Navigating technical, legal, and ethical hurdles to scraping LinkedIn data for academic research

In an era where professional career data is critical for analyzing occupational trends and organizational dynamics, LinkedIn data offers a rich corpus for academic research due to its expansive user base and frequent updates. This paper examines technical, legal, and ethical challenges associated with scraping LinkedIn profiles for research, arguing that scraping is the most effective method for acquiring comprehensive LinkedIn data compared to direct cooperation, purchasing data, or APIs. Despite prohibitive measures and potential legal issues outlined by LinkedIn, recent court decisions provide favorable precedents for the lawful scraping of public profiles. The paper also compiles prior research studies that leveraged LinkedIn data, highlighting various acquisition methods and their applicability to academic research. It explores strategies to ethically and legally navigate scraping, providing recommendations on how researchers can responsibly collect LinkedIn data, ensuring compliance with evolving privacy laws and ethical standards. Finally, technical considerations are discussed, emphasizing the use of tools like Selenium to overcome LinkedIn's sophisticated anti-scraping measures.