Next Article in Journal
Exploring the Path to Enhance Employee Creativity in Chinese MSMEs: The Influence of Individual and Team Learning Orientation, Transformational Leadership, and Creative Self-Efficacy
Previous Article in Journal
Prediction of Heatwave Using Advanced Soft Computing Technique
Previous Article in Special Issue
A Blockchain-Based Efficient and Verifiable Attribute-Based Proxy Re-Encryption Cloud Sharing Scheme
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Information
Volume 14
Issue 8
10.3390/info14080448
information-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association between Obesity and COVID-19: Insights from Social Media Content

by
Mohammed Alotaibi
1,*,
Rajesh R. Pai
2,
Sreejith Alathur
3,
Naganna Chetty
4,*,
Tareq Alhmiedat
1,
Majed Aborokbah
5,
Umar Albalawi
5,
Ashraf Marie
1,
Anas Bushnag
5 and
Vishal Kumar
6
1
Artificial Intelligence and Sensing Technologies Center, University of Tabuk, Tabuk 71491, Saudi Arabia
2
Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
3
Indian Institute of Management, Kozhikode 673570, India
4
Department of Information Science and Engineering, NMAM Institute of Technology, Nitte 574110, India
5
Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
6
Bipin Tripathi Kumaon Institute of Technology, Dwarahat 263653, India
*
Authors to whom correspondence should be addressed.
Information 2023, 14(8), 448; https://doi.org/10.3390/info14080448
Submission received: 20 June 2023 / Revised: 4 August 2023 / Accepted: 4 August 2023 / Published: 8 August 2023
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
Browse Figures
Versions Notes

Abstract

:
The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity prevention policies. Understanding the nature and forums of obese metaphors in social media is the first step in policy intervention. The purpose of this paper is to understand the mutual influence between obesity and COVID-19 and determine its policy implications. This paper analyzes the public responses to obesity using Twitter data collected during the COVID-19 pandemic. The emotional nature of tweets is analyzed using the NRC lexicon. The results show that COVID-19 significantly influences perceptions of obesity; this indicates that existing public health policies must be revisited. The study findings delineate prerequisites for obese disease control programs. This paper provides policy recommendations for improving social media interventions in health service delivery in order to prevent obesity.

1. Introduction

Obesity has become one of the most common chronic diseases in the last two decades as people’s lifestyles have changed worldwide. Obesity is measured using the BMI value, which considers an individual’s mass (weight) and height. The BMI is defined as “the body mass divided by the square of the body height and is universally expressed in units of kg/m2, resulting from mass in kilograms and height in meters” [1].
According to the World Health Organization (WHO) over 1.9 billion people aged 18 and above are overweight, with Asia contributing to around half of all overweight children [2]. Obesity affects over 650 million of these, and approximately 41 million children under the age of five are obese worldwide [2]. In 2016, Obesity Atlas reported that 33.4 percent of females and 24.1 percent of males were overweight or obese [3]. Furthermore, the obesity rate is also very high in some developing countries. For example, for children between the ages of 4 and 8, the prevalence of overweight and obesity in Saudi Arabia is approximately19.2 percent [3].
COVID-19 affects people with poor immune systems more than healthier people [4]. Several studies have found a link between obesity and other chronic conditions such as diabetes and hypertension [5,6,7]. According to these studies, obese children have a higher risk of developing diabetes at a young age. Modern lifestyle, eating habits and less physical activity contributes to obesity especially in children [8]. This implies the necessity of efforts to reduce obesity rates, particularly among children, by raising awareness in the early stages of their lives. As a result, modern technologies such as the Internet of Things, mobile health and social robotics offers excellent and cost-effective solutions to enhance societal understanding.
Modern technologies, intelligent mobile devices, robotics and social networks and the internet has accelerated the design and delivery of healthcare systems, aiding in managing and understanding chronic diseases like obesity and diabetes. This technological revolution established new virtual world with multilingual social networks allowing users to communicate with friends or individuals across geographical, political or economic boundaries. About 3.6 billion internet users use social networks, and these figures are expected to rise as mobile devices and social networks gain attraction [9].
AI technology uses large amounts of raw data as a new area of development and a valuable source of information about people’s perceptions of political decisions or elections. Big data is a concept that describes the massive amount of structured and unstructured information that regularly floods a company [10]. In general, social networks are the most popular sources of big data. The attributes can be collected for each tweet, resulting in a massive volume of data in a short period, with the stream of data increasing sharply second by second. AI technologies help to analyze big data and they assist business decision-makers or governments with forming a picture about the population’s opinions on a social, political or economic issue. The use of natural language processing to analyze healthcare data is prevalent [11]. Twitter data could be analyzed using deep learning techniques [12].
This study used Twitter data to investigate the polarity and emotions of people related to obesity issues during the COVID-19 pandemic. Twitter is a popular microblogging social network with a large number of active users worldwide [13]. It had 152 million monetizable daily active users (mDAU) globally as of the fourth quarter of 2019. The update feed allows registered users to read and publish tweets and follow others [9]. These tweets are significant because governments and decision-makers can use them to study people’s behavior and, as a result, revise their strategies and action plans to meet their targets [14] through a process known as sentiment analysis. Sentiment analysis, also known as opinion mining, uses data mining techniques and methods to determine the opinions (positive or negative) and emotions of a text or message extracted from social media or surveys [15,16]. It helps a company to understand its brand, product, or service by tracking online conversations.
Previous research has found these techniques helpful in predicting and analyzing stock markets [17], elections [18], resource allocations [19] and new products and services [20,21]. Recently, the health domain has taken part in this virtual society. In addition to these fields, some patients post their experiences with a disease, and some physicians have social network accounts and use these to post clinical information to the public. Sentiment analysis has been used in the healthcare sector [22] to study people’s behavior and sentiments towards a specific vaccination [23], lockdown, and the COVID-19 outbreak [24], as well as in social media games [25]. Obese people are more vulnerable to receiving obesity stigma [26]. The discourse referring to obesity/overweight could lead to mental health issues [27,28].
Social media platforms can be used to maintain privacy and self-disclose their physical condition and feelings about sensitive topics such as mental health, diabetes, cancer, heart disease, and HIV/AIDS [29,30,31,32,33,34,35]. Identifying people’s and users’ attitudes and emotions about obesity is critical, especially during the COVID-19 pandemic when nations are in lockdown and there are no regular fitness activities or regimes. The studies [36,37,38] conducted using social media during COVID-19 show that there is a relationship between obesity and COVID-19. As a result, this study aims to understand the mutual influence between obesity and COVID-19 and determine its policy implications using the Twitter platform.
Using Twitter data, AI can learn a great deal about obesity and people’s perceptions of it. The large number of Twitter users worldwide, the high proportion of obese people worldwide, and the advanced AI technologies are sufficient motivations to conduct such a study. Moreover, most existing studies have shown the interrelationship between obesity and COVID-19 on medical grounds. With this motivation in mind, the objective of this study is to understand the mutual influence between obesity and COVID-19 from social media content and to determine the implications of this for policy.
The paper is structured as follows. Section 2 reviews the literature on the mutual influence between obesity and the COVID-19 pandemic, the methodology adopted is presented in Section 3, and in Section 4, the results are presented and discussed. Section 5 outlines the policy implications and Section 6 concludes the work.

