Data-Driven Deep Journalism to Discover Age Dynamics in Multi-Generational Labour Markets from LinkedIn Media

We live in the information age and, ironically, meeting core function of journalism—i.e., to provide people with access unbiased information—has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach study how LinkedIn media could be useful for journalism. Specifically, we apply automatically extract and analyse big data public about labour markets; people’s skills education; businesses industries from multi-generational perspectives. The Great Resignation Quiet Quitting phenomena coupled rapidly changing generational attitudes are bringing unprecedented uncertain changes markets economies societies, hence need journalistic investigations into these topics is highly significant. combine machine learning create a whole pipeline software tool that allows discovering parameters dynamics using data. collect total 57,000 posts use it discover 15 by Latent Dirichlet Allocation algorithm (LDA) group them 5 macro-parameters, namely Generations-Specific Issues, Skills Qualifications, Employment Sectors, Consumer Industries, Issues. used this can make objective, cross-sectional, multi-perspective available all. It bring rigour making easy generate learning, tools so anyone uncover matters importance. work novel since no earlier has reported such an leveraged multigenerational perspectives (parameters) markets. extended additional AI other media.