Containment of misinformation spread in online social networks

Nguyen, Nam; Yan, Guanhua; Thai, My T.; Eidenbenz, Stephan

doi:10.1145/2380718.2380746

Cited by 189 publications

(85 citation statements)

References 27 publications

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“…To see that (15) and (16) do not dominate one another, we show that the RHS for one constraint need not always be larger than the RHS for the other constraint. For x = (0, 0, 0, 0, 0, 1, 0), note that the RHS of inequality (15) is 5, while the RHS for (16) is 6.…”

Section: Theorem 4 Consider a Spread Network Gx(vx Ax) For Which Thementioning

confidence: 89%

“…The authors assume that if good and bad information simultaneously arrive at a node, the good information will be adopted, and that the set of nodes spreading misinformation is known a priori. Nguyen et al [15] study the problem of finding a smallest set of nodes from which good influence serves to contain the spread of misinformation. They investigate both the case in which the originating nodes that spread misinformation are known, and the case in which they are unknown.…”

Section: Literature Reviewmentioning

confidence: 99%

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A cutting-plane algorithm for solving a weighted influence interdiction problem

2013

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We consider a two-stage defender-attacker game that takes place on a network, in which the attacker seeks to take control over (or "influence") as many nodes as possible. The defender acts first in this game by protecting a subset of nodes that cannot be influenced by the attacker. With full knowledge of the defender's action, the attacker can then influence an initial subset of unprotected nodes. The influence then spreads over a finite number of time stages, where an uninfluenced node becomes influenced at time t if a threshold number of its neighbors are influenced at time t − 1. The attacker's objective is to maximize the weighted number of nodes that are influenced over the time horizon, where the weights depend both on the node and on the time at which that is influenced. This defender-attacker game is especially difficult to optimize, because the attacker's problem itself is NP-hard, which precludes a standard inner-dualization approach that is common in many interdiction studies. We provide three models for solving the attacker's problem, and develop a tailored cutting-plane algorithm for solving the defender's problem. We then demonstrate the computational efficacy of our proposed algorithms on a set of randomly generated instances.

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Section: Theorem 4 Consider a Spread Network Gx(vx Ax) For Which Thementioning

confidence: 89%

Section: Literature Reviewmentioning

confidence: 99%

A cutting-plane algorithm for solving a weighted influence interdiction problem

2013

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“…An analysis of the counter measures proposed and modeled in the literature against the spread of misinformation in OSNs are at times not in consonance with the effectiveness of the measures as suggested in studies of cognitive psychology. Theoretical framework for limiting the viral propagation of misinformation has been proposed in [12,13]. The authors have proposed a model for identifying the most influential nodes whose decontamination with good information would prevent the spread of misinformation.…”

Section: Countering the Spread Of Misinformationmentioning

confidence: 99%

Detecting misinformation in online social networks using cognitive psychology

Kumar

Geethakumari

2014

Hum. Cent. Comput. Inf. Sci.

190

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The paper explores the use of concepts in cognitive psychology to evaluate the spread of misinformation, disinformation and propaganda in online social networks. Analysing online social networks to identify metrics to infer cues of deception will enable us to measure diffusion of misinformation. The cognitive process involved in the decision to spread information involves answering four main questions viz consistency of message, coherency of message, credibility of source and general acceptability of message. We have used the cues of deception to analyse these questions to obtain solutions for preventing the spread of misinformation. We have proposed an algorithm to effectively detect deliberate spread of false information which would enable users to make informed decisions while spreading information in social networks. The computationally efficient algorithm uses the collaborative filtering property of social networks to measure the credibility of sources of information as well as quality of news items. The validation of the proposed methodology has been done on the online social network 'Twitter'.

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“…They assume that each node's influence propagation is limited to the community it resides and thus they evaluate the influence propagation within each community to improve the computational efficiency. There are also many works for influence propagation or other social network applications taking the advantage of community structures (please see e.g., [42][43][44][45] for recent works).…”

Section: Related Workmentioning

confidence: 99%

Influence-based community partition for social networks

Zhu

et al. 2014

Compu Social Networls

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Background/Purpose: Community partition is of great importance in sociology, biology and computer science. Due to the exponentially increasing amount of social network applications, a fast and accurate method is necessary for community partition in social networks. In view of this, we investigate the social community partition problem from the perspective of influence propagation, which is one of the most important features of social communication. Methods:We formulate social community partition as a combinatorial optimization problem that aims at partitioning a social network into K disjoint communities such that the sum of influence propagation within each community is maximized. When K = 2 we develop an optimal algorithm that has a provable performance guarantee for a class of influence propagation models. For general K, we prove that it is N P-hard to find a maximum partition for social networks in the well-known linear threshold and independent cascade models. To get near-optimal solutions, we develop a greedy algorithm based on the optimal algorithm. We also develop a heuristic algorithm with a low computational complexity for large social networks. Results: To evaluate the practical efficiency of our algorithms, we do a simulation study based on real world scenarios. The experiments are conducted on three real-world social networks, and the experimental results show that more accurate partitions according to influence propagation can be obtained using our algorithms rather than using some classic community partition algorithms. Conclusions: In this study, we investigate the community partition problem in social networks. It is formulated as an optimization problem and investigated both theoretically and practically. The results can be applied to find communities in social networks and are also useful for the influence propagation problem in social networks.

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Containment of misinformation spread in online social networks

Cited by 189 publications

References 27 publications

A cutting-plane algorithm for solving a weighted influence interdiction problem

A cutting-plane algorithm for solving a weighted influence interdiction problem

Detecting misinformation in online social networks using cognitive psychology

Influence-based community partition for social networks

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