2. Literature Review

Obesity and COVID-19 have mutual effects on individuals [39]. The emergence of COVID-19 worsened the health condition of people with obesity and vice versa. A separate review is presented of the impacts that each has on the other in the following sub-sections.

2.1. Impact of Obesity on Those with COVID-19

This section reviews the literature on the impact of obesity on an individual in terms of worsening the COVID-19 pandemic situation. Existing studies have shown that being obese increases the risks of respiratory diseases [39]. The experience with H1N1 influenza revealed that patients with obesity need utmost care in order to control H1N1 flu and similar diseases [40]. Table 1 shows the impact of overweight and obesity on people with the COVID-19 virus.
People with obesity are more vulnerable to the consequences of COVID-19. The mortality rate of obese patients with COVID-19 was greater than that of those who had COVID-19 but were not obese [41]. COVID-19 attacks irrespective of the age of the target but its impact is most severe in older, overweight, and obese people than in young and normal-weight individuals [42,43]. COVID-19 also attacks irrespective of gender, but the mortality risks are greater for male patients than for female patients [44].

2.2. Impact of COVID-19 on Development of Obesity

The COVID-19 pandemic spread to most of the world by March 2020. The association between COVID-19 and obesity is not restricted to BMI but is rather a complex relationship with different levels of obesity [47]. Quarantine, isolation, lockdown, and border sealing are the standard measures adopted to contain the COVID-19 pandemic by the governing authorities of an individual nation. The COVID-19 control measures change an individual’s lifestyle and eating habits and this can lead to overweight and obesity [48]. The availability of processed food lacking in nutrients was prevalent at the beginning of the pandemic as transportation and other facilities were blocked due to lockdowns in different counties [41,49]. High levels of consumption of non-nutrients and processed food could increase the risks of overweight and obesity [42,49]. Table 2 shows the relationship between COVID-19 and obesity development.
The COVID-19 control measures, such as lockdowns, disrupted individuals’ activities [50]. This disruption of normal activities induced stress, anxiety, anger, and depression. Stress makes individuals eat more and it reduces physical activity, leading to increased overweight and obesity [35,48]. Specifically, the lockdowns resulted in lifestyle changes, mental health issues, and increased weight and obesity in people [47]. In general, the COVID-19 pandemic is increasing obesity by disturbing the normal activities of people.

3. Methodology

A qualitative (i.e., user-generated content) methodology is followed in this study. The adopted method is a two-fold approach, with the first part supporting data extraction and the second part supporting data analysis. In the first part, the data were extracted from the Twitter social media platform by creating an application programming interface (API) using the RStudio tool. As the keyword ‘obesity’ is appropriate and generic, it was used for searching for relevant tweets on Twitter, following the approach described in the research of Bharti et al. (2016) [51]. While searching for ‘obesity’, the program extracted 52,077 posts from the Twitter website during the second wave of the COVID-19 pandemic (February 2021 to August 2021).
In the second part of the methodology, a pre-processing procedure was applied to the extracted tweets to smooth out or clean the data by eliminating content such as digits, punctuations, stop words, and website addresses. Stemming was applied to transform the different appearances of a phrase into its root. A sentiment analysis approach was used to assess the responses of Twitter users in the form of emotions. The emotional nature of tweets is analyzed using NRC (National Research Council) lexicon [52]. The wordcloud approach is used to group the words which depict different emotions. Topic modeling was applied to the pre-processed Twitter content to understand the interrelationship between obesity and COVID-19. To provide deeper insights into the data, a few tweets showing the relationship between obesity and COVID-19 are presented in the following section, and their purpose is interpreted. By making collective interpretations, inferences are drawn from the results to create policy suggestions.

