Dataset Information

Differential ability of network and natural language information on social media to predict interpersonal and mental health traits.

ABSTRACT:

Objective

Previous studies have shown that digital footprints (mainly Social Networking Services, or SNS) can predict personality traits centered on the Big Five. The present study investigates to what extent different types of SNS information predicts wider traits and attributes.

Method

We collected an intensive set of 24 (52 subscales) personality traits and attributes (N = 239) and examined whether machine learning models trained on four different types of SNS (i.e., Twitter) information (network, time, word statistics, and bag of words) predict the traits and attributes.

Results

We found that four types of SNS information can predict 23 subscales collectively. Furthermore, we validated our hypothesis that the network and word statistics information, respectively, exhibit unique strengths for the prediction of inter-personal traits such as autism and mental health traits such as schizophrenia and anxiety. We also found that intelligence is predicted by all four types of SNS information.

Conclusions

These results reveal that the different types of SNS information can collectivity predict wider human traits and attributes than previously recognized, and also that each information type has unique predictive strengths for specific traits and attributes, suggesting that personality prediction from SNS is a powerful tool for both personality psychology and information technology.

SUBMITTER: Mori K

PROVIDER: S-EPMC8160829 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMachine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.ObjectiveThe primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice.MethodsThis systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.ResultsA total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.ConclusionsMachine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.