The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
조회수
101
로 알려진 현상 사회적 양극화하나의 사회 집단이 둘 이상의 집단으로 분열되는 현상은 의견의 과격화와 잘못된 정보의 확산을 초래하여 사회의 분열을 초래할 수 있으며, 이는 온라인 커뮤니티에서 특히 중요합니다. 온라인 소셜 네트워크에서 양극화의 영향을 완화하기 위한 기술을 개발하려면 양극화 발생을 유발하는 메커니즘을 이해하는 것이 필요합니다. 온라인 소셜 네트워크 사용자의 정량화된 의견을 기반으로 네트워크 구조와 사용자 의견이 바뀌는 사회적 양극화 모델이 있습니다. 그러나 이는 온라인 소셜 네트워크를 통해 연결된 사용자 간의 상호 작용을 기반으로 합니다. 현재 추천 시스템은 비슷한 관심사를 갖고 있다고 간주되는 알려지지 않은 사용자의 정보를 제공합니다. 우리는 이 상황을 사용자 간의 지역적 상호 작용에 기반한 것이 아니라 네트워크 시스템에 의해 발생하는 비지역적 효과로 해석할 수 있습니다. 본 논문에서는 온라인 소셜 네트워크의 비국소적 효과를 수학적으로 설명할 수 있는 분광 그래프 이론을 바탕으로 사용자 행동과 네트워크 구조가 비국소적 효과를 포함하여 서로 영향을 미치며 변화하는 양극화 모델을 제안한다. 제안된 모델의 특성을 조사한다. 동시에 본 연구에 필요한 네트워크 양극화 정도를 정량적으로 평가할 수 있는 지표를 제안한다.
Tomoya KINOSHITA
Tokyo Metropolitan University
Masaki AIDA
Tokyo Metropolitan University
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부
Tomoya KINOSHITA, Masaki AIDA, "A Spectral-Based Model for Describing Social Polarization in Online Communities" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 10, pp. 1181-1191, October 2022, doi: 10.1587/transcom.2021MEP0001.
Abstract: The phenomenon known as social polarization, in which a social group splits into two or more groups, can cause division of the society by causing the radicalization of opinions and the spread of misinformation, is particularly significant in online communities. To develop technologies to mitigate the effects of polarization in online social networks, it is necessary to understand the mechanism driving its occurrence. There are some models of social polarization in which network structure and users' opinions change, based on the quantified opinions held by the users of online social networks. However, they are based on the interaction between users connected by online social networks. Current recommendation systems offer information from unknown users who are deemed to have similar interests. We can interpret this situation as being yielded non-local effects brought on by the network system, it is not based on local interactions between users. In this paper, based on the spectral graph theory, which can describe non-local effects in online social networks mathematically, we propose a model of polarization that user behavior and network structure change while influencing each other including non-local effects. We investigate the characteristics of the proposed model. Simultaneously, we propose an index to evaluate the degree of network polarization quantitatively, which is needed for our investigations.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021MEP0001/_p
부
@ARTICLE{e105-b_10_1181,
author={Tomoya KINOSHITA, Masaki AIDA, },
journal={IEICE TRANSACTIONS on Communications},
title={A Spectral-Based Model for Describing Social Polarization in Online Communities},
year={2022},
volume={E105-B},
number={10},
pages={1181-1191},
abstract={The phenomenon known as social polarization, in which a social group splits into two or more groups, can cause division of the society by causing the radicalization of opinions and the spread of misinformation, is particularly significant in online communities. To develop technologies to mitigate the effects of polarization in online social networks, it is necessary to understand the mechanism driving its occurrence. There are some models of social polarization in which network structure and users' opinions change, based on the quantified opinions held by the users of online social networks. However, they are based on the interaction between users connected by online social networks. Current recommendation systems offer information from unknown users who are deemed to have similar interests. We can interpret this situation as being yielded non-local effects brought on by the network system, it is not based on local interactions between users. In this paper, based on the spectral graph theory, which can describe non-local effects in online social networks mathematically, we propose a model of polarization that user behavior and network structure change while influencing each other including non-local effects. We investigate the characteristics of the proposed model. Simultaneously, we propose an index to evaluate the degree of network polarization quantitatively, which is needed for our investigations.},
keywords={},
doi={10.1587/transcom.2021MEP0001},
ISSN={1745-1345},
month={October},}
부
TY - JOUR
TI - A Spectral-Based Model for Describing Social Polarization in Online Communities
T2 - IEICE TRANSACTIONS on Communications
SP - 1181
EP - 1191
AU - Tomoya KINOSHITA
AU - Masaki AIDA
PY - 2022
DO - 10.1587/transcom.2021MEP0001
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2022
AB - The phenomenon known as social polarization, in which a social group splits into two or more groups, can cause division of the society by causing the radicalization of opinions and the spread of misinformation, is particularly significant in online communities. To develop technologies to mitigate the effects of polarization in online social networks, it is necessary to understand the mechanism driving its occurrence. There are some models of social polarization in which network structure and users' opinions change, based on the quantified opinions held by the users of online social networks. However, they are based on the interaction between users connected by online social networks. Current recommendation systems offer information from unknown users who are deemed to have similar interests. We can interpret this situation as being yielded non-local effects brought on by the network system, it is not based on local interactions between users. In this paper, based on the spectral graph theory, which can describe non-local effects in online social networks mathematically, we propose a model of polarization that user behavior and network structure change while influencing each other including non-local effects. We investigate the characteristics of the proposed model. Simultaneously, we propose an index to evaluate the degree of network polarization quantitatively, which is needed for our investigations.
ER -