4. Results and Discussion

The results obtained from the sentiment analysis, topic modeling and manual evaluation to identify specific themes concerned with obesity are discussed here. The analysis is carried out on 52,077 tweets.

4.1. Sentiment Analysis

Polarity and the emotions associated with obesity can be understood by applying sentiment analysis to the extracted posts. In this study, sentiment analysis was performed with Syuzhet and the sentiment analysis packages of RStudio. The results of the sentiment analysis of the posts, based on polarities and emotions, are calculated and depicted in Figure 1.
Figure 1 shows that the total negative sentiment count (72.44 K) is higher than the positive count (41.07 K, signifying that people have mainly negative opinions about obesity. In addition, people primarily expressed sadness (55.89 K), disgust (47.61 K), and fear (29.27 K) in their posts, among other emotional components. The expression of negative emotions may have been due to the pandemic-induced curfews and lockdowns across nations, resulting in the closure of sports centers, gyms, and other facilities, leading to food consumption for comfort.
A word cloud was drawn to depict the contents of the posts according to their emotional components, i.e., joy, sadness, surprise, anger, fear or disgust [53]. Figure 2 shows the word cloud for the retrieved Twitter content during the COVID-19 pandemic. The word cloud presents emotions and the words used to express them in different fonts and colors. The term “weight” has a high frequency, which reveals that individuals may have gained more weight during the pandemic and obesity increased.
Table 3 shows the association between the most frequent words found in the tweets. The cell values of the adjacency matrix indicate the co-occurring frequency of the terms. These values reveal that obesity is most closely associated with food, health, people and COVID-19. Obesity could have been increased due to the unhealthy eating habits of an individual and the emergence of COVID-19. The weight of a person co-appeared most with the terms ‘obesity’ and ‘people’ in the discussions. Similarly, the word ‘risk’ was used alongside the terms ‘obesity’, ‘diabetes’, and ‘COVID’. The life of an individual is more at risk with diseases such as obesity, diabetes, and COVID-19.
The graphical representation of the adjacency matrix or the association among the frequent terms used to discuss obesity during the COVID-19 pandemic is depicted in Figure 3. The nodes represent the terms, and the edges represent the relationship strength between the words. Obesity is the central topic of discussion because it occurred most time, and the node labeled ‘obesity’ is centered in the graph. The obesity and COVID-19 nodes are connected, and the distance between the two is average. The connection and the average distance between both diseases indicate that they influence each other.

4.2. Evaluation of Selected Tweets

In this section, a few of the tweets showing the interrelationship between obesity and COVID-19 are selected and their meaning interpreted. The selected tweets representing the influence of obesity on COVID-19 are categorized as risk, hospitalization, mortality, and future possibility, as shown in Table 4. The tweets numbered 1 to 6 reveal the dangers of COVID-19 for persons with obesity. That is, those with obesity are more vulnerable to COVID-19 infection. The hospitalization possibilities of obese individuals are presented through tweets 7 and 8. In these tweets, obesity is viewed as one of the most significant causes of hospitalization in COVID-19 patients.
The tweets numbered 9 to 13 show the mortality/death rate of COVID-19 patients with obesity. That is, there is a greater possibility of dying from COVID-19 for patients who are obese. The last tweet, number 14, alerts and cautions people about the future by saying that if you survive COVID-19, you may be at risk of obesity in the future. Twitter users believed that obese people are more vulnerable to the risks associated with COVID-19 in one way or another. Some users tweeted about the causes of obesity also. For brevity, only the tweets that expressly mention the relationship between obesity and COVID-19 are presented.
The selected tweets that discussed the influence of COVID-19 on obesity are briefly described here. One tweet saying “Coronavirus lockdown has been shown to worsen child obesity worldwide; reports have shown children have LESS physic…” reveals the impact of COVID-19 on children in terms of developing obesity. Another tweet, “Health Risks of Obesity. More people have become obese with all of @thedemocrats’ China-pandemic bailouts & welfar…” states that more people developed obesity during the COVID-19 pandemic than before it. Similarly, the following tweet, “No question that prolonged school closure has led to a spike in Pediatric obesity,” highlights the influence of COVID-19 on children in terms of increased obesity.
One tweet commented on the influence of physical activity and the intake of high-calorie foods: “#Obesity is a result of several factors, like reduced #physicalactivity, increased availability of #highcalorie…”. It concluded that reduced physical activity and the consumption of high-calorie foods resulted in increased obesity. The tweet saying “During #COVID-19 pandemic and lockdowns there have been huge rises in stress, depression, anxiety, alcoholism, suici…” indicates the development of stress, depression, anxiety, consumption of alcohol and suicide attempts during the COVID-19 pandemic. Both tweets reveal the indirect influence of the COVID-19 pandemic on obesity development.

