Emotional Labor in Everyday Resilience: Class-Based Experiences of Navigating Unemployment Amid the COVID-19 Pandemic in the U.S

During the COVID-19 global health crisis, institutions, policymakers, and academics alike have called for practicing resilience to overcome its ongoing disruptions. This paper contributes a comparative study of the job search experiences of working-class and upper-middle-class job seekers, particularly in relation to their resilience practices during the pandemic. Drawing from in-depth interviews with 12 working-class and 11 upper-middle-class job seekers in the U.S., we unpack challenges resulting from both the pandemic and unemployment and job seekers' novel practices of navigating these challenges in their everyday disrupted life. Job seekers' ongoing negotiation with their resources, situations, and surroundings gives practical meanings to building everyday resilience, which we theorize as an ongoing process of becoming resilient. While job seekers across classes experienced similar challenges, working-class job seekers took on additional emotional labor in their everyday resilience due to their limited experience in the digital job search space, competition with higher-degree holding job seekers applying for the same jobs, limited social support networks, and at times, isolation. By foregrounding the uneven distribution of emotional labor in realizing the promise of resilience along class lines, this work cautions against the romanticization of resilience and calls for a more critical and nuanced understanding of resilience in CSCW. © 2022 Owner/Author.

Research on Data Analysis and Visualization of Recruitment Positions Based on Text Mining

With the rapid development of the Internet and the impact of COVID-19, online recruitment has gradually become the mainstream form of recruitment. However, existing online recruitment platforms fail to fully combine the job seekers' demands for salary, region, benefits, and other aspects, which cloud not display the information related to recruitment positions in a multidimensional way. To solve this problem, this paper firstly uses a web crawler to collect job information from recruitment websites based on keywords retrieved by users, then extracts job information using regular expressions, and cleans and processes the extracted job information using third-party libraries such as Pandas and NumPy. Finally, through the probabilistic theme model of text mining, the topic model of job description content in the recruitment information is modeled. Combining with the django development framework and related visualization technology, the relationship among education requirement, experience requirement, job location, salary, and other aspects in the recruitment information is visually displayed in a multidimensional way. At the same time, the GM model is used to realize the gray prediction of the number of employment personnel in related industries, which provides employment reference for the majority of job seekers and enterprises. © 2022 Yuanyuan Chen and Ruijie Pan.

Mining the Impacts of COVID-19 Pandemic on the Labour Market

Under the influence of the pandemic environment, many people may have lost their jobs or on the verge of being laid off, while there are many new job seekers. Hence, the status of new jobs under the pandemic and how various industries are affected by the pandemic-including predicting future work trends-have become the focus of attention. In this paper, we present a social informatics solution to mine the impacts of COVID-19 pandemic on the labour market. We make good use of data mining (especially, frequent pattern mining), statistical analysis, and prediction. Evaluation of real-life Canadian labour market data demonstrates the practicality of our tool. Although we illustrate our ideas with the Canadian labour market, our solution can be adaptable to mine labour markets in other geographical locations. © 2022 IEEE.

A Digital Bridge: Increasing Digital Access to Low-Income Job Seekers and the Role of Community Organizations

With the onset of the COVID-19 pandemic in the Spring of 2020, much of work and professional life shifted to virtual environments. For many with existing and reliable access to digital devices, the internet, and digital literacy skills, this sudden shift was a minor adjustment. However, for many others the shift to online work life highlighted the disparities in access to reliable technology and exacerbated the existing digital divide among vulnerable populations. In response to this change, the City of Seattle, and the Seattle Jobs Initiative (SJI) launched the Digital Bridge (DB) program, a pilot program aimed at providing reliable internet access and digital devices to current job seekers working with SJI. Through the DB program, we see needs beyond initial access to technology and the internet, and the role that community organizations play in providing assistance. This paper aims to explore two key components: 1) lessons learned from the Digital Bridge project, and 2) organizational relationships and the impacts on program implementation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.