Using geolocated social media for ecological momentary assessments of emotion: Innovative opportunities in psychology science and practice.

Abstract

Social media applications have become popular methods of online communication, interaction, and social networking. Many people use social media websites and mobile applications, such as Twitter, to create and post personal expressions in public online forums. This online content presents opportunities for using social media as a data source with the potential to improve evaluation of theoretical models of emotional and stressful experiences across various topics and subfields of psychology science and practice. In this article, we discuss emerging information retrieval and analytic methods using social media for ecological momentary assessments of emotion. We describe 2 specific methods we have developed in the context of Twitter and their use in a broader study investigating relationships among people's emotional experiences, their expressions of experiences in social media, their daily geospatial movements and locations, and their stress experiences. We conclude with a discussion of potential applications and ethical considerations for these methods in professional psychology practice and science.

Spatio-Temporal Distribution of Negative Emotions in New York City After a Natural Disaster as Seen in Social Media

“…Social media data, such as from Twitter, may help to fill these gaps, as they are largely and publicly available, have been successfully applied to disaster research, and provide pre-, peri-, and post-disaster information [ 23 , 24 , 25 ]. For example, specific emotions, such as fear or sadness, can be detected from Twitter streams through advanced sentiment analysis [ 26 , 27 ] and can be related to periods before, during, and after disasters. Furthermore, such data provide the opportunity for the geographic assessment of single posts.…”

“…Furthermore, such data provide the opportunity for the geographic assessment of single posts. Hence, ecological momentary assessment of emotional reactions—that is, ongoing evaluation of in-the-moment experiences—becomes possible [ 26 ]. For example, in a study on emotional responses in the greater New York City area during Superstorm Sandy, Gruebner et al [ 21 ] used Twitter data and found specific negative emotions clustered over space and time.…”

Trace of pace, place, and space in personal relationships: The chronogeometrics of studying relationships at scale

“…The challenges faced by personal relationship researchers pursuing big data approaches is not only theoretical but clearly methodological as well (e.g., Ahmad et al, 2019;Boyd & Pennebaker, 2017;Luhmann, 2017;Mahmoodi, Leckelt, van Zalk, Geukes, & Back, 2017;Shaughnessy et al, 2018). For example, one among many challenges of coping with this deluge of information is identifying the most appropriate unit of analysis.…”

Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts

“…These datasets are highly disaggregated across space and time, and offer new, highly granular windows into the routine dynamics of human and natural processes (Batty et al, ; Fritz, Schuurman, Robertson, & Lear, ). Sensors, for example, can track when and where people board transit, point of sale records detail the times and locations of electronic purchases, and cellular phone call and social media metadata permit exploration of social connectivity, movements, and momentary expressions of perceptions and emotions across space (Ahas et al, ; Calabrese, Diao, Di Lorenzo, Ferreira, & Ratti, ; Shaughnessy et al, ; Shen & Cheng, ).…”

“…There are many advantages to individual‐level spatial data, such as greater spatial and temporal granularity and greater precision in estimates of variables of interest (health status, stress level, activity space, perceptions of neighborhood safety, etc.). Also, recent research has demonstrated the need for mobile methods that capture geographic context dynamically over various spatial and temporal scales to truly understand environment–individual relationships (Ahas et al, ; Shaughnessy et al, ; Sheller & Urry, ).…”