 Yet, as the Internet has evolved into a more social and communicative tool and venue, the ethical issues have shifted from purely data-driven to more human-centered. “On-ground” or face-to-face analogies, however, may not be applicable to online research. For example, the concept of the public park has been used as a site where researchers might observe others with little ethical controversy, but online, the concepts of public versus private are much more complex (SACHRP 2013). Thus, some scholars suggest that the specificity of Internet research ethics calls for new regulatory and/or professional and disciplinary guidance. For these reasons, the concept of human subjects research policies and regulation, informs this entry, which will continue discussions around ethical and methodological complexity, including personal identifiability, reputational risk and harm, notions of public space and public text, ownership, and longevity of data as they relate to Internet research. Specifically, the emergence of the social web raises issues around subject or participant recruitment practices, tiered informed consent models, and protection of various expectations and forms of privacy in an ever-increasing world of diffused and ubiquitous technologies. Additional ethical concerns center on issues of anonymity and confidentiality of data in spaces where researchers and their subjects may not fully understand the terms and conditions of those venues or tools, challenges to data integrity as research projects can be outsourced or crowdsourced to online labor marketplaces, and jurisdictional issues as more research is processed, stored, and disseminated via cloud computing or in remote server locales, presenting myriad legal complexities given jurisdictional differences in data laws. Further, the dominance of big data research has continued across research spaces, with the notions of “real-world data” and pervasive computing readily accepted and used in all disciplines. The ease of access and availability to use big data sets in myriad ways has enabled AI (artificial intelligence) and ML (machine learning) to grow as standard tools for researchers.