Analyzing Language for Insights into Cognitive and Social Processes

Social media users are increasingly likely to use social media platforms to express themselves, curate their online profiles, and communicate with others. The digital traces of online behavior provide valuable signals for the psychometric measurements of users to characterize their self- and social identity and appraisal and estimate their emotional and mental health. This article first examines computational approaches for mining psychological signals from social media data. Secondly, it engages with the theoretical frameworks and the key research findings. Thirdly, it discusses the methodological issues that researchers face. Finally, it identifies directions for future research. This is an extended version of a book chapter by the two authors, titled "Analyzing language for insights into cognitive and social processes'' and forthcoming in The Routledge Handbook of Communication and Social Cognition, edited by Torsten Reimer, Lyn van Swol, and Arnd Florack. It includes an expanded section on Theoretical frameworks and key findings, which discusses primary research on using language, primarily social media, to understand affect, well-being, personality and psychological traits, and mental health.