About this Research Topic
The inaugural Research Topic for the Computational Social Psychology (CSP) section focuses on the intersection of social psychology and artificial intelligence (AI), highlighting the innovative use of computational methods to advance research, data analysis, and the understanding of computational models within the realm of social psychology. Through this inaugural issue we aim to: (a) showcase the fascinating work that is emerging at the nexus of social psychology, machine learning, personality science, artificial intelligence, and data science; and (b) foreshadow the enormous potential that this field of computational social psychology may have as a formalized discipline in psychology that is inspired in equal parts by the rich history of social psychology and the analytical power of modern computational approaches.
Frontiers in Social Psychology's inaugural Research Topic seeks to celebrate this budding area of research in all its complexity and diversity. As such we invite empirical, theoretical, and conceptual cutting-edge work stemming from social psychology, data science and anywhere in-between. We are equally interested in research that uses computational approaches (e.g., large-scale, complex data sets; formal modeling; network analysis) to reveal novel insights into social psychological phenomena, and in research that uses social psychological theory to understand computational models and AI systems, addressing the ethical, societal, and psychological implications of AI in social contexts.
We welcome both bold exploratory research investigating and describing entirely new phenomena, as well as predictive and confirmatory research providing new takes on old problems. As a journal dedicated to computational social psychology, we place a special emphasis on work featuring innovative computational methods, Big Data, simulations, and foundational models. We promote rigorous, open, and replicable science in everything we do and publish.
Frontiers in Social Psychology's inaugural Research Topic seeks to celebrate this budding area of research in all its complexity and diversity. As such we invite empirical, theoretical, and conceptual cutting-edge work stemming from social psychology, data science and anywhere in-between. We are equally interested in research that uses computational approaches (e.g., large-scale, complex data sets; formal modeling; network analysis) to reveal novel insights into social psychological phenomena, and in research that uses social psychological theory to understand computational models and AI systems, addressing the ethical, societal, and psychological implications of AI in social contexts.
We welcome both bold exploratory research investigating and describing entirely new phenomena, as well as predictive and confirmatory research providing new takes on old problems. As a journal dedicated to computational social psychology, we place a special emphasis on work featuring innovative computational methods, Big Data, simulations, and foundational models. We promote rigorous, open, and replicable science in everything we do and publish.
Keywords: Artificial intelligence, Data science, Social psychology, Social behavior, Machine learning, Digital traces, Digital footprints, Computational psychology, Computational Social Science
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
