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# Examining the role of Social Media in Emergency Healthcare Communication: A Bibliometric Approach

Keshav Dhir¹ [0000-0003-2434-4425], Prabhsimran Singh² [0000-0002-1453-3989],
Yogesh K. Dwivedi³ [0000-0002-5547-9990]
Sargun Sawhney⁴, Ravinder Singh Sawhney⁵ [0000-0001-7892-0825]

¹Department of Computer Science and Engineering,

¹Chandigarh University, Gharuan, Mohali, India,

1,2,4Department of Computer Engineering & Technology,

⁵Department of Electronics Technology,

1,2,4,5Guru Nanak Dev University, Amritsar, India,

³School of Management, Emerging Market Research Center (EMaRC),

⁵Swansea University, Swansea, UK,

³Department of Management, Symbiosis Institute of Business Management, Pune &

⁵Symbiosis International (Deemed University), Pune, Maharashtra, India.

¹kesdhir@gmail.com, ²prabhsimran.dcet@gndu.ac.in, ³prabh_singh32@yahoo.com,

³y.k.dwivedi@swansea.ac.uk, ⁴sargunsawhney@gmail.com, ⁵sawhney.ece@gndu.ac.in

**Abstract.** Social media has been extensively used for the communication of health-related information and consecutively for the potential spread of medical misinformation. The aim of this study was to perform a bibliometric analysis of the current literature from Scopus database to discover the prevalent trends and topics related to communication regarding healthcare emergencies via social media. To accomplish this task, the research work has been carried in form of defending three research questions related to most impactful sources, most impactful publications and most impactful authors. The adopted methodology has been successful towards answering the research questions in the light of our collected dataset of Scopus articles.

**Keywords:** Bibliometric Analysis, Crisis Communication, Emergency Communication, Healthcare Communication, Social Media, Scopus, Twitter.

## 1 Introduction

COVID-19 pandemic has turned out to be the greatest enemy of the humankind in recent years (Singh et al., 2020a). This pandemic has exposed vulnerability of our medical system. With the advancement in information technology, people were able to combat this deadly pandemic through better communication for information diffusion (Dwivedi et al., 2020). Social media platforms, especially Twitter played an important role in passing the critical information to people around the world in real time breaking the traditional geographical barriers (Haman, 2020; Singh et al., 2018; Xue et al., 2020). The proliferation of Internet has led to exponential rise in social media users worldwide (Singh et al., 2019). Social media has now become more of an information-sharer platform rather than just a media related fun service or a tool. It
has been observed that people now share all sorts of information, whether related to politics, sports, disaster etc (Dwivedi et al., 2018; Kapoor et al., 2018; Singh et al., 2020b).

Medical emergencies at global levels can be threatening to the human race. Extensive use of social media, in the recent years has helped many people across the globe to make use of the shared information related to medical healthcare whether disease specific or the precautionary measures related to an outbreak. People are becoming comparatively more knowledgeable about the crisis management through the information being shared by other people through their own experiences via social media channels or groups. Social media is a competitive differential towards the provision of health services and it has revolutionized the methods how any kind of information can be transferred to the masses (Margus et al., 2021; Riaz et al., 2020).

Hence, this paper explores the use of social media in emergency healthcare communications. To accomplish our research objectives towards completion of this task, we made use of bibliometric analysis. Pritchard and Wittig, 1981 first coined the term “bibliometrics”. This approach has evolved quite well reaching the crescendo towards growth of new age technologies. In order to effectively assess and analyze the overall outcome of the research undertaken by researchers in managing the cooperation among the universities, authors, countries etc., bibliometrics has become an extremely predominant tool (Gutiérrez-Salcedo et al., 2018).

This research work reviews the literature concerning utilization of social media in emergency healthcare communications over a decade between 2014 and 2021. In doing so, we try to investigate and defend the following research questions (RQs):

**RQ1:** What are the most impactful sources (journals)?

**RQ2:** What are the most impactful documents (publications)?

**RQ3:** Who are the most impactful authors?

The rest of the paper is structured as follows: Section 2 describes the methodology and selection criteria. Section 3, deals with various results and findings, followed by discussions in Section 4. Finally, we make concluding remarks in section 5.

## 2 Methodology

The task of accomplishing the bibliographic analysis is performed in numerous stages. To explore the favorable bibliographic database (e.g. PubMed, EMbase, SpringerLink, Scopus, Web of Science etc.) and to heap up the essential articles as well as journals, is considered as the most crucial stage, and this happens to be our initial stage. Here, it is to be noted that not all the databases provide us with the information or data in order to directly perform the bibliometric analysis. In this study, Scopus database is considered for bibliometrics on the retrieved sources (articles, journals, etc.) as it gives access to the citation data and huge databases related to social sciences, health sciences, physical as well as life sciences. Scopus
database can be accessed through Elsevier website and it covers three types of sources: book series, journals and trade journals.