5. Policy Implications

The policy implications are derived from the results of the analysis and they are discussed below. For collective suggestions of policies to contain the emergence and impact of COVID-19, this study’s outcome is synthesized from literature, sentiment analysis, and the manual evaluation of tweets.
Vaccination is one of the essential measures employed to contain COVID-19 and similar infectious diseases [54]. Providing vaccination to all the world’s citizens quickly is difficult, as it requires financial investment and a strategic plan to schedule the manufacturing. Until the completion of the vaccination program for all citizens, there will be the appreciable implementation of non-pharmaceutical measures such as lockdowns, social distancing, quarantine, and isolation to control COVID-19. Therefore, the primary effort of each nation should be focused on the vaccination of everyone while also adopting non-pharmaceutical measures. As the vaccine is less effective in persons who are obese or overweight [54], the emphasis could be extended to specific cases (obese) rather than concentrating only on general cases during the testing and development of vaccines.
The consumption of quality food, and daily physical activity could keep an individual healthy. As the frequent consumption of processed food develops overweight/obesity in an individual [49], people may be motivated to use better quality food products, or policy makers should regulate the food processing system to meet current requirements. The collective impact of processed food and the lack of physical activity is to increase the risk of contracting COVID-19. Therefore, the governance authorities could improve the infrastructure to make physical exercise accessible to everyone.
The sentiment analysis of Twitter users’ responses reveals that most expressed their views through negative emotions such as sadness, disgust, and fear during the COVID-19 pandemic. A frequent expression of negative emotions may result in depression, stress, and anxiety in an individual. Reducing negative emotions and their impact on individuals could be achieved through intervention policies using intermediaries such as social media platforms. Social media platforms should intervene positively to counter negative feelings and nullify the panic that people feel. The countering emotions of an individual may be expressed through discussions that aim to persuade and shape behavior.
The word cloud and adjacency graph show the interrelationships between the different issues associated with people and COVID-19. The larger font size of the words COVID, food, weight, and obesity in the word cloud reveals that consuming low-quality food leads to increased weight, which causes obesity and then increases the severity of a COVID-19 infection in an individual. The connection between COVID, diabetes, obesity, and weight in the adjacency graph shows that people with high weight, diabetes, and obesity are most prone to the risks of the COVID-19 pandemic. The manual evaluation of the selected tweets also shows a mutual influence between obesity and the COVID-19 pandemic. Hence, the analysis of different sections such as literature, sentiments, adjacency matrix, and tweets reveals the stigma of obesity. Therefore, policy makers could emphasize associated diseases rather than concentrating only on the control of COVID-19.

6. Conclusions

Social media acts as an enabler for maintaining privacy and disclosing people’s feelings about highly sensitized topics such as obesity, mental health, and diabetes. The study presented in this paper emphasizes obesity and COVID-19. This study aimed to analyze Twitter content to understand the mutual influences between obesity and the COVID-19 pandemic. The analysis of the Twitter content was carried out through sentiment analysis, a word cloud and adjacency graph, and the manual evaluation of tweets.
The sentiment analysis results show that there were more negative opinions than positive opinions and this demonstrates that people have negative feelings about the situation during the pandemic. In addition, people primarily expressed feelings of sadness, disgust, and fear in their posts, among other emotional components. The terms included in the word cloud reveal that the consumption of unhealthy food leads to people becoming overweight, leading to obesity and higher risks associated with COVID-19.
The adjacency graph analysis shows the interrelationship between different issues, i.e., obesity, weight, diabetes, and COVID-19. Moreover, the selected responses from Twitter users reveal that obesity and COVID-19 have a mutual influence on each other; a mutual association exists between both pandemics, and the control of both is required to reduce their impact on human beings. With the results and the existing research, this study can be generalized to provide the relationship between disasters and health issues.
This study considered only the Twitter social media content to understand the relationship between COVID-19 and obesity. The outcome can be strengthened by considering the use of other social media content during the pandemic and robust machine learning techniques.

Author Contributions

Conceptualization, M.A. (Mohammed Alotaibi), R.R.P., A.B. and N.C.; methodology, S.A., T.A., M.A. (Majed Aborokbah) and U.A.; software, M.A. (Mohammed Alotaibi), N.C., R.R.P., V.K. and A.M.; validation, S.A., A.M., A.B., T.A. and M.A. (Majed Aborokbah); formal analysis, M.A., V.K. and A.M.; investigation, N.C., M.A (Mohammed Alotaibi) and R.R.P.; resources, M.A. (Mohammed Alotaibi), T.A. and A.M.; data curation, A.B., M.A. (Majed Aborokbah) and U.A.; writing—original draft preparation, M.A. (Mohammed Alotaibi), R.R.P. and N.C.; writing—review and editing, S.A., A.M., V.K. and A.B.; visualization, N.C., U.A. and M.A. (Mohammed Alotaibi); supervision, S.A., M.A. and A.B.; project administration, M.A. (Mohammed Alotaibi); funding acquisition, M.A. (Mohammed Alotaibi). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Deanship of Scientific Research (DSR) University of Tabuk, Tabuk, Saudi Arabia, grant number 1440-258 and The APC was funded by 1440-258.

