Knowing what you get when seeking semantic similarity: exploring classic NLP method biases
Responding to the increasingly blurred boundaries between humans and technology, this innovative Handbook reveals the intricate patterns of interaction between individuals, machines, organizations and beyond, even including AI-extended human interaction with animals and plants. Using cutting-edge data and analysis, expert contributors provide new insight into the rapidly growing digitalization of society.
Chapters span disciplinary boundaries, covering both computer science and AI, as well as sociology and psychology, to encompass all aspects of social computing as an emerging field. They also examine the complexity of social networks and algorithmic decision-making whilst drawing on case studies from diverse industries and exploring important issues, such as the ethical implications of AI, data privacy regulation, and safe data sharing. Ultimately, this Handbook illustrates the diverse ways in which digital technologies can be used to analyze social behavior, recognise individual and group interaction patterns, and improve daily life.
Providing a comprehensive overview of the latest developments in social computing, this Handbook will be an essential read for students and scholars of human dynamics, social network analysis, and sociology. It will also be an invaluable guide for professionals seeking a deeper understanding of how technology can be used to analyze social dynamics.
Charles, J. S., Mongeau, P. et Renaud-Desjardins, L. (2024). Knowing what you get when seeking semantic similarity: Exploring classic nlp method biases. Dans Peter A. Gloor, Francesca Grippa et A. P. Andrea Fronzetti Colladon (dir.), Handbook of social computing. Cheltenham : Edward Elgar Publishing