To gather the data the following search query was used: ('Social Media' OR 'Twitter' OR 'Tweet' OR 'Social Media Analytic') AND ('Medical' OR 'M-Health' OR 'Health' OR 'Healthcare') AND ('Emergency' OR 'Crisis' OR 'Epidemic' OR 'Pandemic') AND ('Management' OR 'Awareness' OR 'Info-Management'). In addition to this, the period of publication was selected as 2014-2021, as we wanted to concentrate on the latest literature articles related to the utilization of social media in emergency healthcare communications. Further, only highly rated journal publications were considered for our analysis.

In bibliometric analysis, the data preprocessing and cleaning is an important as well as daunting task that should be performed as the first step. Scopus data collected using the query string resulted in a total of 1273 published articles, which was then refined manually on the basis of relatable titles from the total list, bringing the bibliographic data down to 756 articles. Then to narrow it down further, the remaining articles were filtered one by one on the basis of useful abstracts by reading them individually, bringing the whole count to a solid 251 but after thoroughly analyzing each paper and data munging, which required removal of unwanted keyword data, filtering and manual inspection of every article based on their abstract, methodology and conclusion, the outcome reduced to a favorable 67 unique articles, on which this whole analysis was conducted. An overview of collected articles is given in Table 1.

<table><caption>Table 1: Overview of Collected Articles</caption><thead><tr><th colspan="2">Main Information</th><th colspan="2">Authors</th><th colspan="2">Authors Collaboration</th></tr></thead><tbody><tr><td>Sources</td><td>56</td><td>Authors</td><td>300</td><td>Single-authored documents</td><td>8</td></tr><tr><td>Documents</td><td>67</td><td>Author Appearances</td><td>311</td><td>Documents per Author</td><td>0.233</td></tr><tr><td>Average years from publication</td><td>2.14</td><td>Authors of single-authored documents</td><td>7</td><td>Authors per Document</td><td>4.29</td></tr><tr><td>Average citations per documents</td><td>13.44</td><td>Authors of multi-authored documents</td><td>293</td><td>Co-Authors per Documents</td><td>4.44</td></tr><tr><td>Average citations per year per document</td><td>3.498</td><td></td><td></td><td>Collaboration Index</td><td>4.73</td></tr><tr><td>References</td><td>3272</td><td></td><td></td><td></td><td></td></tr></tbody></table>

For this research study, an open-source R Package, "bibliometrix" was used which incorporates several analysis options. It is a powerful library that can perform complete bibliometric and scientometric analysis (Aria and Cuccurullo, 2017). Furthermore, it allows us to obtain multiple types of graphs and features not common in other libraries and any non-coder can also use this tool as well.

## 3 Results and Findings

In this section, we share the results and findings of bibliometric analysis. This section has been divided in four sub-sections. The first sub-section deals with most impactful
sources (journals), while, second sub-section deals with most impactful documents. Third sub-section deals with most impactful authors, while prominent publication themes are discussed in fourth sub-section.

## 3.1 Impactful Sources

In order to identify the most impactful sources, we first check the relevance of the sources i.e. number of articles published from a particular source in our shortlisted 67 articles (shortlisted local database). The result of this analysis is shown in Table 2. With five articles Journal of Medical Internet Research (Himelein-Wachowiak et al., 2021; Iyu et al., 2021; Margus et al., 2021; Nagar et al., 2014; Park et al., 2021) made the maximum contribution, followed by Plos One (Allen et al., 2016; Buchanan et al., 2021; Hartley et al., 2017; Rivieccio et al., 2021) with four articles.

<table><caption>**Table 2:** Journals with maximum publications</caption><thead><tr><td>Rank</td><td>Journals</td><td>Number of Articles Published</td></tr></thead><tbody><tr><td>1</td><td>Journal of Medical Internet Research</td><td>5</td></tr><tr><td>2</td><td>Plos One</td><td>4</td></tr><tr><td>=3</td><td>International Journal of Environmental Research and Public Health</td><td>3</td></tr><tr><td>=3</td><td>Online Social Networks and Media</td><td>3</td></tr><tr><td>=5</td><td>IEEE Access</td><td>2</td></tr><tr><td>=5</td><td>Indonesian Journal of Geography</td><td>2</td></tr><tr><td>=5</td><td>Journal of Information and Knowledge Management</td><td>2</td></tr></tbody></table>

Next, we attempt to identify most locally cited sources. The term locally cited refers to the citations that an article receives from other articles in the review corpus only citations (Donthu et al., 2021). With 45 citations Plos One was the most locally cited source, followed by Journal of Medical Internet Research with 37 citations. The result of top ten locally cited sources is shown in Table 3.

