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Register a new userEmotions in Online Content Diffusion
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Social media-transmitted online information, particularly content that is emotionally charged, shapes our thoughts and actions. In this study, we incorporate social network theories and analyses to investigate how emotions shape online content diffusion, using a computational approach. We rigorously quantify and characterize the structural properties of diffusion cascades, in which more than six million unique individuals transmitted 387,486 articles in a massive-scale online social network, WeChat. We detected the degree of eight discrete emotions (i.e., surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust) embedded in these articles, using a newly generated domain-specific and up-to-date emotion lexicon. We found that articles with a higher degree of anxiety and love reached a larger number of individuals and diffused more deeply, broadly, and virally, whereas sadness had the opposite effect. Age and network degree of the individuals who transmitted an article and, in particular, the social ties between senders and receivers, significantly mediated how emotions affect article diffusion. These findings offer valuable insight into how emotions facilitate or hinder information spread through social networks and how people receive and transmit online content that induces various emotions.
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The explosive nature of Covid-19 transmission drastically altered the rhythm of daily life by forcing billions of people to stay at their homes. A critical challenge facing transportation planners is to identify the type and the extent of changes in peoples activity-travel behavior in the post-pandemic world. In this study, we investigated the travel behavior evolution by analyzing a longitudinal two-wave panel survey data conducted in the United States from April 2020 to October 2020 (wave 1) and from November 2020 to May 2021(wave 2). Encompassing nearly 3,000 respondents across different states, we explored pandemic-induced changes and underlying reasons in four major categories of telecommute/telemedicine, commute mode choice, online shopping, and air travel. Upon concrete evidence, our findings substantiate significantly observed and expected changes in habits and preferences. According to results, nearly half of employees anticipate having the alternative to telecommute and among which 71% expect to work from home at least twice a week after the pandemic. In the post-pandemic period, auto and transit commuters are expected to be 9% and 31% less than pre-pandemic, respectively. A considerable rise in hybrid work and grocery/non-grocery online shopping is expected. Moreover, 41% of pre-covid business travelers expect to have fewer flights (after the pandemic) while only 8% anticipate more, compared to the pre-pandemic. Upon our analyses, we discuss a spectrum of policy implications in all mentioned areas.
How are economies in a modern age impacted by epidemics? In what ways is economic life disrupted? How can pandemics be modeled? What can be done to mitigate and manage the danger? Does the threat of pandemics increase or decrease in the modern world? The Covid-19 pandemic has demonstrated the importance of these questions and the potential of complex systems science to provide answers. This article offers a broad overview of the history of pandemics, of established facts, and of models of infection diffusion, mitigation strategies, and economic impact. The example of the Covid-19 pandemic is used to illustrate the theoretical aspects, but the article also includes considerations concerning other historic epidemics and the danger of more infectious and less controllable outbreaks in the future.
Rapid rise in income inequality in India is a serious concern. While the emphasis is on inclusive growth, it seems difficult to tackle the problem without looking at the intricacies of the problem. Social mobility is one such important tool which helps in reaching the cause of the problem and focuses on bringing long term equality in the country. The purpose of this study is to examine the role of social background and education attainment in generating occupation mobility in the country. By applying an extended version of the RC association model to 68th round (2011-12) of the Employment and Unemployment Survey by the National Sample Survey Office of India, we found that the role of education is not important in generating occupation mobility in India, while social background plays a critical role in determining ones occupation. This study successfully highlights the strong intergenerational occupation immobility in the country and also the need to focus on education. In this regard, further studies are needed to uncover other crucial factors limiting the growth of individuals in the country.
Due to the unavailability of nationally representative data on time use, a systematic analysis of the gender gap in unpaid household and care work has not been undertaken in the context of India. The present paper, using the recent Time Use Survey (2019) data, examines the socioeconomic and demographic factors associated with variation in time spent on unpaid household and care work among men and women. It analyses how much of the gender gap in the time allocated to unpaid work can be explained by differences in these factors. The findings show that women spend much higher time compared to men in unpaid household and care work. The decomposition results reveal that differences in socioeconomic and demographic factors between men and women do not explain most of the gender gap in unpaid household work. Our results indicate that unobserved gender norms and practices most crucially govern the allocation of unpaid work within Indian households.
The article presents the results of multivariate classification of Russian regions by the indicators characterizing the population income and their concentration. The clusterization was performed upon an author approach to selecting the characteristics which determines the academic novelty in the evaluation of regional differentiation by population income and the interconnected characteristics. The performed analysis was aimed at the evaluation of the real scale of disproportions in spatial development of the country territories by the considered characteristics. The clusterization results allowed to formulate the condition of a relatively strong position of a group of high-income regions (the changes in the array of regions constituting it is highly unlikely in the foreseeable future). Additionally there has been revealed a group of Russian regions that the population is struggling to live on quite low income. These so-called poor regions, within the crisis conditions caused by Covid-19 are in need of additional public support, without which their population will impoverish.
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