IPMC2022 PSYCH 2022 : Call-for-Papers Machine learning in psychology & social science (VSI: IPMC2022 PSYCH) A Special Issue for Information Processing & Management (IP&M), Elsevier
Call For Papers
Machine learning in psychology & social science (VSI: IPMC2022 PSYCH)
A Special Issue for Information Processing & Management (IP&M), Elsevier
Deadline: June 15, 2022
** Apologies for cross-posting **
Information Processing and Management (IF = 6.2) is announcing a special issue on machine learning in psychology and social sciences. This special issue is also a Thematic Track at the Information Processing & Management Conference 2022 (IP&MC2022), meaning that at least one author of the accepted manuscript will need to attend the IP&MC2022 conference.
June 15, 2022: Thematic track manuscript submission due date; authors are welcome to submit early as reviews will be rolling
July 31, 2022: Author notification
October 20-23, 2022: IP&MC conference presentation and feedback
January 1, 2023: Post-conference revision due date, but authors welcome to submit earlier
Call for contributions
While machine learning is finding its way in predicting observable human behavior (such as election outcomes) across social sciences, psychology is only discovering ML methods. One of the reasons for this is that major psychological categories are latent and hard to measure reliably, which is only partly true for social sciences. At the same time, the reproducibility crisis in psychology coupled with the proliferation of digital data and large-scale online experimenting are likely to boost the application of machine learning approaches in psychology and deal with latent constructs used in other sciences.
This conference track and the respective special issue welcome original empirical research applying the newest supervised and unsupervised machine learning methods for various tasks in psychology and social sciences, especially related to latent variables, including their prediction and usage as features. We appreciate works that critically analyze the problems of quality and interpretability of prediction results, their internal and external validity, reliability, and the utility of unsupervised methods. Systematic reviews and meta-analytical work may also be considered. Examples of relevant psychological categories include personality traits or psychological conditions and cognitive states, while relevant social categories may be exemplified by latent cultural predispositions or latent social prejudices. Examples of relevant source data used for predictions include digital footprints from social media, web and mobile applications (e.g., chatbots), data from smartphones, wearable or voice assistant devices.
Topics of interest include, but are not limited to:
New ML-based approaches to predicting latent psychological traits, conditions and latent social categories;
Benchmark datasets creation and data collection for ML in psychology and social sciences;
Practical deployment of ML-based applications in the sphere of psychology and social science;
Early alert and prevention systems based on ML in psychology and social sciences;
Interpretability vs prediction quality of ML for psychological assessment and social category modeling;
Issues of reliability and validity in ML approaches to psychological assessment;
Cultural and demographic biases in ML based approaches to psychological assessment and social category prediction;
Prediction of well-being, mood disorders and emotional states;
Assessment of cognitive functioning Detection of social prejudices, biases and latent cultural norms;
Behavior prediction with joint data from digital traces and latent categories.
Please find the extended call for papers here.
If you have any further questions, please, do not hesitate to contact us via email@example.com
IPM special issue guest editors