<table><caption>**Table 3:** Most Local Cited Sources</caption><thead><tr><td>Rank</td><td>Journals</td><td>Number of Citations</td></tr></thead><tbody><tr><td>1</td><td>Plos One</td><td>45</td></tr><tr><td>2</td><td>Journal of Medical Internet Research</td><td>37</td></tr><tr><td>3</td><td>Government Information Quarterly</td><td>30</td></tr><tr><td>4</td><td>Science</td><td>19</td></tr><tr><td>5</td><td>Nonprofit and Voluntary Sector Quarterly</td><td>18</td></tr><tr><td>6</td><td>Nature</td><td>17</td></tr><tr><td>=7</td><td>Computers in Human Behavior</td><td>16</td></tr><tr><td>=7</td><td>Public Relations Review</td><td>16</td></tr><tr><td>9</td><td>American Journal of Public Health</td><td>15</td></tr><tr><td>10</td><td>Information</td><td>14</td></tr></tbody></table>

## 3.2 Impactful Documents

In order to investigate the most impactful document, we first find the most globally cited documents. Islam et al., (2020), is the most globally cited article with 198
citations, followed by Gaspar et al., (2016) and Nagar et al., (2014) with 94 citations each. The results most globally cited documents is shown in Table 4.

<table><caption>**Table 4: Most Globally Cited Documents**</caption><thead><tr><td>Rank</td><td>Publication</td><td>Total Citations</td><td>Total Citations Per Year</td><td>Normalized Total Citations</td></tr></thead><tbody><tr><td>1</td><td>Islam et al., 2020</td><td>198</td><td>66.000</td><td>7.8628</td></tr><tr><td>=2</td><td>Gaspar et al., 2016</td><td>94</td><td>13.429</td><td>1.9583</td></tr><tr><td>=2</td><td>Nagar et al., 2014</td><td>94</td><td>10.444</td><td>1.4242</td></tr><tr><td>4</td><td>Allen et al., 2016</td><td>68</td><td>9.714</td><td>1.4167</td></tr><tr><td>5</td><td>Sutton et al., 2015</td><td>57</td><td>7.125</td><td>1.0000</td></tr><tr><td>6</td><td>Plotnick and Hiltz 2016</td><td>55</td><td>7.857</td><td>1.1458</td></tr><tr><td>7</td><td>Househ, 2016</td><td>47</td><td>6.714</td><td>0.9792</td></tr><tr><td>8</td><td>Shahi et al., 2021</td><td>44</td><td>22.000</td><td>10.8736</td></tr><tr><td>9</td><td>Teufel et al., 2020</td><td>32</td><td>10.667</td><td>1.2708</td></tr><tr><td>10</td><td>Li et al., 2020</td><td>21</td><td>7.000</td><td>0.8339</td></tr></tbody></table>

Next, we compute the most locally cited documents. To our surprise only four documents were locally cited. The results of the most locally cited documents are shown in Table 5. Nagar et al., (2014), was the most locally cited document with three local citations.

<table><caption>**Table 5: Most Locally Cited Documents**</caption><thead><tr><td>Rank</td><td>Publication</td><td>Local Citations (LC)</td><td>Global Citations (GC)</td><td>LC/GC Ratio (%)</td><td>Normalized Local Citation</td><td>Normalized Global Citation</td></tr></thead><tbody><tr><td>1</td><td>Nagar et al., 2014</td><td>3</td><td>94</td><td>3.19</td><td>2.00</td><td>1.42</td></tr><tr><td>2</td><td>Shahi et al., 2021</td><td>2</td><td>44</td><td>4.55</td><td>43.00</td><td>10.87</td></tr><tr><td>=3</td><td>Islam et al., 2020</td><td>1</td><td>198</td><td>0.51</td><td>5.50</td><td>7.86</td></tr><tr><td>=3</td><td>Hassan et al., 2020</td><td>1</td><td>11</td><td>9.09</td><td>5.50</td><td>0.44</td></tr></tbody></table>

### 3.3 Impactful Authors

In order to identify the most impactful authors, we first check the relevance of the authors i.e. number of articles published by an author in shortlisted local database. There were a total of four authors (Benítez De Gracia, E; Herrera-Peco, I; Jiménez-Gómez, B; Ruiz Núñez, C) who published two articles and coincidently they authored the same two articles. The results of same is shown in Table 6.

<table><caption>**Table 6: Most Relevant Authors**</caption><thead><tr><td>Author</td><td>Publications</td><td>Number of Articles</td><td>Article Fractionalized</td></tr></thead><tbody><tr><td>Benítez De Gracia, E</td><td>Herrera-Peco et al., 2021a; Herrera-Peco et al., 2021b</td><td>2</td><td>0.34</td></tr><tr><td>Herrera-Peco, I</td><td>Herrera-Peco et al., 2021a; Herrera-Peco et al., 2021b</td><td>2</td><td>0.34</td></tr><tr><td>Jiménez-Gómez, B</td><td>Herrera-Peco et al., 2021a; Herrera-Peco et al., 2021b</td><td>2</td><td>0.34</td></tr><tr><td>Ruiz Núñez, C</td><td>Herrera-Peco et al., 2021a; Herrera-Peco et al., 2021b</td><td>2</td><td>0.34</td></tr></tbody></table>

Next, major question emanates that the authors of which country made the maximum impact. So, in order to investigate this question, we check the corresponding authors'
country. The maximum impact was observed to be created by USA with 18 articles. The results of the same are shown in Table 7.

