The Association of Internet Use Intensity and Lifestyle Behaviors During the COVID-19 Pandemic: A Cross-Sectional Study in Chinese Adults

Public Health Surveillance of Behavioral Cancer Risk Factors During the COVID-19 Pandemic: Sentiment and Emotion Analysis of Twitter Data

BACKGROUND

The COVID-19 pandemic and its associated public health mitigation strategies have dramatically changed patterns of daily life activities worldwide, resulting in unintentional consequences on behavioral risk factors, including smoking, alcohol consumption, poor nutrition, and physical inactivity. The infodemic of social media data may provide novel opportunities for evaluating changes related to behavioral risk factors during the pandemic.

OBJECTIVE

We explored the feasibility of conducting a sentiment and emotion analysis using Twitter data to evaluate behavioral cancer risk factors (physical inactivity, poor nutrition, alcohol consumption, and smoking) over time during the first year of the COVID-19 pandemic.

METHODS

Tweets during 2020 relating to the COVID-19 pandemic and the 4 cancer risk factors were extracted from the George Washington University Libraries Dataverse. Tweets were defined and filtered using keywords to create 4 data sets. We trained and tested a machine learning classifier using a prelabeled Twitter data set. This was applied to determine the sentiment (positive, negative, or neutral) of each tweet. A natural language processing package was used to identify the emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) based on the words contained in the tweets. Sentiments and emotions for each of the risk factors were evaluated over time and analyzed to identify keywords that emerged.

RESULTS

The sentiment analysis revealed that 56.69% (51,479/90,813) of the tweets about physical activity were positive, 16.4% (14,893/90,813) were negative, and 26.91% (24,441/90,813) were neutral. Similar patterns were observed for nutrition, where 55.44% (27,939/50,396), 15.78% (7950/50,396), and 28.79% (14,507/50,396) of the tweets were positive, negative, and neutral, respectively. For alcohol, the proportions of positive, negative, and neutral tweets were 46.85% (34,897/74,484), 22.9% (17,056/74,484), and 30.25% (22,531/74,484), respectively, and for smoking, they were 41.2% (11,628/28,220), 24.23% (6839/28,220), and 34.56% (9753/28,220), respectively. The sentiments were relatively stable over time. The emotion analysis suggests that the most common emotion expressed across physical activity and nutrition tweets was trust (69,495/320,741, 21.67% and 42,324/176,564, 23.97%, respectively); for alcohol, it was joy (49,147/273,128, 17.99%); and for smoking, it was fear (23,066/110,256, 20.92%). The emotions expressed remained relatively constant over the observed period. An analysis of the most frequent words tweeted revealed further insights into common themes expressed in relation to some of the risk factors and possible sources of bias.

CONCLUSIONS

This analysis provided insight into behavioral cancer risk factors as expressed on Twitter during the first year of the COVID-19 pandemic. It was feasible to extract tweets relating to all 4 risk factors, and most tweets had a positive sentiment with varied emotions across the different data sets. Although these results can play a role in promoting public health, a deeper dive via qualitative analysis can be conducted to provide a contextual examination of each tweet.

The impact of internet use on health status among older adults in China: The mediating role of social support

Background

With the popularization of the Internet, the use of the Internet is becoming more and more important in the daily life of older adults. However, previous research mainly focuses on Internet use and health in general, and the mechanism of this effect remains to be studied. To bridge this gap, this study aims to explore the mediational effects of social support between Internet use and health among older adults in China.

Methods

The data used in this article are from the 2021 Chinese General Social Survey (CGSS). Social support is divided into two aspects and four dimensions: informal social support (relatives support, friends support, neighbors support) and formal social support (social insurance). This article uses the nested multivariate OLS regression models to analyze the impact of Internet use on health. Furthermore, Finally, SPSS macro PROCESS is applied to test their mediation effects.

Results

Informal social support positively influenced the health status among older adults, while formal social support did not. Among the three types of informal social support, relatives support and friends support significantly affected health status among Chinese older adults. Regarding social support differences between urban and rural areas, it was found that relatives support is a positively significant factor for rural older adults, while friends support is significant for urban older adults.

Conclusions

Since Internet use has many ways of impacting health status, social support only plays a partial mediating role in this study. It recommends that the government should take compelling measures to encourage and promote the use of the Internet among older adults and obtain various social support to improve their health status.

Does the Internet Bring People Closer Together or Further Apart? The Impact of Internet Usage on Interpersonal Communications

The complementarity interference (CI) model suggests that the Internet may either inhibit or facilitate interpersonal communications. This paper empirically examines the impact of Internet usage on interpersonal interactions, using a micro dataset from China to answer whether the Internet brings people closer together or further apart. The empirical results demonstrate, first, that Internet usage significantly increases both the time and frequency of people’s communications with their family and friends, rather than causing them to feel more disconnected and isolated. Holding other factors constant, for each one-standard-deviation increase in Internet usage, weekly communications with family members increases by an average of 102.150 min, while there is an average increase of 54.838 min in interactions with friends. These findings as to its positive effects are robust when using other regression models and interpersonal contact measures, as well as the instrumental variable method. Second, Internet usage also contributes to decreased loneliness; it exerts this effect primarily by improving people’s interactions with their family members. However, communications with friends do not significantly mediate such impacts. Third, the positive role of Internet usage on communications is more prominent for people with more frequent online socialization and self-presentation, better online skills, younger age, higher educational level, and who are living in urban areas. In addition, the beneficial effects of Internet usage are larger for communications with family members in the case of migrants. Therefore, in the context of the rapid development of information technology, the network infrastructure should be improved to make better use of the Internet to facilitate interpersonal communications and promote people’s wellness.