Fast rumor source identification via random walks

Jain, Alankar; Borkar, Vivek S.; Garg, Dinesh

doi:10.1007/s13278-016-0373-6

Cited by 24 publications

(18 citation statements)

References 12 publications

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“…The idea of finding the source node on the network spreading the rumor was addressed by Jain et al (2016). They proposed heuristic algorithms based on hitting time statistics of the surrogate random walking method to estimate the maximum likelihood of the source of the rumor.…”

Section: Literature Reviewmentioning

confidence: 99%

Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets

et al. 2020

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Twitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM model is compared with several conventional machine and deep learning models. The proposed Attention-based LSTM model achieved an F 1-score of 0.88 in classifying rumor and non-rumor tweets, which is better than the state-of-the-art results. The proposed system can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms.

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Section: Literature Reviewmentioning

confidence: 99%

Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets

et al. 2020

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“…Under the SEIR rumor propagation model, a node in the network can be in any one of the four possible states: susceptible state (S), exposed state (E), infected state (I), recovered state (R). Under the discrete time slot propagation model, we assume that all nodes may change their own states at each discrete time slot based on their own states at the previous time slot [40]. At time slot t = 0, we suppose all nodes are in S-state but only one node s * is in I-state, which we call this node as the rumor source.…”

Section: Problem Formulation a Rumor Propagation Modelmentioning

confidence: 99%

“…where d(x, y) is the shortest distance between node x and node y. Thus, the computational complexity for the closeness centrality is O(|I |•|E|) [40]. (2) The Betweenness centrality quantifies the number of times a node x acts as a bridge along the shortest path between two other nodes i, j.…”

Section: Computational Complexity Analysismentioning

confidence: 99%

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Rumor Source Detection in Networks Based on the SEIR Model

Zhou

Zhu

et al. 2019

IEEE Access

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Online social networks have become extremely important in daily life and can be used to influence lives in dramatic ways. Two issues are the veracity and provenance of posted information, including rumors. There is a need for methods for tracing rumors (or any piece of information) to their most likely source in such networks. We consider the detection problem of single rumor source based on observed snapshots based on the susceptible-exposed-infected-recovered (SEIR) model. According to the SEIR model, all nodes in the network are formulated into four possible states: susceptible (S), exposed (E), infected (I), and recovered (R). Given an observed snapshot in the network, from which we can know the relevant graph topology and all infected nodes, but where nodes in susceptible, exposed, or recovered status cannot be distinguished, the purpose of our research is to identify the rumor source based on the observed snapshot and graph topology. We propose the concept of the optimal infection process and derive an estimator for the rumor source based on this optimal infection process. Subsequently, we prove that this estimator matches the rumor source with a high probability. The effectiveness of the proposed scheme is validated using experiments based on regular tree networks with different degrees. We further evaluate the performance of our scheme on two well-known synthetic complex networks and four real-world networks; the results suggest that our proposed scheme outperforms the traditional rumor centrality heuristics. The performance analysis on computational complexity demonstrates that our scheme has advantages in efficiency compared with other rumor centrality heuristics used in rumor detection methods. INDEX TERMS Rumor source detection, optimal infection process, susceptible-exposed-infected-recovered (SEIR) model, information security.

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“…Mathematical modeling is one of the most important applications of mathematics that contribute to the representation and simulation of social, economic, biological, and ecological phenomena and convert them into mathematical equations that are formulated, studied, analyzed, and interpreted [10]. In this context, many researchers have developed different mathematical models representing the dynamics of the rumor [11] and the elements interfering with its spread [12][13][14][15], and especially [16] in the work [17], authors gave a review and a study of several mathematical models of rumor's propagation.…”

Section: Introductionmentioning

confidence: 99%

Modeling a Rumor Propagation in Online Social Network: An Optimal Control Approach

Ghazzali

Bhih

Laaroussi

et al. 2020

Discrete Dynamics in Nature and Society

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We propose to model the phenomenon of the spread of a rumor in social networks in this paper. From an existing SIR model, we manipulate a new one that is based on the model of cholera in order to take into account professional pages that specialize in spreading rumors. In the second part, we introduce a control strategy to fight against the diffusion of the rumor. Our main objective is to characterize the three optimal controls that minimize the number of spreader users, fake pages, and the corresponding costs. For that matter, using the maximum principle of Pontryagin, we prove the existence and we give characterization of our controls. Numerical simulations are given to concretize our approach.

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Fast rumor source identification via random walks

Cited by 24 publications

References 12 publications

Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets

Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets

Rumor Source Detection in Networks Based on the SEIR Model

Modeling a Rumor Propagation in Online Social Network: An Optimal Control Approach

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