Learning behavior rate models on social network data

Результат исследований: Научные публикации в периодических изданияхстатья в журнале по материалам конференции


Intensity is one of the main characteristics of human behavior, using data about behavior intensity we can make high enough quality predictions about future human behavior. But it is often impossible to get a direct behavior rate, because of high cost, time consumption or restrictions for monitoring private lives, so we need tools to estimate it indirectly. We offer two models for behavior rate evaluation with expert-defined and learned structures. These models are Bayesian belief networks. They include information about the intervals in days between the last three behavior episodes of the study period, the minimum and maximum intervals between episodes, and the interval between the last episode of the study period and the next episode, respectively, after the end of the study period. As we need for the models approbation an example of behavior allowing us to get direct behavior rate, we take users' posting behavior in social network. For learning parameters and structure one of the models, testing models, data from the social network Vkontakte for December 2019 was collected. This data includes an information about posting on own users' "walls" for this month, i.e. posts quantity, time of last three posts, maximum and minimum time interval between posts for December 2019, and time of the first post starting from January 2020.

Язык оригиналаанглийский
Страницы (с-по)200-209
Число страниц10
ЖурналCEUR Workshop Proceedings
СостояниеОпубликовано - 2020
Событие2020 "Russian Advances in Artificial Intelligence", RAAI 2020 - Moscow, Российская Федерация
Продолжительность: 10 окт 202016 окт 2020

Предметные области Scopus

  • Компьютерные науки (все)

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