KFU. Card publication. Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods

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ANALYSIS OF INTERPERSONAL RELATIONSHIPS OF SOCIAL NETWORK USERS USING EXPLAINABLE ARTIFICIAL INTELLIGENCE METHODS

Form of presentation

Articles in international journals and collections

Year of publication

2023

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Authors, employees of KFU

Berdnikov Andrey Anatolevich, author

Gafarov Fail Mubarakovich, author

Ustin Pavel Nikolaevich, author

Bibliographic description in the original language

Ustin P, Gafarov F, Berdnikov A. Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods. OBM Neurobiology 2023; 7(3): 180

Annotation

The emergence of the social networking phenomenon and the sudden spread of the coronavirus pandemic (COVID-19) around the world have significantly affected the transformation of the system of interpersonal relations, partly shifting them towards virtual reality. Online social networks have greatly expanded the boundaries of human interpersonal interaction and initiated processes of integration of different cultures. As a result, research into the possibilities of predicting human behavior through the characteristics of virtual communication in social networks has become more relevant. The aim of the study is: to explore the possibilities of machine learning model interpretability methods for interpreting the success of social network users based on their profile data. This paper uses a specific method of explainable artificial intelligence, SHAP (SHapley Additive exPlanations), to analyze and interpret trained machine learning models. The research is based on Social Network Analysis (SNA), a modern line of research conducted to understand different aspects of the social network as a whole as well as its individual nodes (users). User accounts on social networks provide detailed information that characterizes a user's personality, interests, and hobbies and reflects their current status. Characteristics of a personal profile also make it possible to identify social graphs - mathematical models reflecting the characteristics of interpersonal relationships of social network users. An important tool for social network analysis is various machine learning algorithms that make different predictions based on sets of characteristics (social network data). However, most of today's powerful machine learning methods are «black boxes,« and therefore the challenge of interpreting and explaining their results arises. The study trained RandomForestClassifier and XGBClassifier models and showed the nature and degree of influence of the personal profile metrics of VKontakte social network users and indicators of their interpersonal relationship characteristics (graph metrics).

Keywords

Interpersonal relations; social networks; success; predictors; explainable artificial intelligence; machine learning; Shapley values

The name of the journal

OBM Neurobiology

On-line resource for training course

http://dspace.kpfu.ru/xmlui/bitstream/handle/net/177566/obm.neurobiol.2303180.pdf?sequence=1&isAllowed=y

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=292451&p_lang=2

Resource files

File name	Size (MB)	Format
obm.neurobiol.2303180.pdf	0,52	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Berdnikov Andrey Anatolevich	ru_RU
dc.contributor.author	Gafarov Fail Mubarakovich	ru_RU
dc.contributor.author	Ustin Pavel Nikolaevich	ru_RU
dc.date.accessioned	2023-01-01T00:00:00Z	ru_RU
dc.date.available	2023-01-01T00:00:00Z	ru_RU
dc.date.issued	2023	ru_RU
dc.identifier.citation	Ustin P, Gafarov F, Berdnikov A. Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods. OBM Neurobiology 2023; 7(3): 180	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=292451&p_lang=2	ru_RU
dc.description.abstract	OBM Neurobiology	ru_RU
dc.description.abstract	The emergence of the social networking phenomenon and the sudden spread of the coronavirus pandemic (COVID-19) around the world have significantly affected the transformation of the system of interpersonal relations, partly shifting them towards virtual reality. Online social networks have greatly expanded the boundaries of human interpersonal interaction and initiated processes of integration of different cultures. As a result, research into the possibilities of predicting human behavior through the characteristics of virtual communication in social networks has become more relevant. The aim of the study is: to explore the possibilities of machine learning model interpretability methods for interpreting the success of social network users based on their profile data. This paper uses a specific method of explainable artificial intelligence, SHAP (SHapley Additive exPlanations), to analyze and interpret trained machine learning models. The research is based on Social Network Analysis (SNA), a modern line of research conducted to understand different aspects of the social network as a whole as well as its individual nodes (users). User accounts on social networks provide detailed information that characterizes a user's personality, interests, and hobbies and reflects their current status. Characteristics of a personal profile also make it possible to identify social graphs - mathematical models reflecting the characteristics of interpersonal relationships of social network users. An important tool for social network analysis is various machine learning algorithms that make different predictions based on sets of characteristics (social network data). However, most of today's powerful machine learning methods are «black boxes,« and therefore the challenge of interpreting and explaining their results arises. The study trained RandomForestClassifier and XGBClassifier models and showed the nature and degree of influence of the personal profile metrics of VKontakte social network users and indicators of their interpersonal relationship characteristics (graph metrics).	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods	ru_RU
dc.type	Articles in international journals and collections	ru_RU