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Testing Behavior Rate Models on data from Vk.com Social Network. / Toropova, Aleksandra; Tulupyeva, Tatiana.

в: CEUR Workshop Proceedings, Том 2782, 2020, стр. 258-263.

Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование

Harvard

Toropova, A & Tulupyeva, T 2020, 'Testing Behavior Rate Models on data from Vk.com Social Network', CEUR Workshop Proceedings, Том. 2782, стр. 258-263.

APA

Toropova, A., & Tulupyeva, T. (2020). Testing Behavior Rate Models on data from Vk.com Social Network. CEUR Workshop Proceedings, 2782, 258-263.

Vancouver

Toropova A, Tulupyeva T. Testing Behavior Rate Models on data from Vk.com Social Network. CEUR Workshop Proceedings. 2020;2782:258-263.

Author

Toropova, Aleksandra ; Tulupyeva, Tatiana. / Testing Behavior Rate Models on data from Vk.com Social Network. в: CEUR Workshop Proceedings. 2020 ; Том 2782. стр. 258-263.

BibTeX

@article{dc46834b782a4e2f866056be9ff1c1c5,
title = "Testing Behavior Rate Models on data from Vk.com Social Network",
abstract = "In human science, we often face problem of estimating human behavior parameters. Behavior frequency is one of the most used behavior indicators. Knowing the behavior frequency, we can draw conclusions about significant aspects of behavior both in the present and in the future. It is often impossible to obtain data about behavior frequency directly. Therefore, there is a problem to estimate behavior frequency on limited data such as data about some last episodes of behavior. We propose two models based on Bayesian belief networks to estimate behavior frequency. We use last three behavior episodes, maximum and minimum intervals between the episodes as initial data and test the models on the data set collected from the social network Vk.com.",
keywords = "Bayesian belief network, Behavior episodes, Behavior frequency, Behavior rate, Hidden variables, Posting behavior",
author = "Aleksandra Toropova and Tatiana Tulupyeva",
note = "Publisher Copyright: {\textcopyright} 2020 CEUR-WS. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; Russian Advances in Fuzzy Systems and Soft Computing: Selected Contributions to the 8th International Conference on {"}Fuzzy Systems, Soft Soft Computing and Intelligent Technologies{"},FSSCIT 2020 ; Conference date: 29-06-2020 Through 01-07-2020",
year = "2020",
language = "English",
volume = "2782",
pages = "258--263",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "RWTH Aahen University",

}

RIS

TY - JOUR

T1 - Testing Behavior Rate Models on data from Vk.com Social Network

AU - Toropova, Aleksandra

AU - Tulupyeva, Tatiana

N1 - Publisher Copyright: © 2020 CEUR-WS. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - In human science, we often face problem of estimating human behavior parameters. Behavior frequency is one of the most used behavior indicators. Knowing the behavior frequency, we can draw conclusions about significant aspects of behavior both in the present and in the future. It is often impossible to obtain data about behavior frequency directly. Therefore, there is a problem to estimate behavior frequency on limited data such as data about some last episodes of behavior. We propose two models based on Bayesian belief networks to estimate behavior frequency. We use last three behavior episodes, maximum and minimum intervals between the episodes as initial data and test the models on the data set collected from the social network Vk.com.

AB - In human science, we often face problem of estimating human behavior parameters. Behavior frequency is one of the most used behavior indicators. Knowing the behavior frequency, we can draw conclusions about significant aspects of behavior both in the present and in the future. It is often impossible to obtain data about behavior frequency directly. Therefore, there is a problem to estimate behavior frequency on limited data such as data about some last episodes of behavior. We propose two models based on Bayesian belief networks to estimate behavior frequency. We use last three behavior episodes, maximum and minimum intervals between the episodes as initial data and test the models on the data set collected from the social network Vk.com.

KW - Bayesian belief network

KW - Behavior episodes

KW - Behavior frequency

KW - Behavior rate

KW - Hidden variables

KW - Posting behavior

UR - http://www.scopus.com/inward/record.url?scp=85099044999&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85099044999

VL - 2782

SP - 258

EP - 263

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - Russian Advances in Fuzzy Systems and Soft Computing: Selected Contributions to the 8th International Conference on "Fuzzy Systems, Soft Soft Computing and Intelligent Technologies",FSSCIT 2020

Y2 - 29 June 2020 through 1 July 2020

ER -

ID: 78033434