the covid-19 pandemic in daily newspapers’ front pages: case study of spain and italy. Int J Environ Res Public Health 17(17):6330

    Article  Google Scholar 

  • Verma R, Chhabra A, Gupta A (2022) A statistical analysis of tweets on covid-19 vaccine hesitancy utilizing opinion mining: an indian perspective. Soc Netw Anal Min 13(1):12

    Article  Google Scholar 

  • Vicari S, Murru MF (2020) One platform, a thousand worlds: on Twitter irony in the early response to the covid-19 pandemic in Italy. Soc Media+ Soc 6(3):2056305120948254

    Google Scholar 

  • Wu JT, Leung K, Leung GM (2020) Nowcasting and forecasting the potential domestic and international spread of the 2019-ncov outbreak originating in wuhan, china: a modelling study. Lancet 395(10225):689–697

    Article  Google Scholar 

  • Zeemering ES (2021) Functional fragmentation in city hall and twitter communication during the covid-19 pandemic: evidence from atlanta, san francisco, and washington, dc. Gov Inf Q 38(1):101539

    Article  Google Scholar 

Download references

Acknowledgements

The author would like to acknowledge the support of Technology Innovation and Development Foundation, Indian Institute of Technology Guwahati.

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  1. Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Anindita Borah

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  1. Anindita Borah
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Borah, A. Impact of COVID-19 on Indian politics: analyzing political leaders interactions and sentiments on Twitter. Soc. Netw. Anal. Min. 13, 144 (2023). https://doi.org/10.1007/s13278-023-01153-1

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