<table><caption>Table 7: Corresponding Author Country</caption><thead><tr><td>Rank</td><td>Country</td><td>Number of Articles</td><td>Frequency</td><td>Simple Country Publication</td><td>Multiple Country Publication</td><td>MCP_Ratio</td></tr></thead><tbody><tr><td>1</td><td>USA</td><td>18</td><td>0.3673</td><td>17</td><td>1</td><td>0.0556</td></tr><tr><td>=2</td><td>Indonesia</td><td>4</td><td>0.0816</td><td>4</td><td>0</td><td>0.0000</td></tr><tr><td>=2</td><td>United Kingdom</td><td>4</td><td>0.0816</td><td>3</td><td>1</td><td>0.2500</td></tr><tr><td>=4</td><td>Egypt</td><td>3</td><td>0.0612</td><td>3</td><td>0</td><td>0.0000</td></tr><tr><td>=4</td><td>Saudi Arabia</td><td>3</td><td>0.0612</td><td>2</td><td>1</td><td>0.3333</td></tr><tr><td>=6</td><td>China</td><td>2</td><td>0.0408</td><td>2</td><td>0</td><td>0.0000</td></tr><tr><td>=6</td><td>Korea</td><td>2</td><td>0.0408</td><td>1</td><td>1</td><td>0.5000</td></tr><tr><td>=6</td><td>Spain</td><td>2</td><td>0.0408</td><td>2</td><td>0</td><td>0.0000</td></tr><tr><td>=6</td><td>Tunisia</td><td>2</td><td>0.0408</td><td>2</td><td>0</td><td>0.0000</td></tr></tbody></table>

### 3.4 Prominent Publication Themes

When analysing the multiple articles, it becomes significant to find the most prominent themes in order to cluster them into groups. Further it also helps us to identify the under-explored themes that can be taken by researchers in future work. In order to get themes of these articles, latent dirichlet allocation (LDA) based topic modeling is used (Jeong et al., 2019; Singh et al., 2020b). Three prominent themes were identified namely (a) **Theme-1:** Government use of social media during healthcare emergency (b) **Theme-2:** Public use of social media during healthcare emergency (c) **Theme-3:** Spread of fake news and misinformation during healthcare emergency. Though many can argue that theme-1 and theme-2 overlap each other as both are related to use of social media during healthcare emergency; however, the main difference lies between the context of major stake holders using the social media; in theme-1 it is the government, while in theme-2 it is the general public. The results of topic modeling are shown in Table 8.

<table><caption>Table 8: Results of Topic Modeling</caption><thead><tr><td>Themes</td><td>Total number of Publications</td><td>References</td></tr></thead><tbody><tr><td>Government use of social media during healthcare emergency</td><td>21</td><td>Adriani et al., 2020; Akbar et al., 2021; Alomari et al., 2021; Al-Sarem et al., 2021; Arpaci et al., 2021; Bélanger et al., 2021; Crook et al., 2016; Ilyas et al., 2021; Islam et al., 2020; Jing et al., 2021; Laserna et al., 2021; London Jr et al., 2021; Machmud et al., 2021; Mansoor et al., 2021; O'Leary et al., 2020; Park et al., 2021; Rivas-De-roca et al., 2021; Santoso et al., 2019; Solnick et al., 2021; Teufel et al., 2020; Thelwall, 2021</td></tr><tr><td>Information Diffusion through social media during healthcare emergency</td><td>33</td><td>Ahmed et al., 2020; Al-Dulaimi et al., 2018; Allen et al., 2016; Andreadis et al., 2021; Botero-Rodríguez et al., 2021; Buchanan et al., 2021; Cherichi et al., 2019; Chong et al., 2021; Enoki et al., 2017; Galindo Neto et al., 2021; Gaspar et al., 2016; Hartley et al., 2017; Haupt et al., 2021; Househ et al., 2016; Li et al., 2020; Lyu et al., 2021; Mann et al., 2021; Margus et al., 2021; Nagar et al., 2014; Nikolovska</td></tr></tbody></table>
<table><tr><td></td><td></td><td>et al., 2020; O'Connor et al., 2021; Plotnick et al., 2016; Priyadarshini et al., 2021; Riaz et al., 2020; Rivieccio et al., 2021; Schweinberger et al., 2021; Shakeri, 2020; Sun et al., 2021; Tsai et al., 2021; Tully et al., 2019; Wang et al., 2021; Wu, 2021;</td></tr><tr><td>Spread of fake news and misinformation during healthcare emergency</td><td>12</td><td>Abdelminaam et al., 2021; Burel et al., 2021; Hassan et al., 2020; Herrera-Peco et al., 2021a; Herrera-Peco et al., 2021b; Himelein-Wachowiak et al., 2021; Jemielniak et al., 2021; Muric et al., 2021; Qureshi et al., 2021; Safarnejad et al., 2020; Shahi et al., 2021; Sutton et al., 2015</td></tr></table>

