Mining Online Social Networks for Skills Evolution and Well-Being Challenges

A growing body of research leverages online social networks to study users’ interests and behaviors for reasons such as personalization and recommendation. However, the utility of online social platforms in understanding and predicting the workers and labor market has not received the attention it deserves in the literature. Therefore, this Ph.D. research explores the temporal and causal relationships between online social topics and the changes in the job market from several complementary aspects. More specifically, this research will be focused on addressing three main problems: (1) identifying information workers’ challenges and their impact on workers’ emotional well-being; (2) investigating whether social content is an effective indicator of future job requirements; and (3) examining which types of social content are more indicative of future in-demand skills and would be most appropriate when analyzing skill trends. The findings of this doctoral dissertation will shed light on how online social network data can serve as a reliable lens allowing us to better understand workers’ challenges and well-being in the knowledge economy and how online social platforms can be vehicles for providing effective peer support to improve workers’ well-being. This work also assists learners and job seekers to gain insight into important job-related skill trends, which can help them in their career-long learning process to stay in demand and remain employable. Also, the outcomes of this thesis can help governments and policymakers understand workforce challenges and design programs and solutions that can support workers in the knowledge economy and enhance their well-being.