A human-machine collaborative approach measures economic development using satellite imagery.

Machine learning approaches using satellite imagery are providing accessible ways to infer socioeconomic measures without visiting a region. However, many algorithms require integration of ground-truth data, while regional data are scarce or even absent in many countries. Here we present our human-machine collaborative model which predicts grid-level economic development using publicly available satellite imagery and lightweight subjective ranking annotation without any ground data.

The Potential of Chatbots for Emotional Support and Promoting Mental Well-Being in Different Cultures: Mixed Methods Study.

Background: Artificial intelligence chatbot research has focused on technical advances in natural language processing and validating the effectiveness of human-machine conversations in specific settings. However, real-world chat data remain proprietary and unexplored despite their growing popularity, and new analyses of chatbot uses and their effects on mitigating negative moods are urgently needed.

Objective: In this study, we investigated whether and how artificial intelligence chatbots facilitate the expression of user emotions, specifically sadness and depression.

Social dimensions impact individual sleep quantity and quality.

While sleep positively impacts well-being, health, and productivity, the effects of societal factors on sleep remain underexplored. Here we analyze the sleep of 30,082 individuals across 11 countries using 52 million activity records from wearable devices. Our data are consistent with past studies of gender and age-associated sleep characteristics.

User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis.

Background: Chatbots have become a promising tool to support public health initiatives. Despite their potential, little research has examined how individuals interacted with chatbots during the COVID-19 pandemic. Understanding user-chatbot interactions is crucial for developing services that can respond to people's needs during a global health emergency.

QAnon shifts into the mainstream, remains a far-right ally.

The rise of domestic fringe groups within the United States has been well documented, threatening political and social stability. The QAnon conspiracy theory has developed as one such destructive group, though it remains a largely misunderstood movement. Through a mixed-methods analysis of over 3.

Misinformation, believability, and vaccine acceptance over 40 countries: Takeaways from the initial phase of the COVID-19 infodemic.

The COVID-19 pandemic has been damaging to the lives of people all around the world. Accompanied by the pandemic is an infodemic, an abundant and uncontrolled spread of potentially harmful misinformation. The infodemic may severely change the pandemic's course by interfering with public health interventions such as wearing masks, social distancing, and vaccination.

The Conflict Between People's Urge to Punish AI and Legal Systems.

Regulating artificial intelligence (AI) has become necessary in light of its deployment in high-risk scenarios. This paper explores the proposal to extend legal personhood to AI and robots, which had not yet been examined through the lens of the general public. We present two studies ( = 3,559) to obtain people's views of electronic legal personhood vis-à-vis existing liability models.

On the Social-Relational Moral Standing of AI: An Empirical Study Using AI-Generated Art.

The moral standing of robots and artificial intelligence (AI) systems has become a widely debated topic by normative research. This discussion, however, has primarily focused on those systems developed for social functions, e.g.

Urban green space and happiness in developed countries.

Unlabelled: Urban green space is thought to contribute to citizen happiness by promoting physical and mental health. Nevertheless, how urban green space and happiness are related across many countries with different socioeconomic conditions has not been explored. By measuring the urban green space score (UGS) from high-resolution satellite imagery of 90 global cities covering 179,168 km and 230 million people in 60 developed countries, we find that the amount of urban green space and GDP are correlated with a nation's happiness level.

Correction: COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication.

[This corrects the article DOI: 10.2196/23272.].

COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication.

Background: COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic.

Prevalence of Misinformation and Factchecks on the COVID-19 Pandemic in 35 Countries: Observational Infodemiology Study.

Background: The COVID-19 pandemic has been accompanied by an infodemic, in which a plethora of false information has been rapidly disseminated online, leading to serious harm worldwide.

Objective: This study aims to analyze the prevalence of common misinformation related to the COVID-19 pandemic.

Methods: We conducted an online survey via social media platforms and a survey company to determine whether respondents have been exposed to a broad set of false claims and fact-checked information on the disease.

Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study.

Background: As societies become more complex, larger populations suffer from insomnia. In 2014, the US Centers for Disease Control and Prevention declared that sleep disorders should be dealt with as a public health epidemic. However, it is hard to provide adequate treatment for each insomnia sufferer, since various behavioral characteristics influence symptoms of insomnia collectively.

Rumor Detection over Varying Time Windows.

This study determines the major difference between rumors and non-rumors and explores rumor classification performance levels over varying time windows-from the first three days to nearly two months. A comprehensive set of user, structural, linguistic, and temporal features was examined and their relative strength was compared from near-complete date of Twitter. Our contribution is at providing deep insight into the cumulative spreading patterns of rumors over time as well as at tracking the precise changes in predictive powers across rumor features.

The Effect of Bad News and CEO Apology of Corporate on User Responses in Social Media.

While social media has become an important platform for social reputation, the emotional responses of users toward bad news have not been investigated thoroughly. We analyzed a total of 20,773 Twitter messages by 15,513 users to assess the influence of bad news and public apology in social media. Based on both computerized, quantitative sentiment analysis and in-depth qualitative analysis, we found that rapid public apology effectively and immediately reduced the level of negative sentiment, where the degree of change in sentiments differed by the type of interactions users engaged in.

Activities on Facebook reveal the depressive state of users.

Background: As online social media have become prominent, much effort has been spent on identifying users with depressive symptoms in order to aim at early diagnosis, treatment, and even prevention by using various online social media. In this paper, we focused on Facebook to discern any correlations between the platform's features and users' depressive symptoms. This work may be helpful in trying to reach and detect large numbers of depressed individuals more easily.

Modeling the adoption of innovations in the presence of geographic and media influences.

While there is a large body of work examining the effects of social network structure on innovation adoption, models to date have lacked considerations of real geography or mass media. In this article, we show these features are crucial to making more accurate predictions of a social contagion and technology adoption at a city-to-city scale. Using data from the adoption of the popular micro-blogging platform, Twitter, we present a model of adoption on a network that places friendships in real geographic space and exposes individuals to mass media influence.