Data Availability Statement

Data is unavailable due to privacy or ethical restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ferrera, L. Focus on Body Mass Index and Health Research; Nova Science Publishers: New York, NY, USA, 2006. [Google Scholar]
  2. WHO. World Health Organization. 2021. Available online: http://www.who.int/home (accessed on 3 August 2023).
  3. Worldobesity. World Obesity Federation|Data. 2018. Available online: https://www.worldobesity.org/data/ (accessed on 21 May 2018).
  4. Chetty, N.; Alathur, S.; Kumar, V. 2019-nCoV disease control and rehabilitation: Insights from twitter analytics. In Proceedings of the 2020 5th International Conference on Computing, Communication and Security (ICCCS), Patna, India, 14–16 October 2020; pp. 1–4. [Google Scholar]
  5. Nguyen, N.; Nguyen, X.; Lane, J.; Wang, P. Relationship between Obesity and Diabetes in a US Adult Population: Findings from the National Health and Nutrition Examination Survey, 1999–2006. Obes. Surg. 2010, 21, 351–355. [Google Scholar]
  6. Eckel, R.; Kahn, S.; Ferrannini, E.; Goldfine, A.; Nathan, D.; Schwartz, M.; Smith, R.; Smith, S. Obesity and Type 2 Diabetes: What Can Be Unified and What Needs to Be Individualized? Diabetes Care 2011, 34, 424–1430. [Google Scholar]
  7. Rossner, S. Obesity and type 2 diabetes. Pract. Diabetes Int. 2001, 18, 263–264. [Google Scholar] [CrossRef]
  8. Al Dhaifallah, A.; Mwanri, L.; Aljoudi, A. Childhood obesity in Saudi Arabia: Opportunities and challenges. Saudi J. Obes. 2015, 3, 2–7. [Google Scholar]
  9. Statista. Most Used Social Media 2020. Statista. 2020. Available online: https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/ (accessed on 1 November 2020).
  10. Buyya, R. Big Data; Morgan Kaufmann Publisher: Burlington, VT, Canada, 2016. [Google Scholar]
  11. Elbattah, M.; Arnaud, É.; Gignon, M.; Dequen, G. The Role of Text Analytics in Healthcare: A Review of Recent Developments and Applications. Healthinf 2021, 5, 825–832. [Google Scholar]
  12. Ainapure, B.S.; Pise, R.N.; Reddy, P.; Appasani, B.; Srinivasulu, A.; Khan, M.S.; Bizon, N. Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches. Sustainability 2023, 15, 2573. [Google Scholar]
  13. Gautam, A.; Top Microblogging Sites List with High pr Andbest. My WP Tips. 2014. Available online: http://mywptips.com/top-microblogging-sites-list/ (accessed on 21 July 2021).
  14. Daoud, D.M. Detecting and ranking events in Twitter using diversity analysis. Int. J. Bus. Intell. Data Min. 2018, 13, 129–146. [Google Scholar]
  15. Liu, B. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions; Cambridge University Press: London, UK, 2015. [Google Scholar]
  16. Er, M.J.; Liu, F.; Wang, N.; Zhang, Y.; Pratama, M. User-level twitter sentiment analysis with a hybrid approach. In International Symposium on Neural Networks; Springer: Cham, Switzerland, 2016; pp. 426–433. [Google Scholar]
  17. Mittal, A.; Goel, A. Stock Prediction Using Twitter Sentiment Analysis. 2012. Available online: http://cs229.stanford.edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis.pdf (accessed on 30 April 2020).
  18. Wang, H.; Can, D.; Kazemzadeh, A.; Bar, F.; Narayanan, S. A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In Proceedings of the ACL 2012 System Demonstrations, Jeju Island, Republic of Korea, 9–11 July 2012; pp. 115–120. [Google Scholar]
  19. Kudyba, S.; Hamar, G.B.; Gandy, W.M. Utilising neural network applications to enhance efficiency in the healthcare industry: Predicting populations of future chronic illness. Int. J. Bus. Intell. Data Min. 2006, 1, 371–383. [Google Scholar]
  20. Jebaseeli, A.N.; Kirubakaran, E. A survey on sentiment analysis of (product) reviews. Int. J. Comput. Appl. 2012, 47, 36–39. [Google Scholar]
  21. Serrano-Guerrero, J.; Olivas, J.A.; Romero, F.P.; Herrera-Viedma, E. Sentiment analysis: A review and comparative analysis of web services. Inf. Sci. 2015, 311, 18–38. [Google Scholar]
  22. Khan, M.T.; Khalid, S. Sentiment analysis for health care. In Big Data: Concepts, Methodologies, Tools, and Applications; IGI Global: Hershey, PA, USA, 2016; pp. 676–689. [Google Scholar]
  23. Salathe, M.; Khandelwal, S. Assessing vaccination sentiments with online social media: Implications for infectious disease dynamics and control. PLoS Comput. Biol. 2011, 7, e1002199. [Google Scholar] [CrossRef] [Green Version]
  24. Barkur, G.; Vibha; Kamath, G.B. Sentiment analysis of nationwide lockdown due to COVID-19 outbreak: Evidence from India. Asian J. Psychiatry 2020, 51, 102089. [Google Scholar]
