Defining facets of social distancing during the COVID-19 pandemic: Twitter analysis

Kwon, Jiye; Grady, Connor B.; Feliciano, Josemari; Fodeh, Samah Jamal

doi:10.1016/j.jbi.2020.103601

Cited by 36 publications

(32 citation statements)

References 23 publications

(26 reference statements)

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“…The term social distancing is a relatively older concept; however, its enactment has seen variations with the changing times. Early studies of the preventive measure of social distancing found that it is a multi-faceted intervention where facets or stages unfold as the pandemic impacts society (Kwon et al, 2020). These facets may change in subsequent studies as deeper insights into COVID 19 evolves.…”

Section: Preventive Measures and Covid 19mentioning

confidence: 99%

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

et al. 2021

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The pandemic COVID 19 has altered individuals' daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as 'lockdown,' 'emergency,' or curfew' to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member's behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February-June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country's citizens when facing future unexpected exogenous shocks.

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Section: Preventive Measures and Covid 19mentioning

confidence: 99%

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

et al. 2021

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“…Themes of previous studies that focus on exploration of, description of, correlation of, or predictive modeling with Twitter data during COVID-19 pandemic include sentiment analysis [17], [25], [26], [27], [28], public attitude/interest measurement [21], [29], [30], [31], content analysis [32], [33], [15], [34], [35], [36], topic modeling [37], [16], [38], [39], [40], [26], [27], analysis of misinformation, disinformation, or conspiracies [41], [20], [42], [43], [44], [45], [46], outbreak detection or disease nowcasting/forecasting [19], [18], and more [47], [48], [49], [50], [51], [52]. Similarly, data from other social media channels (e.g.…”

Section: Going Beyond Correlationsmentioning

confidence: 99%

Causal Modeling of Twitter Activity During COVID-19

Gencoglu

Gruber²

2020

Preprint

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Understanding the characteristics of public attention and perception is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g. number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and perception. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.

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“…One of the challenging issues during the COVID-19 pandemic around the world is spreading information and misinformation. Many reviewed articles mentioned this significant issue in outbreaks and other natural disasters (Smith 2010;Doan et al 2011;Sreenivasan et al 2011;Thomson et al 2012;Cho et al 2013;Carter 2014;Kostkova et al 2014;Oyeyemi et al 2014;Cooper et al 2015;Takahashi et al 2015;Fu et al 2016;Mukkamala and Beck 2016;Wang et al 2016;Chatfield and Reddick 2017;Ortiz-Martínez and Jiménez-Arcia 2017;Subba and Bui 2017;Ahmed et al 2018;Ferrara 2020;Golder et al 2020;Kouzy et al 2020;Kwon et al 2020;Rosenberg et al 2020;Rufai and Bunce 2020;Sarker et al 2020;Sharma et al 2020;Wicke and Bolognesi 2020;Yang et al 2020). It was claimed that publishing misinformation about the COVID-19 pandemic has reached alarming levels that endanger public health (Kouzy et al 2020).…”

Section: Disseminating Informationmentioning

confidence: 99%

“…Rapid assessment during the first days after a natural disaster and during the response is vital to improve operations. Some of the reviewed articles mentioned this significant topic as a function of Twitter in natural disasters (Smith 2010;Fu et al 2016;Ahmed et al 2018;Golder et al 2020;Kwon et al 2020;Sarker et al 2020;Wicke and Bolognesi 2020). For example, Sarker et al (2020) found that Twitter data on the characteristics of people infected with COVID-19 were as useful as clinical data (Sarker et al 2020).…”

Section: Assessmentmentioning

confidence: 99%

“…For example, Sarker et al (2020) found that Twitter data on the characteristics of people infected with COVID-19 were as useful as clinical data (Sarker et al 2020). Kwon et al (2020) claimed that using tweet analysis in the United States, various aspects of social distancing (methods of preventing infection) were identified and analyzed (Kwon et al 2020).…”

Section: Assessmentmentioning

confidence: 99%

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Saving Lives and Changing Minds with Twitter in Disasters and Pandemics: A Literature Review

Seddighi

Salmani

Seddighi

2020

Journalism and Media

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Twitter is a major tool for communication during emergencies and disasters. This study aimed to investigate Twitter use during natural hazards and pandemics. The included studies reported the role of Twitter in disasters triggered by natural hazards. Electronic databases were used for a comprehensive literature search to identify the records that match the mentioned inclusion criteria published through May 2020. Forty-five articles met the selection criteria and were included in the review. These indicated ten functions of Twitter in disasters, including early warning, dissemination of information, advocacy, assessment, risk communication, public sentiment, geographical analysis, charity, collaboration with influencers and building trust. Preventing the spread of misinformation is one of the most important issues in times of disaster, especially pandemics. Sharing accurate, transparent and prompt information from emergency organizations and governments can help. Moreover, analyzing Twitter data can be a good way to understand the mental state of the community, estimate the number of injured people, estimate the points affected by disasters and model the prevalence of epidemics. Therefore, various groups such as politicians, government, nongovernmental organizations, aid workers and the health system can use this information to plan and implement interventions.

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Defining facets of social distancing during the COVID-19 pandemic: Twitter analysis

Abstract: Graphical abstract

Cited by 36 publications

References 23 publications

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

Causal Modeling of Twitter Activity During COVID-19

Saving Lives and Changing Minds with Twitter in Disasters and Pandemics: A Literature Review

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