Depression detection based on social networking sites using data mining

Pande, Sandeep Dwarkanath; Hasane Ahammad, S. K.; Gurav, Madhuri Navnath; Faragallah, Osama S.; Eid, Mahmoud M. A.; Rashed, Ahmed Nabih Zaki

doi:10.1007/s11042-023-16564-7

Cited by 4 publications

(1 citation statement)

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“…Of particular interest in the context of psychological studies of the person's online activity is the study by S. Pande and his colleagues [45], in which an attempt was made to develop a system for indicating the psycho-emotional state by markers of behavior in a social network. In particular, these authors point to sentiment analysis (SA), natural language processing (NLP), and user profiling as important indicators of depression risk.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Assessment of a Person's Social Success Through the Characteristics of Interpersonal Relationships in A Virtual Environment

Shevcov,

Ustin,

Popov

et al. 2023

OBM Neurobiol

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The sudden spread of the COVID-19 pandemic has convincingly proved the role of social networks in human life activity as an actor of interpersonal relations. The need for isolation and the limitation of face-to-face communication between people has significantly transformed the system of interpersonal connections through an extensive increase in social contacts in the virtual environment and the growing importance of online social services. In the study, we addressed the problem of diagnosing the indicator of social success of a personality, reflecting characteristics of its interpersonal relations in offline activity through the attributes of its virtual activity in social networks. The research was based on the methods of social network analysis and traditional ways of psychodiagnostics. Social networks were analyzed using social graphs - mathematical models that describe the characteristics of relationships between users in social networks through various metrics (friends, elements, edges, density, closeness centrality, degree centrality, clustering coefficient, etc.). The study aims to prove the predictive validity of social graph indicators as predictors of personality social success through correlations of graph characteristics reflecting the features of interpersonal relations of a social network user in a virtual environment with the socio-psychological hands of traditional psychodiagnostic tools. The study included 601 subjects. Data was collected using psychological questionnaires and personal profiles from the social network VKontakte. The results of the study showed correlations between several characteristics of social graphs (density, clustering coefficient, closeness centrality, etc.) and several indicators of psychodiagnostic techniques (Dominance index in relationships with others, Organizational abilities, Desire for people, Narcissism, etc.). The significant contribution of the research is to expand the methodological apparatus of psychological science and to open new methods of predicting interpersonal relations of personality through its activity in a virtual environment.

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Section: Theoretical Frameworkmentioning

confidence: 99%