## 4 Discussions

Social media is so deeply embedded in our everyday lives, that we have become helplessly reliant on it for every source of information. This is also true for healthcare related information. This research explores the use of social media in emergency healthcare communication. Our research was based upon three research questions, namely, the most impactful resource, the most impactful document and lastly, the most prominent author(s) and additionally, the country too. According to our results, Journal of Medical Internet Research and Plos One were the most impactful sources (journals). Similarly, Islam et al., 2020 and Nagar et al., 2014 were the most impactful documents (publications). Benítez De Gracia, E; Herrera-Peco, I; Jiménez-Gómez, B; Ruiz Núñez, C were the most impactful authors, while USA was the most impactful country.

This study also reveals that the current subject matter in discussion is not limited to a single domain like information systems (IS), healthcare sciences, information technology (IT) and the social media, but has further advanced into multidisciplinary connotation. People from different disciplines like sciences, healthcare and information systems are working together to explore this area of knowledge.

Like any other research work, this research work also has its own limitations: Firstly, all the sources have been retrieved from a single database, 'Scopus'. The articles from most other prominent databases e.g. PubMed, EMbase, SpringerLink, Web of Science etc. are not considered for bibliometric analysis. Within the selected database, only peer-reviewed articles are ruminated and leaving behind all the other forms of publications, such as conference papers, review papers, book chapters, etc. Secondly, we have considered only the literature written in the English language and no other language is included for the analysis which might have impacted the overall outcome of the findings of this study. Thirdly, the search query used for this study also poses a limitation to the whole analysis as any other combination of search query might have given different results. Finally, even if different initials or surnames have been used by the authors, still, this analysis does not capture common names for active researchers.

Regardless of these limitations, this study can play a vital role in contributing towards the methodological review of active journals published in the area of medical
emergencies, related to the communication via famous social media platforms by performing bibliometrics on them. Furthermore, these limitations can be addressed in future studies.

## 5 Conclusions

This research analyzes the evolution of research trends in the use of social media for information sharing and communication during global medical emergency situations. In this research work, we have used the bibliometric analysis to investigate and defend our three research questions. Though the limitations of this research work have been listed, yet the purpose of completing this investigation seems fulfilled. Further, as highlighted from our research results that spread of fake news and misinformation during healthcare emergency was under explored research domain, which the researchers can explore in future studies.

## References

Abdelminaam, D. S., Ismail, F. H., Taha, M., Taha, A., Houssein, E. H., & Nabil, A. (2021). CoAID-DEEP: an optimized intelligent framework for automated detecting COVID-19 misleading information on Twitter. Ieee Access, 9, 27840-27867.

Adriani, M., Azzahro, F., & Hidayanto, A. N. (2020). Disease surveillance in Indonesia through Twitter posts. Journal of applied research and technology, 18(3), 214-228.

Ahmed, M. A., Sadri, A. M., & Amini, M. H. (2020). Data-driven Inferences of Agency-level Risk and Response Communication on COVID-19 through Social Media based Interactions. arXiv preprint arXiv:2008.03866.

Akbar, G. G., Kurniadi, D., & Nurliawati, N. (2021). Content Analysis of Social Media: Public and Government Response to COVID-19 Pandemic in Indonesia. Jurnal Ilmu Sosial Dan Ilmu Politik, 25(1), 16-31.

Al-Dulaimi, O. H. Z. Image Content based Topological Analysis for Friend Recommendation on Twitter.

Allen, C., Tsou, M. H., Aslam, A., Nagel, A., & Gawron, J. M. (2016). Applying GIS and machine learning methods to Twitter data for multiscale surveillance of influenza. PloS one, 11(7), e0157734.

Alomari, E., Katib, I., Albeshri, A., & Mehmood, R. (2021). COVID-19: Detecting government pandemic measures and public concerns from Twitter arabic data using distributed machine learning. International Journal of Environmental Research and Public Health, 18(1), 282.

Al-Sarem, M., Alsaeedi, A., Saeed, F., Boulila, W., & AmeerBakhsh, O. (2021). A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs. Applied Sciences, 11(17), 7940.

Andreadis, S., Antzoulatos, G., Mavropoulos, T., Giannakeris, P., Tzionis, G., Pantelidis, N., ... & Kompatsiaris, I. (2021). A social media analytics platform visualising the spread of
COVID-19 in Italy via exploitation of automatically geotagged tweets. Online Social Networks and Media, 23, 100134.

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics, 11(4), 959-975.

Arpaci, I., Alshehabi, S., Mahariq, I., & Topcu, A. E. (2021). An Evolutionary Clustering Analysis of Social Media Content and Global Infection Rates During the COVID-19 Pandemic. Journal of Information & Knowledge Management, 20(03), 2150038.

