Mining Facebook data for Quality of Life assessment

Dados Bibliográficos

ID21452995
AutoresDavide Marengo (0000-0002-7107-0810, University of Turin), Danny Azucar (0000-0003-2513-3283, University of Turin, autor correspondente), Claudio Longobardi (0000-0002-8457-6554, University of Turin), Michele Settanni (0000-0001-9115-4555, University of Turin)
Ano2021
Volume40
Fascículo6
Páginas597-607
Data de publicação2021-04-26
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoBehaviour and Information Technology (JOURNAL)
Identificadores do periódicoISSN: 0144-929X • E-ISSN: 1362-3001
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/0144929x.2019.1711454
OpenAlexW3001994893
IdiomaEN
Citações recebidas2
Referências citadas56

Research indicates that how individuals utilise language to express themselves reflects individual-level differences regarding psychosocial characteristics, including perceived Quality of Life (QoL). In this study, we apply a language modelling technique to the natural user-generated language from Facebook to examine associations between language expressed on Facebook and self-reported QoL. Specifically, we collected the user-generated language from a sample of 603 Facebook users (76.3% females), mined emerging text corpora using the LIWC closed-vocabulary approach, and examined associations between LIWC features and self-reported domain-specific QoL (Physical, Psychological, Social), and General QoL. In line with previous research, we found use of pronouns, negative emotions, death and sleep words, and use of profanity to be significantly associated with QoL. Next, we used the Random Forest algorithm to test the predictability of QoL dimensions based on LIWC features and posting activity statistics. The models achieved moderate predictive power (r ranging from .22 to .33), the Psychological and General QoL dimensions showing the highest accuracy. An alternative approach combining LIWC features, posting activity, and predicted scores for domain-specific QoL components showed increased accuracy when predicting General QoL (r = .43). Findings are discussed in light of previous literature. Suggestions for improving models in future studies are provided

Developmental psychology · Linguistics · Predictability · Predictive power · Psychosocial · Psychotherapist · Quality of life (healthcare) · Random forest · Statistics · Vocabulary · Applied Psychology · Artificial Intelligence · Clinical Psychology · Computer Science · Digital Communication and Language · Mental Health via Writing · Psychology · Sentiment Analysis and Opinion Mining