Wearable Devices for Health and Wellbeing: Design Insights from Twitter

El‐Gayar, Omar F.; Nasralah, Tareq; Elnoshokaty, Ahmed

doi:10.24251/hicss.2019.467

Cited by 13 publications

(14 citation statements)

References 18 publications

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“…Second, Twitter offers an application programming interface (API) to access its database (Twitter Inc., 2020). El-Gayar et al (2019) used Twitter data to study the acceptance and consumer feedback on the use of wearable devices. To collect relevant tweets, snscrape API (JustAnotherArchivist, 2020) was used to get HTML links of tweets that fell within the search criteria, and then tweepy API (Roesslein, 2020) was used to download tweets using the HTML links.…”

Section: Methodsmentioning

confidence: 99%

Clothing Consumption During the COVID-19 Pandemic: Evidence From Mining Tweets

Liu

Xia

Lang

2021

Clothing and Textiles Research Journal

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Understanding how consumers have shifted in clothing consumption in the midst of the global COVID-19 pandemic is critical for fashion clothing brands and businesses to identify what value means to consumers to locate growth opportunities. This exploratory study intends to provide a picture of consumers’ clothing consumption evolution while going through the pandemic crisis. We take a viewpoint that integrates the perspectives of life status changes and stress coping to examine consumers’ responses to clothing consumption during the COVID-19 global pandemic. A total of 68,511 relevant tweets were collected from January 1, 2020, through September 31, 2020. Sentiment and content analysis identified five themes which are revealed by 16 topics associated with clothing consumption over the phases of pre-lockdown, lockdown, and reopening. Pent-up demand for clothing products and changed clothing consumption habits were identified. Our findings provide evidence that consumption change is the fundamental mechanism of stress coping.

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Section: Methodsmentioning

confidence: 99%

Clothing Consumption During the COVID-19 Pandemic: Evidence From Mining Tweets

Liu

Xia

Lang

2021

Clothing and Textiles Research Journal

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“…We also found a qualitative study that analyzed tweets related to leading brands of wearables in the US to explore perceptions and reactions toward wearable devices that improve health and well-being (El-Gayar et al, 2019). More specifically, the authors used supervised learning, sentiment analysis, and automated content analysis, which demonstrated "the relevance of persuasive design features such as dialogue, credibility, and social support, through to various degrees, " among other uses (El-Gayar et al, 2019, p. 3858).…”

Section: Well-being Research Through the Collection Of Social Media Datamentioning

confidence: 99%

Social Impact of Psychological Research on Well-Being Shared in Social Media

et al. 2020

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The purpose of this article is to demonstrate how the Social Impact in Social Media (SISM, hereinafter) methodology applied in psychological research provides evidence for the visibility of the social impact of the research. This article helps researchers become aware of whether and how their improvements are capturing the interest of citizens and how citizens are applying such evidence and obtaining better outcomes, in this case, in relation to well-being. In addition, citizens can access the latest evidence on social media and act as channels of communication between science and social or personal networks and, in doing so, they can improve the living conditions of others. This methodology is also useful for agencies that support researchers in psychology with financial assistance, which can use it to evaluate the social impact of the funds that they invest in research. In this article, the 10 studies on well-being were selected for analysis using the following criteria: their research results led to demonstrable improvement in well-being, and these improvements are presented on social media. We applied the social impact coverage ratio to identify the percentage of the social impact shared in social media in relation to the total amount of social media data collected. Finally, examples of quantitative and qualitative evidence of the social impact of the research on well-being are presented.

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“…Popular social media platforms, including Twitter, have enabled new channels for users to share information and their experiences [12]. These platforms provide efficient methods of information access for health surveillance and social intelligence [13][14][15], and they have a growing popularity for sharing and debating scientific information [16][17][18]. Several studies have used Twitter as a data source to demonstrate the potential to identify the public's reactions to a variety of public health concerns, including the opioid crisis [19], marijuana [20][21][22], and vaping [23].…”

Section: Introductionmentioning

confidence: 99%

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data

Al-Ramahi¹,

Elnoshokaty²,

El‐Gayar³

et al. 2021

JMIR Public Health Surveill

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Background Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. Objective This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. Methods We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. Results The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. Conclusions These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics.

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Wearable Devices for Health and Wellbeing: Design Insights from Twitter

Cited by 13 publications

References 18 publications

Clothing Consumption During the COVID-19 Pandemic: Evidence From Mining Tweets

Clothing Consumption During the COVID-19 Pandemic: Evidence From Mining Tweets

Social Impact of Psychological Research on Well-Being Shared in Social Media

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data

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