Bélanger, M. E., & Lavenex, S. (2021). Communicating Mobility Restrictions During the COVID-19 Crisis on Twitter: The Legitimacy Challenge. Swiss Political Science Review, 27(4), 822-839.

Botero-Rodríguez, F., Mosquera-Quiñónez, M., Torres, L. M. M., Bolívar-Moná, S., Jovel, G., Vargas, L., ... & Gómez-Restrepo, C. (2021). Análisis de percepciones y repercusiones emocionales en usuarios de Twitter en Colombia durante la pandemia de COVID-19. Revista Colombiana de Psiquiatría.

Buchanan, K., Aknin, L. B., Lotun, S., & Sandstrom, G. M. (2021). Brief exposure to social media during the COVID-19 pandemic: Doom-scrolling has negative emotional consequences, but kindness-scrolling does not. Plos one, 16(10), e0257728.

Burel, G., Farrell, T., & Alani, H. (2021). Demographics and topics impact on the co-spread of COVID-19 misinformation and fact-checks on Twitter. Information Processing & Management, 58(6), 102732.

Cherichi, S., & Faiz, R. (2019). Upgrading event and pattern detection to big data. International Journal of Computational Science and Engineering, 18(4), 404-412.

Chong, M., & Park, H. W. (2021). COVID-19 in the Twitterverse, from epidemic to pandemic: information-sharing behavior and Twitter as an information carrier. Scientometrics, 126(8), 6479-6503.

Crook, B., Glowacki, E. M., Suran, M., K. Harris, J., & Bernhardt, J. M. (2016). Content analysis of a live CDC Twitter chat during the 2014 Ebola outbreak. Communication Research Reports, 33(4), 349-355.

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. *Journal of Business Research, 133*, 285-296.

Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., ... & Upadhyay, N. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International journal of information management, 55, 102211.

Dwivedi, Y. K., Kelly, G., Janssen, M., Rana, N. P., Slade, E. L., & Clement, M. (2018). Social media: The good, the bad, and the ugly. Information Systems Frontiers, 20(3), 419-423.

Enoki, M., Yoshida, I., & Oguchi, M. (2017). Capacity Control of Social Media Diffusion for Real-Time Analysis System. IEICE TRANSACTIONS on Information and Systems, 100(4), 776-784.

Galindo Neto, N. M., Sá, G. G. D. M., Pereira, J. D. C. N., Barbosa, L. U., Henriques, A. H. B., & Barros, L. M. (2021). COVID-19: comments on official social network of the Ministry of Health about action Brazil Count on Me. Revista Gaúcha de Enfermagem, 42.
Gaspar, R., Pedro, C., Panagiotopoulos, P., & Seibt, B. (2016). Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events. Computers in Human Behavior, 56, 179-191.

Gutiérrez-Salcedo, M., Martínez, M. Á., Moral-Munoz, J. A., Herrera-Viedma, E., & Cobo, M. J. (2018). Some bibliometric procedures for analyzing and evaluating research fields. Applied intelligence, 48(5), 1275-1287.

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.

Hartley, D. M., Giannini, C. M., Wilson, S., Frieder, O., Margolis, P. A., Kotagal, U. R., ... & Macaluso, M. (2017). Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital. PLoS One, 12(7), e0182008.

Hassan, N., Gomaa, W., Khoriba, G., & Haggag, M. (2020). Credibility detection in twitter using word n-gram analysis and supervised machine learning techniques. International Journal of Intelligent Engineering and Systems, 13(1), 291-300.

Haupt, M. R., Jinich-Diamant, A., Li, J., Nali, M., & Mackey, T. K. (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 Social Networks and Media, 21, 100114.

Herrera-Peco, I., Jiménez-Gómez, B., Romero Magdalena, C. S., Deudero, J. J., García-Puente, M., Benítez De Gracia, E., & Ruiz Núñez, C. (2021a). Antivaccine movement and COVID-19 negationism: A content analysis of spanish-written messages on twitter. Vaccines, 9(6), 656.

Herrera-Peco, I., Jiménez-Gómez, B., Romero-Magdalena, C. S., & Benítez De Gracia, E. (2021b). COVID-19 and vaccination: analysis of public institution's role in information spread through Twitter. Revista espanola de salud publica, 95.

Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H. A., ... & Curtis, B. (2021). Bots and misinformation spread on social media: Implications for COVID-19. Journal of Medical Internet Research, 23(5), e26933.

Househ, M. (2016). Communicating Ebola through social media and electronic news media outlets: A cross-sectional study. Health informatics journal, 22(3), 470-478.

Ilyas, H., Anwar, A., Yaqub, U., Alzamil, Z., & Appelbaum, D. (2021). Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India. Global Knowledge, Memory and Communication.