  25. Pai, R.R.; Alathur, S. Social media games: Insights from Twitter analytics. Int. J. Web Based Communities 2020, 16, 34–50. [Google Scholar]
  26. Lewis, S.; Thomas, S.L.; Blood, R.W.; Castle, D.J.; Hyde, J.; Komesaroff, P.A. How do obese individuals perceive and respond to the different types of obesity stigma that they encounter in their daily lives? A qualitative study. Soc. Sci. Med. 2011, 73, 1349–1356. [Google Scholar]
  27. Gailey, J.A. The violence of fat hatred in the “obesity epidemic” discourse. Humanit. Soc. 2022, 46, 359–380. [Google Scholar] [CrossRef]
  28. Hunger, J.M.; Major, B.; Blodorn, A.; Miller, C.T. Weighed down by stigma: How weight-based social identity threat contributes to weight gain and poor health. Soc. Personal. Psychol. Compass 2015, 9, 255–268. [Google Scholar]
  29. Gabarron, E.; Dorronzoro, E.; Rivera-Romero, O.; Wynn, R. Diabetes on Twitter: A Sentiment Analysis. J. Diabetes Sci. Technol. 2019, 13, 439–444. [Google Scholar]
  30. Manikonda, L.; Beigi, G.; Liu, H.; Kambhampati, S. Twitter for sparking a movement, reddit for sharing the moment: #metoo through the lens of social media. arXiv 2018, arXiv:1803.08022. [Google Scholar]
  31. Altman, I.; Taylor, D.A. Social Penetration: The Development of Interpersonal Relationships; Holt, Rinehart & Winston: New York, NY, USA, 1973. [Google Scholar]
  32. Joinson, A.N.; Paine, C.B. Self-disclosure, privacy and the Internet. Oxf. Handb. Internet Psychol. 2007, 3, 237–252. [Google Scholar]
  33. Ramallo, J.; Kidder, T.; Albritton, T.; Blick, G.; Pachankis, J.; Grandelski, V.; Kershaw, T. Exploring social networking technologies as tools for HIV prevention for men who have sex with men. AIDS Educ. Prev. 2015, 27, 298–311. [Google Scholar] [CrossRef] [Green Version]
  34. Young, S.D.; Rivers, C.; Lewis, B. Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes. Prev. Med. 2014, 63, 112–115. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Abbas, A.M.; Fathy, S.K.; Fawzy, A.T.; Salem, A.S.; Shawky, M.S. The mutual effects of COVID-19 and obesity. Obes. Med. 2020, 19, 100250. [Google Scholar] [CrossRef] [PubMed]
  36. Robinson, E.; Boyland, E.; Chisholm, A.; Harrold, J.; Maloney, N.G.; Marty, L.; Mead, B.R.; Noonan, R.; Hardman, C.A. Obesity, eating behavior and physical activity during COVID-19 lockdown: A study of UK adults. Appetite 2021, 156, 104853. [Google Scholar] [CrossRef] [PubMed]
  37. Eşer Durmaz, S.; Keser, A.; Tunçer, E. Effect of emotional eating and social media on nutritional behavior and obesity in university students who were receiving distance education due to the COVID-19 pandemic. J. Public Health 2022. [Google Scholar] [CrossRef] [PubMed]
  38. Pearl, R.L. Weight stigma and the “Quarantine-15”. Obesity 2020, 28, 1180. [Google Scholar] [CrossRef]
  39. Cai, Q.; Chen, F.; Wang, T.; Luo, F.; Liu, X.; Wu, Q.; He, Q.; Wang, Z.; Liu, Y.; Liu, L.; et al. Obesity and COVID-19 severity in a designated hospital in Shenzhen, China. Diabetes Care 2020, 43, 1392–1398. [Google Scholar] [CrossRef]
  40. Dietz, W.; Santos-Burgoa, C. Obesity and its implications for COVID-19 mortality. Obesity 2020, 28, 1005. [Google Scholar] [CrossRef] [Green Version]
  41. Popkin, B.M.; Du, S.; Green, W.D.; Beck, M.A.; Algaith, T.; Herbst, C.H.; Alsukait, R.F.; Alluhidan, M.; Alazemi, N.; Shekar, M. Individuals with obesity and COVID-19: A global perspective on the epidemiology and biological relationships. Obes. Rev. 2020, 21, e13128. [Google Scholar]
  42. Busetto, L.; Bettini, S.; Fabris, R.; Serra, R.; Pra, C.D.; Maffei, P.; Rossato, M.; Fioretto, P.; Vettor, R. Obesity and COVID-19: An Italian snapshot. Obesity 2020, 28, 1600–1605. [Google Scholar] [CrossRef]
  43. Gao, F.; Zheng, K.I.; Wang, X.-B.; Sun, Q.-F.; Pan, K.-H.; Wang, T.-Y.; Chen, Y.-P.; Targher, G.; Byrne, C.D.; George, J.; et al. Obesity is a risk factor for greater COVID-19 severity. Diabetes Care 2020, 43, e72–e74. [Google Scholar] [CrossRef]
  44. Nakeshbandi, M.; Maini, R.; Daniel, P.; Rosengarten, S.; Parmar, P.; Wilson, C.; Kim, J.M.; Oommen, A.; Mecklenburg, M.; Salvani, J.; et al. The impact of obesity on COVID-19 complications: A retrospective cohort study. Int. J. Obes. 2020, 44, 1832–1837. [Google Scholar] [CrossRef]
  45. Nagy, É.; Cseh, V.; Barcs, I.; Ludwig, E. The Impact of Comorbidities and Obesity on the Severity and Outcome of COVID-19 in Hospitalized Patients—A Retrospective Study in a Hungarian Hospital. Int. J. Environ. Res. Public Health 2023, 20, 1372. [Google Scholar] [CrossRef]
