[1]JING Junchang,ZHANG Zhiyong,SONG Bin,et al.Disinformation diffusion control method integrating user propagation risk and node influence analysis[J].CAAI Transactions on Intelligent Systems,2024,19(2):360-369.[doi:10.11992/tis.202210009]
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Disinformation diffusion control method integrating user propagation risk and node influence analysis

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 2 Page number: 360-369 Column: 学术论文—智能系统 Public date: 2024-03-05
Title:
Disinformation diffusion control method integrating user propagation risk and node influence analysis
Author(s):
JING Junchang1; 2;  ZHANG Zhiyong1; 2;  SONG Bin1; 2;  BAN Aiying1; 2;  GAO Dongjun1; 2
1. Information Engineering College, He’nan University of Science and Technology, Luoyang 471023, China;
2. He’nan International Joint Laboratory of Cyberspace Security Applications, He’nan University of Science and Technology, Luoyang 471023, China
Keywords:
online social networks; disinformation; propagation risk; embedded representation; node influence; adaptive weighting; discrete particle swarm; diffusion control
CLC:
TP309
DOI:
10.11992/tis.202210009
Abstract:
The spread of disinformation on online social networks (OSNs) has become a critical challenge for cyberspace security governance. This paper presents DDC-UPRNI, a disinformation diffusion control method, by integrating user propagation risk with node influence analysis. First, comprehensively considering the diversity and complexity of the characteristic space of disinformation propagation, an embedded representation of the behavior, time and content dimensions of user propagation of disinformation is realized through the self-attention mechanism, and the automatic classification of different user propagation risk levels is achieved using the improved unsupervised clustering K-means++ algorithm. Second, an adaptive weighting strategy is designed to improve the discrete particle swarm optimization algorithm, and a method for selecting key nodes of disinformation propagation is proposed based on the discrete particle swarm optimization. This method determines several influential control driving nodes from the user node set with a specific propagation risk level to achieve accurate and highly efficient disinformation propagation control. Finally, experiments are performed on a real OSN platform, and the results demonstrate that the proposed DDC-UPRNI method has obvious advantages over other existing algorithms in some important indicators, including control effect and time complexity. This method provides a significant reference value for the current governance of disinformation in social cyberspace.
References:
[1] CHOI D, OH H, CHUN Selin, et al. Preventing rumor spread with deep learning[J]. Expert systems with applications, 2022, 197: 116688.
[2] ZHAO Zilong, ZHAO Jichang, SANO Y, et al. Fake news propagates differently from real news even at early stages of spreading[J]. EPJ data science, 2020, 9(1): 7.
[3] VOSOUGHI S, ROY D, ARAL S. The spread of true and false news online[J]. Science, 2018, 359(6380): 1146–1151.
[4] CHEN Weineng, TAN Dazhao, YANG Qiang, et al. Ant colony optimization for the control of pollutant spreading on social networks[J]. IEEE transactions on cybernetics, 2020, 50(9): 4053–4065.
[5] 黄宏程, 赖礼城, 胡敏, 等. 基于严格可控理论的社交网络信息传播控制方法[J]. 电子与信息学报, 2018, 40(7): 1707–1714
HUANG Hongcheng, LAI Licheng, HU Min, et al. Information propagation control method in social networks based on exact controllability theory[J]. Journal of electronics & information technology, 2018, 40(7): 1707–1714
[6] JIA Jianfeng, LIU Xuewei, ZHANG Yixin, et al. Rumor propagation controlling based on finding important nodes in complex network[J]. Journal of industrial & management optimization, 2020, 16(5): 2521–2529.
[7] JIANG Qingye, SONG Guojie, CONG Gao, et al. Simulated annealing based influence maximization in social networks[C]//Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. San Francisco: ACM, 2011: 127–132.
[8] LESKOVEC J, KRAUSE A, GUESTRIN C, et al. Cost-effective outbreak detection in networks[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose: ACM, 2007: 420–429.
[9] GOYAL A, LU Wei, LAKSHMANAN L V S. CELF++: optimizing the greedy algorithm for influence maximization in social networks[C]//Proceedings of the 20th International Conference Companion on World Wide Web. Hyderabad: ACM, 2011: 47–48.
[10] LIU Huanli, MA Chuang, XIANG Bingbing, et al. Identifying multiple influential spreaders based on generalized closeness centrality[J]. Physica A:statistical mechanics and its applications, 2018, 492: 2237–2248.
[11] LEV T, BEN-GAL I, SHMUELI E. Influence maximization through scheduled seeding in a real-world setting[J]. IEEE transactions on computational social systems, 2022, 9(2): 494–507.
[12] SRINIVASAN S, DHINESH BABU L D. A bio-inspired defensive rumor confinement strategy in online social networks[J]. Journal of organizational and end user computing, 2021, 33(1): 47–70.
[13] CHANG C K. Situation analytics—at the dawn of a new software engineering paradigm[J]. Science China information sciences, 2018, 61(5): 050101.
[14] ZHANG Zhiyong, SUN Ranran, WANG Xiaoxue, et al. A situational analytic method for user behavior pattern in multimedia social networks[J]. IEEE transactions on big data, 2019, 5(4): 520–528.