Islam, A. N., Laato, S., Talukder, S., & Sutinen, E. (2020). Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective. Technological forecasting and social change, 159, 120201.

Jemielniak, D., & Krempovych, Y. (2021). An analysis of AstraZeneca COVID-19 vaccine misinformation and fear mongering on Twitter. Public Health, 200, 4-6.

Jeong, B., Yoon, J., & Lee, J. M. (2019). Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management, 48, 280-290.

Jing, E., & Ahn, Y. Y. (2021). Characterizing partisan political narrative frameworks about COVID-19 on Twitter. EPJ data science, 10(1), 53.
Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: Past, present and future. Information Systems Frontiers, 20(3), 531-558.

Laserna, M. S. S., Marí-Sáez, V. M., & Ceballos-Castro, G. (2021). Analysis of the solidarity discourse of spanish NGOS on the coronavirus on Twitter. Tonos Digital.

Li, Y., Twersky, S., Ignace, K., Zhao, M., Purandare, R., Bennett-Jones, B., & Weaver, S. R. (2020). Constructing and communicating COVID-19 stigma on Twitter: a content analysis of tweets during the early stage of the COVID-19 outbreak. International Journal of Environmental Research and Public Health, 17(18), 6847.

London Jr, J., & Matthews, K. (2021). Crisis communication on social media-lessons from Covid-19. Journal of Decision Systems, 1-21.

Lyu, J. C., Le Han, E., & Luli, G. K. (2021). COVID-19 vaccine–related discussion on Twitter: topic modeling and sentiment analysis. Journal of medical Internet research, 23(6), e24435.

Machmud, M., Irawan, B., Karinda, K., & Susilo, J. (2021). Analysis of the intensity of communication and coordination of government officials on twitter social media during the Covid-19 handling in Indonesia. Academic Journal of Interdisciplinary Studies, 10(3), 319-319.

Mann, M., Byun, S. E., & Ginder, W. (2021). B Corps’ Social Media Communications during the COVID-19 Pandemic: Through the Lens of the Triple Bottom Line. Sustainability, 13(17), 9634.

Mansoor, M. (2021). Citizens' trust in government as a function of good governance and government agency's provision of quality information on social media during COVID-19. Government Information Quarterly, 38(4), 101597.

Margus, C., Brown, N., Hertelendy, A. J., Safferman, M. R., Hart, A., & Ciottone, G. R. (2021). Emergency physician Twitter use in the COVID-19 pandemic as a potential predictor of impending surge: Retrospective observational study. Journal of Medical Internet Research, 23(7), e28615.

Muric, G., Wu, Y., & Ferrara, E. (2021). COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Data Set of Antivaccine Content, Vaccine Misinformation, and Conspiracies. JMIR public health and surveillance, 7(11), e30642.

Nagar, R., Yuan, Q., Freifeld, C. C., Santillana, M., Nojima, A., Chunara, R., & Brownstein, J. S. (2014). A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. Journal of medical Internet research, 16(10), e3416.

Nikolovska, M., Johnson, S. D., & Ekblom, P. (2020). “Show this thread”: policing, disruption and mobilisation through Twitter. An analysis of UK law enforcement tweeting practices during the Covid-19 pandemic. Crime Science, 9(1), 1-16.

O'Connor, C., O'Connell, N., Burke, E., Dempster, M., Graham, C. D., Scally, G., ... & Darker, C. D. (2021). Bordering on crisis: A qualitative analysis of focus group, social media, and news media perspectives on the Republic of Ireland-Northern Ireland border during the 'first wave' of the COVID-19 pandemic. Social Science & Medicine, 282, 114111.

O'Leary, D. E., & Storey, V. C. (2020). A Google–Wikipedia–Twitter model as a leading indicator of the numbers of coronavirus deaths. Intelligent Systems in Accounting, Finance and Management, 27(3), 151-158.
Park, S., Han, S., Kim, J., Molaie, M. M., Vu, H. D., Singh, K., ... & Cha, M. (2021). COVID-19 discourse on Twitter in four asian countries: case study of risk communication. Journal of medical Internet research, 23(3), e23272.

Plotnick, L., & Hiltz, S. R. (2016). Barriers to use of social media by emergency managers. Journal of Homeland Security and Emergency Management, 13(2), 247-277.

Pritchard, A., & Wittig, G. R. (1981). Bibliometrics. Watford: AllM Books.

Priyadarshini, I., Mohanty, P., Kumar, R., Sharma, R., Puri, V., & Singh, P. K. (2021). A study on the sentiments and psychology of twitter users during COVID-19 lockdown period. Multimedia Tools and Applications, 1-23.

Qureshi, K. A., Malick, R. A. S., Sabih, M., & Cherifi, H. (2021). Complex Network and Source Inspired COVID-19 Fake News Classification on Twitter. IEEE Access, 9, 139636-139656.

Riaz, M., Wang, X., & Guo, Y. (2020). An empirical investigation of precursors influencing social media health information behaviors and personal healthcare habits during coronavirus (COVID-19) pandemic. Information Discovery and Delivery.

