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Application of Random Forest in Choosing a Method of Recovering the Age of Social Network Users. / Корепанова, Анастасия Андреевна; Абрамов, Максим Викторович.

In: Scientific and Technical Information Processing, Vol. 49, No. 5, 01.12.2022, p. 317–324.

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@article{027c5d14e2e2412f8a2a199ad3bfa6b9,
title = "Application of Random Forest in Choosing a Method of Recovering the Age of Social Network Users",
abstract = "Abstract: This article is devoted to the problem of recovering the ages of social network users by using machine learning to combine the methods suggested in this article. Methods based on analyzing the user profile information about education, subscription, and information about the education of the user{\textquoteright}s friends are considered. All of these methods can be used individually for samples of users with certain characteristics. To increase the proportion of users whose age can be recovered, a classification model was built for choosing the best age inference method for each. Two other age inference algorithms were tested as well, namely, ranking and score averaging. As a result, the first approach produced the best results on the test sample. The theoretical significance of this work consists in proposing a method of combining age inference algorithms, which increases the applicability and accuracy of individual algorithms. The study results can be applied in many spheres of analyzing user profiles in social network studies.",
keywords = "attribute inference, machine learning, social engineering attacks, social media analysis, sociocomputing",
author = "Корепанова, {Анастасия Андреевна} and Абрамов, {Максим Викторович}",
year = "2022",
month = dec,
day = "1",
doi = "10.3103/s0147688222050057",
language = "English",
volume = "49",
pages = "317–324",
journal = "Scientific and Technical Information Processing",
issn = "0147-6882",
publisher = "Allerton Press, Inc.",
number = "5",

}

RIS

TY - JOUR

T1 - Application of Random Forest in Choosing a Method of Recovering the Age of Social Network Users

AU - Корепанова, Анастасия Андреевна

AU - Абрамов, Максим Викторович

PY - 2022/12/1

Y1 - 2022/12/1

N2 - Abstract: This article is devoted to the problem of recovering the ages of social network users by using machine learning to combine the methods suggested in this article. Methods based on analyzing the user profile information about education, subscription, and information about the education of the user’s friends are considered. All of these methods can be used individually for samples of users with certain characteristics. To increase the proportion of users whose age can be recovered, a classification model was built for choosing the best age inference method for each. Two other age inference algorithms were tested as well, namely, ranking and score averaging. As a result, the first approach produced the best results on the test sample. The theoretical significance of this work consists in proposing a method of combining age inference algorithms, which increases the applicability and accuracy of individual algorithms. The study results can be applied in many spheres of analyzing user profiles in social network studies.

AB - Abstract: This article is devoted to the problem of recovering the ages of social network users by using machine learning to combine the methods suggested in this article. Methods based on analyzing the user profile information about education, subscription, and information about the education of the user’s friends are considered. All of these methods can be used individually for samples of users with certain characteristics. To increase the proportion of users whose age can be recovered, a classification model was built for choosing the best age inference method for each. Two other age inference algorithms were tested as well, namely, ranking and score averaging. As a result, the first approach produced the best results on the test sample. The theoretical significance of this work consists in proposing a method of combining age inference algorithms, which increases the applicability and accuracy of individual algorithms. The study results can be applied in many spheres of analyzing user profiles in social network studies.

KW - attribute inference

KW - machine learning

KW - social engineering attacks

KW - social media analysis

KW - sociocomputing

UR - https://www.mendeley.com/catalogue/84a03436-ebb3-3e99-9dc3-61442a8f422c/

U2 - 10.3103/s0147688222050057

DO - 10.3103/s0147688222050057

M3 - Article

VL - 49

SP - 317

EP - 324

JO - Scientific and Technical Information Processing

JF - Scientific and Technical Information Processing

SN - 0147-6882

IS - 5

ER -

ID: 113494877