[15] 张志勇, 荆军昌, 李斐, 等. 人工智能视角下的在线社交网络虚假信息检测、传播与控制研究综述[J]. 计算机学报, 2021, 44(11): 2261–2282
ZHANG Zhiyong, JING Junchang, LI Fei, et al. Survey on fake information detection, propagation and control in online social networks from the perspective of artificial intelligence[J]. Chinese journal of computers, 2021, 44(11): 2261–2282
[16] LIU Weibo, WANG Zidong, YUAN Yuan, et al. A novel sigmoid-function-based adaptive weighted particle swarm optimizer[J]. IEEE transactions on cybernetics, 2021, 51(2): 1085–1093.
[17] GONG Maoguo, YAN Jianan, SHEN Bo, et al. Influence maximization in social networks based on discrete particle swarm optimization[J]. Information sciences, 2016, 367/368: 600–614.
[18] TANG Jianxin, ZHANG Ruisheng, YAO Yabing, et al. Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization[J]. Physica A:statistical mechanics and its applications, 2019, 513: 477–496.
[19] LIU Zhaoli, QIN Tao, SUN Qindong, et al. SIRQU: dynamic quarantine defense model for online rumor propagation control[J]. IEEE transactions on computational social systems, 2022, 9(6): 1703–1714.
[20] ZHANG N, HUANG H, DUARTE M, et al. Risk analysis for rumor propagation in metropolises based on improved 8-state ICSAR model and dynamic personal activity trajectories[J]. Physica A:statistical mechanics and its applications, 2016, 451: 403–419.
[21] 洪巍, 王晨雪, 吴林海, 等. 基于保护动机理论的食品安全网络谣言关注度影响因素研究[J]. 系统工程理论与实践, 2022, 42(11): 3121–3138
HONG Wei, WANG Chenxue, WU Linhai, et al. Research on influencing factors of food safety Internet rumor attention based on protection motivation theory[J]. Systems engineering-theory & practice, 2022, 42(11): 3121–3138
[22] 杨洋洋, 谢雪梅. 网络谣言风险测度与治理路径研究[J]. 情报科学, 2021, 39(9): 170–177
YANG Yangyang, XIE Xuemei. Research on risk measurement and governance path of Internet rumors[J]. Information science, 2021, 39(9): 170–177
[23] KEMPE D, KLEINBERG J, TARDOS ?. Influential nodes in a diffusion model for social networks[M]//CAIRES L, ITALIANO G F, MONTEIRO L, et al. Automata, Languages and Programming. Berlin: Springer Berlin Heidelberg, 2005: 1127-1138.
[24] TAHERINIA M, ESMAEILI M, MINAEI-BIDGOLI B. Optimizing CELF algorithm for influence maximization problem in social networks[J]. Journal of artificial intelligence and data mining, 2022, 10(1): 25–41.
[25] XIAO Yunpeng, LI Jinghua, ZHU Yangfu, et al. User behavior prediction of social hotspots based on multimessage interaction and neural network[J]. IEEE transactions on computational social systems, 2020, 7(2): 536–545.
[26] GHOSH R, SURACHAWALA T, LERMAN K. Entropy-based classification of ‘retweeting’ activity on twitter[EB/OL]. [2011-06-02](2020-01-01). http://arxiv.org/abs/1106.0346.pdf.
[27] FU Boyang, SUI Jie. Multi-modal affine fusion network for social media rumor detection[J]. PeerJ computer science, 2022, 8: e928.
[28] XIAO Yunpeng, HUANG Zhen, LI Qian, et al. Diffusion pixelation: a game diffusion model of rumor & anti-rumor inspired by image restoration[J]. IEEE transactions on knowledge and data engineering, 2023, 35(5): 4682–4694.
[29] KIM D, SEO D, CHO S, et al. Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec[J]. Information sciences, 2019, 477: 15–29.
[30] LIN Zhouhan, FENG Minwei, SANTOS C, et al. A structured self-attentive sentence Embedding[C]//ICLR’2017: Proceedings of the 5th International Conference on Learning Representations. Toulon: OpenReview, 2017.
[31] LI Zhe, HUANG Xinyu. Identifying influential spreaders in complex networks by an improved gravity model[J]. Scientific reports, 2021, 11(1): 22194.
[32] ZHANG Zhiyong, SUN Ranran, ZHAO Changwei, et al. CyVOD: a novel trinity multimedia social network scheme[J]. Multimedia tools and applications, 2017, 76(18): 18513–18529.
[33] JING Junchang, ZHANG Zhiyong, CHOO K K R, et al. Inference of user desires to spread disinformation based on social situation analytics and group effect[J]. IEEE transactions on dependable and secure computing, 2023, 20(3): 1833–1848.
[34] JING Junchang, LI Fei, SONG Bin, et al. Disinformation propagation trend analysis and identification based on social situation analytics and multilevel attention network[J]. IEEE transactions on computational social systems, 2023, 10(2): 507–522.
[35] 卫新乐, 张志勇, 宋斌, 等. 基于纵向联邦学习的社交网络跨平台恶意用户检测方法[J]. 小型微型计算机系统, 2022, 43(7): 1541–1546
WEI Xinle, ZHANG Zhiyong, SONG Bin, et al. Social networks cross-platform malicious user detection method based on vertical federated learning[J]. Journal of Chinese computer systems, 2022, 43(7): 1541–1546
[36] BRANDES U. On variants of shortest-path betweenness centrality and their generic computation[J]. Social networks, 2008, 30(2): 136–145.
[37] FREEMAN L C. Centrality in social networks conceptual clarification[J]. Social networks, 1978, 1(3): 215–239.
[38] BOLDI P, SANTINI M, VIGNA S. PageRank: functional dependencies[J]. ACM transactions on information systems, 2009, 27(4): 19.
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