  46. Guo, S.A.; Bowyer, G.S.; Ferdinand, J.R.; Maes, M.; Tuong, Z.K.; Gillman, E.; Liao, M.; Lindeboom, R.G.H.; Yoshida, M.; Worlock, K.; et al. Obesity is associated with attenuated tissue immunity in COVID-19. Am. J. Respir. Crit. Care Med. 2023, 207, 566–576. [Google Scholar] [CrossRef]
  47. Dohet, F.; Loap, S.; Menzel, A.; Iddir, M.; Dadoun, F.; Bohn, T.; Samouda, H. Obesity considerations during the COVID-19 outbreak. Int. J. Vitam. Nutr. Res. 2021. [Google Scholar] [CrossRef]
  48. Mattioli, A.V.; Pinti, M.; Farinetti, A.; Nasi, M. Obesity risk during collective quarantine for the COVID-19 epidemic. Obes. Med. 2020, 20, 100263. [Google Scholar] [CrossRef]
  49. Pries, A.M.; Ferguson, E.L.; Sharma, N.; Upadhyay, A.; Filteau, S. Exploratory analysis of nutritional quality and metrics of snack consumption among Nepali children during the complementary feeding period. Nutrients 2019, 11, 2962. [Google Scholar] [CrossRef] [Green Version]
  50. Stavridou, A.; Kapsali, E.; Panagouli, E.; Thirios, A.; Polychronis, K.; Bacopoulou, F.; Psaltopoulou, T.; Tsolia, M.; Sergentanis, T.N.; Tsitsika, A. Obesity in Children and Adolescents during COVID-19 Pandemic. Children 2021, 8, 135. [Google Scholar] [CrossRef]
  51. Bharti, S.K.; Vachha, B.; Pradhan, R.K.; Babu, K.S.; Jena, S.K. Sarcastic sentiment detection in tweets streamed in real time: A big data approach. Digit. Commun. Netw. 2016, 2, 108–121. [Google Scholar]
  52. Mohammad, S.M.; Turney, P.D. Nrc Emotion Lexicon; National Research Council: Ottawa, ON, Canada, 2013; Volume 2, p. 234.
  53. De Albornoz, J.C.; Plaza, L.; Gervás, P. SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis. In LREC; European Language Resources Association: Paris, France, 2012; Volume 12, pp. 3562–3567. [Google Scholar]
  54. Kissler, S.M.; Tedijanto, C.; Goldstein, E.; Grad, Y.H.; Lipsitch, M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science 2020, 368, 860–868. [Google Scholar] [CrossRef]
Information 14 00448 g001
Figure 1. Polarity and emotions related to obesity.
Figure 1. Polarity and emotions related to obesity.
Information 14 00448 g001
Information 14 00448 g002
Figure 2. Emotion word cloud for obesity.
Figure 2. Emotion word cloud for obesity.
Information 14 00448 g002
Information 14 00448 g003
Figure 3. Adjacency graph of associated terms.
Figure 3. Adjacency graph of associated terms.
Information 14 00448 g003
Table 1. The impact of obesity on COVID-19-associated risks.
Table 1. The impact of obesity on COVID-19-associated risks.
AuthorsPurposeMethodologyOutcomeRemarks
Popkin et al. (2020) [41]To identify the link between obesity and COVID-19 risk of infection and the medical consequences of infectionMeta-analysis of literature on COVID-19 in Chinese and English languagesIndividuals with obesity are more prone to COVID-19 infection, hospitalization, requiring intensive care, and mortality Obesity increases the risks of COVID-19
Busetto et al. (2020) [42]To evaluate the relationship between the severity of COVID-19 infection and obesityThe statistical analysis method is used on hospitalized COVID-19 patients with different age groups and obesityOverweight and obese patients suffering from COVID-19 requires the facilities of ventilation and the intensive care unit than the normal-weight patients Obesity increases the severity of COVID-19 in patients
Gao et al. (2020) [43]To understand whether obesity is a risk factor for COVID-19 severity or notStatistical analysis of hospitalized COVID-19 patients (75 with obesity and 75 without obesity) Obese individuals were classified as severe COVID-19 patientsObesity increases the severity of COVID-19 in patients
Cai et al. (2020) [39]To understand the association between obesity and severity of COVID-19Statistical analysis was applied to data of consecutively hospitalized COVID-19 patientsThe severity of COVID-19 in overweight patients was greater than in normal-weight patients
The severity of COVID-19 in obese patients was greater than in overweight patients 		Obesity increases the severity of COVID-19 patients
Nakeshbandi et al. (2020) [44]To illustrate the association between obesity and COVID-19A retrospective cohort study on hospitalized COVID-19 patientsOverweight and obese people had a higher mortality risk than normal-weight people.
Overweight and obese people were more likely to require intubation than normal-weight people.
