Dynamic node immunization for restraint of harmful information diffusion in social networks

Yang, Dingda; Liao, Xiangwen; Shen, Huawei; Cheng, Xueqi; Chen, Guolong

doi:10.1016/j.physa.2018.02.128

Cited by 21 publications

(14 citation statements)

References 14 publications

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“…Hence, the main research direction around this topic is to develop a fast, effective algorithm for seeking a proper subset of influence people. See [16][17][18][19], for some typical literature.…”

Section: Truth-publishing Approachesmentioning

confidence: 99%

Containing Misinformation Spread: A Collaborative Resource Allocation Strategy for Knowledge Popularization and Expert Education

Lin-hong

Huang

Yang

2022

Security and Communication Networks

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With the prevalence of online social networks, the potential threat of misinformation has greatly enhanced. Therefore, it is significant to study how to effectively control the spread of misinformation. Publishing the truth to the public is the most effective approach to controlling the spread of misinformation. Knowledge popularization and expert education are two complementary ways to achieve that. It has been proven that if these two ways can be combined to speed up the release of the truth, the impact caused by the spread of misinformation will be dramatically reduced. However, how to reasonably allocate resources to these two ways so as to achieve a better result at a lower cost is still an open challenge. This paper provides a theoretical guidance for designing an effective collaborative resource allocation strategy. First, a novel individual-level misinformation spread model is proposed. It well characterizes the collaborative effect of the two truth-publishing ways on the containment of misinformation spread. On this basis, the expected cost of an arbitrary collaborative strategy is evaluated. Second, an optimal control problem is formulated to find effective strategies, with the expected cost as the performance index function and with the misinformation spread model as the constraint. Third, in order to solve the optimal control problem, an optimality system that specifies the necessary conditions of an optimal solution is derived. By solving the optimality system, a candidate optimal solution can be obtained. Finally, the effectiveness of the obtained candidate optimal solution is verified by a series of numerical experiments.

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Section: Truth-publishing Approachesmentioning

confidence: 99%

Containing Misinformation Spread: A Collaborative Resource Allocation Strategy for Knowledge Popularization and Expert Education

Lin-hong

Huang

Yang

2022

Security and Communication Networks

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“…Deep semantics Ma et al 2016Ma et al 2018Wu et al 2018Reis et al 2019Rashkin et al 2017Martin et al 2018Tseng et al 1999Karimi et al 2018Long et al 2017Shu et al 2019 Profiles Influence Interests Yang et al 2018Ghenai et al 2018 Dynamic networks Static networks Jin et al 2016Tacchini et al 2017Ma et al 2017Ruchansky et al 2017Wu et al 2018 Stance-based Ma et al 2018Lukasik et al 2019 Meta-data based Figure 2. The review of information credibility evaluation methods Wu et al, 2019a).…”

Section: Shallow Semanticsmentioning

confidence: 99%

“…To better evaluate information credibility, more and more credibility features are extracted around social media, especially user features. User-based methods capture disseminator's profile (Long et al, 2017;Shu et al, 2019b), influence (Yang et al, 2018), interests features (Ghenai & Mejova, 2018) and adopt various classification models to detect false information on social media, which demonstrate that these features as distinguishing credibility features are useful, and the concatenation of the user features and text features as the input of models achieves average performance improve-ments margin of about 5% than only text features as the input of models. Additionally, network-based methods involve static-based and dynamic-based.…”

Section: Information Credibility Evaluationmentioning

confidence: 99%

Discovering differential features: Adversarial learning for information credibility evaluation

Rao

Nazir

et al. 2020

Information Sciences

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A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet. However, these extracted features still suffer from many irrelevant and noisy features that restrict severely the performance of the approaches. In this paper, we propose a novel model based on Adversarial Networks and inspirited by the Shared-Private model (ANSP), which aims at reducing common, irrelevant features from the extracted features for information credibility evaluation. Specifically, ANSP involves two tasks: one is to prevent the binary classification of true and false information for capturing common features relying on adversarial networks guided by reinforcement learning. Another extracts credibility features (henceforth, private features) from multiple types of credibility information and compares with the common features through two strategies, i.e., orthogonality constraints and KL-divergence for making the private features more differential. Experiments first on two six-label LIAR and Weibo datasets demonstrate that ANSP achieves the state-of-theart performance, boosting the accuracy by 2.1%, 3.1%, respectively and then on four-label Twit-ter16 validate the robustness of the model with 1.8% performance improvements.

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“…However, social networks are also actively used by scammers. Fraudsters, as well as terrorists, use networks to spread false, harmful, or even life-threatening information [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Study of Strategies for Disseminating Information in Social Networks Using Simulation Tools

Usanin

Zimin

Zamyatina

2021

Lecture Notes in Computer Science

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The paper presents simulation tools for investigation not only the structural characteristics of social networks in order to study information dissemination strategies, but also the dynamic characteristics of this process. A feature of this software system is not only the ability to work with virtual social networks, but also with data from real networks. To collect data about a real social network, a special software agent has been developed. An ontological approach is used to store data about real network.

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Dynamic node immunization for restraint of harmful information diffusion in social networks

Cited by 21 publications

References 14 publications

Containing Misinformation Spread: A Collaborative Resource Allocation Strategy for Knowledge Popularization and Expert Education

Containing Misinformation Spread: A Collaborative Resource Allocation Strategy for Knowledge Popularization and Expert Education

Discovering differential features: Adversarial learning for information credibility evaluation

Study of Strategies for Disseminating Information in Social Networks Using Simulation Tools

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