Rivas-De-roca, R., García-Gordillo, M., & Rojas-Torrijos, J. L. (2021). Communication strategies on Twitter and institutional websites in the Covid-19 second wave: analysis of the governments of Germany, Spain, Portugal, and the United Kingdom. Revista Latina de Comunicacion Social, 49-72.

Rivieccio, B. A., Micheletti, A., Maffeo, M., Zignani, M., Comunian, A., Nicolussi, F., ... & Biganzoli, E. (2021). CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region. PloS one, 16(2), e0247854.

Safarnejad, L., Xu, Q., Ge, Y., Krishnan, S., Bagarvathi, A., & Chen, S. (2020). Contrasting misinformation and real-information dissemination network structures on social media during a health emergency. American Journal of Public Health, 110(S3), S340-S347.

Santoso, A. D. (2019). Tweets flooded in bandung 2016 floods: Connecting individuals and organizations to disaster information. The Indonesian Journal of Geography, 51(3), 242-250.

Schweinberger, M., Haugh, M., & Hames, S. (2021). Analysing discourse around COVID-19 in the Australian Twittersphere: A real-time corpus-based analysis. Big Data & Society, 8(1), 20539517211021437.

Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online social networks and media, 22, 100104.

Shakeri, S. (2020). A framework for the interaction of active audiences and influencers on Twitter: The case of Zika virus. Journal of Information & Knowledge Management, 19(04), 2050032.

Singh, P., Dwivedi, Y. K., Kahlon, K. S., Rana, N. P., Patil, P. P., & Sawhney, R. S. (2019, September). Digital Payment Adoption in India: Insights from Twitter Analytics. In Conference on e-Business, e-Services and e-Society (pp. 425-436). Springer, Cham.

Singh, P., Dwivedi, Y. K., Kahlon, K. S., Sawhney, R. S., Alalwan, A. A., & Rana, N. P. (2020c). Smart monitoring and controlling of government policies using social media and cloud computing, Information Systems Frontiers, 22, 315-337.
Singh, P., Dwivedi, Y. K., Kahlon, K. S., Pathania, A., & Sawhney, R. S. (2020b). Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections. Government Information Quarterly, 37(2), 101444.

Singh, P., Sawhney, R. S., & Kahlon, K. S. (2018). Sentiment analysis of demonetization of 500 & 1000 rupee banknotes by Indian government. ICT Express, 4(3), 124-129.

Singh, P., Singh, S., Sohal, M., Dwivedi, Y. K., Kahlon, K. S., & Sawhney, R. S. (2020a). Psychological fear and anxiety caused by COVID-19: Insights from Twitter analytics. Asian Journal of Psychiatry, 54, 102280.

Solnick, R. E., Chao, G., Ross, R. D., Kraft-Todd, G. T., & Kocher, K. E. (2021). Emergency physicians and personal narratives improve the perceived effectiveness of COVID-19 public health recommendations on social media: a randomized experiment. Academic Emergency Medicine, 28(2), 172-183.

Sun, J., & Gloor, P. A. (2021). Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 US States. Future Internet, 13(7), 184.

Sutton, J., Gibson, C. B., Phillips, N. E., Spiro, E. S., League, C., Johnson, B., ... & Butts, C. T. (2015). A cross-hazard analysis of terse message retransmission on Twitter. Proceedings of the National Academy of Sciences, 112(48), 14793-14798.

Teufel, M., Schweda, A., Dörrie, N., Musche, V., Hetkamp, M., Weismüller, B., ... & Skoda, E. M. (2020). Not all world leaders use Twitter in response to the COVID-19 pandemic: impact of the way of Angela Merkel on psychological distress, behaviour and risk perception. Journal of Public Health, 42(3), 644-646.

Thelwall, M. (2021). Can Twitter give insights into international differences in Covid-19 vaccination? Eight countries' English tweets to 21 March 2021. arXiv preprint arXiv:2103.14125.

Tsai, M. H., & Wang, Y. (2021). Analyzing Twitter data to evaluate people's attitudes towards public health policies and events in the era of COVID-19. International Journal of Environmental Research and Public Health, 18(12), 6272.

Tully, M., Dalrymple, K. E., & Young, R. (2019). Contextualizing nonprofits’ use of links on Twitter during the West African Ebola virus epidemic. Communication Studies, 70(3), 313-331.

Wang, B., Liu, B., & Zhang, Q. (2021). An empirical study on Twitter's use and crisis retweeting dynamics amid Covid-19. Natural Hazards, 107(3), 2319-2336.

Wu, V. C. S. (2021). Beyond policy patrons: A ‘MADE’ framework for examining public engagement efforts of philanthropic foundations on Twitter. Public Management Review, 1-25.

Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020). Twitter discussions and emotions about the COVID-19 pandemic: Machine learning approach. Journal of medical Internet research, 22(11), e20550.