Obesity raises the risk of mortality in males		Obesity increases COVID-19′s associated risks
Nagy et al. (2023) [45]To understand the impact of obesity on COVID-19 patientsObservational study on hospitalized COVID-19 infectedObesity is found as the most significant risk factor for COVID-19 patientsObesity increases COVID-19′s associated risks
Guo et al. (2023) [46]To identify the impact of obesity on respiratory tract immunity for COVID-19 infected Examined the ventilated COVID-19 infected patients with obese and non-obeseThe strength of the nasal immune cells of obese children is reducedBlunted tissue immune responses in obese patients
Table 2. The impact of COVID-19 on obesity.
Table 2. The impact of COVID-19 on obesity.
AuthorsPurpose Results
Abbas et al. (2020) [35]To understand the mutual effects between COVID-19 and obesityObesity increased during the COVID-19 pandemic.
Obesity is riskier for COVID-19 patients
Mattioli et al. (2020) [48]To understand how the COVID-19 pandemic affected the risk of becoming obese COVID-19 control measures induced stress, anxiety and anger.
Stress changes lifestyle and results in obesity
Stavridou et al. (2021) [50]To evaluate obesity among people of different ages during the COVID-19 pandemicThe emergence of COVID-19 disrupted the activities of individuals.
Increased food intake and reduced physical work are leading to obesity
Dohet et al. (2021) [49]To understand obesity during the COVID-19 pandemicCOVID-19-induced lockdown resulted in changes in lifestyle, mental health, and weight, leading to obesity
Table 3. The adjacency matrix of associated terms.
Table 3. The adjacency matrix of associated terms.
TermsObesityFoodHealthPeopleCanProblemWeightRiskDiabetesCOVID
Obesity31,451228034722784144420931640162919362253
Food228030311479141102141856327957
Health3472147947372531911465173198191199
People2784141253463428384534179181496
Can1444102191283243527206150162126
Problem2093141814658427223528182264
Weight1640561735342062827681116061
Risk162932198179150181112267270341
Diabetes19367919118116222602702461213
COVID22535719949612664613412133464
Table 4. The text of the selected tweets.
Table 4. The text of the selected tweets.
NumberTweetCategory
1#COVID19: #obesity and Excess Weight Increase Severe Illness Risk; Racial and Ethnic Disparities Persist…Risk
2@MidwestHedgie Obesity arguably worse than COVID wrt health impacts & cost. There should be more attention here. Si…
3Fat shaming, BMI and alienation: COVID-19 brought new stigma to large-sized people by @marialaganga for the…
4Obesity increases the risk of covid19 as the body is more prone to infections and immunity may not be able to fight…
5@nytimes Obesity = #1 risk factor for Covid
6Next there will be a link between obesity, a sedentary lifestyle and dying of COVID
7Main reason for Covid hospitalization was obesityHospitalization
8@TuckerCarlson @damonroberts 78 percent of people hospitalized for COVID-19 were obese. Obesity caused by bad food…
9Risk of death from Covid increased by 90% in all people with a BMI over 40Mortality
10Biggest contributors to covid deaths, age, obesity and heart conditions
11@DrTomFrieden 94% of all covid deaths were a result of comorbidities preventing the immune system from working a fu…
12@nytimes COVID-19 has a higher mortality rate among the morbidly obese
13@_Simonian Misleading headline, very few healthy people die of COVID, most of the people who die have another co-mo…
14@ABC If you don’t succumb to Covid, obesity is next in lineCaution
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Alotaibi, M.; Pai, R.R.; Alathur, S.; Chetty, N.; Alhmiedat, T.; Aborokbah, M.; Albalawi, U.; Marie, A.; Bushnag, A.; Kumar, V. Association between Obesity and COVID-19: Insights from Social Media Content. Information 2023, 14, 448. https://doi.org/10.3390/info14080448

AMA Style

Alotaibi M, Pai RR, Alathur S, Chetty N, Alhmiedat T, Aborokbah M, Albalawi U, Marie A, Bushnag A, Kumar V. Association between Obesity and COVID-19: Insights from Social Media Content. Information. 2023; 14(8):448. https://doi.org/10.3390/info14080448

Chicago/Turabian Style

Alotaibi, Mohammed, Rajesh R. Pai, Sreejith Alathur, Naganna Chetty, Tareq Alhmiedat, Majed Aborokbah, Umar Albalawi, Ashraf Marie, Anas Bushnag, and Vishal Kumar. 2023. "Association between Obesity and COVID-19: Insights from Social Media Content" Information 14, no. 8: 448. https://doi.org/10.3390/info14080448

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop