Real-Time and Cost-Effective Limitation of Misinformation Propagation

Litou, Iouliana; Kalogeraki, Vana; Katakis, Ioannis; Gunopulos, Dimitrios

doi:10.1109/mdm.2016.33

Cited by 19 publications

(21 citation statements)

References 18 publications

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“…There are several approaches to tackle the problem of unreliable content on social media. For example, some authors opt by analyzing the patterns of propagation [35,49,60], others by creating supervised systems to classify unreliable content [5,42,59], and others by focusing on the characteristics of the accounts that share this type of content [7,9,17]. In addition, some works also focus on developing techniques for fact-checking claims [12,52] or focus on specific case studies [2,13,26,57].…”

Section: Literature Reviewmentioning

confidence: 99%

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A Brief Overview on the Strategiesto Fight Back the Spreadof False Information

Figueira

Guimarães

Torgo

2019

JWE

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The proliferation of false information on social networks is one of the hardest challenges in today's society, with implications capable of changing users perception on what is a fact or rumor. Due to its complexity, there has been an overwhelming number of contributions from the research community like the analysis of specific events where rumors are spread, analysis of the propagation of false content on the network, or machine learning algorithms to distinguish what is a fact and what is "fake news". In this paper, we identify and summarize some of the most prevalent works on the different categories studied. Finally, we also discuss the methods applied to deceive users and what are the next main challenges of this area.

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Section: Literature Reviewmentioning

confidence: 99%

“…A similar approach is proposed in [35] where a Dynamic Linear Model is developed to timely limit the propagation of misinformation. The model differs from other works since it relies on the ability for the user's susceptibility to change over time and how it affects its dissemination of information.…”

Section: Network Propagationmentioning

confidence: 99%

A Brief Overview on the Strategiesto Fight Back the Spreadof False Information

Figueira

Guimarães

Torgo

2019

JWE

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“…In particular, according to recent studies about polarization of opinion in OSNs (Anagnostopoulos et al 2015) and related works about misinformation reduction (Kumar and Geethakumari 2014;Lewandowsky et al 2012), a crucial aspect is to intervene before a competing campaign can reach the users, or at least soon enough, so that a user does not have time to radicalize her/his thoughts. This idea was first captured in (Litou et al 2016), where a dynamic LT model (DLT) is defined to deal with competitive information cascades. The influence weights temporally decay according to a Poisson distribution, and every node can be either positively or negatively activated at a given time depending on the absolute value of the cumulative influence of its neighbors, while the activation sign depends on the sign of the cumulative influence.…”

Section: Related Workmentioning

confidence: 99%

Complex influence propagation based on trust-aware dynamic linear threshold models

Caliò

Tagarelli

2019

Appl Netw Sci

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To properly capture the complexity of influence propagation phenomena in real-world contexts, such as those related to viral marketing and misinformation spread, information diffusion models should fulfill a number of requirements. These include accounting for several dynamic aspects in the propagation (e.g., latency, time horizon), dealing with multiple cascades of information that might occur competitively, accounting for the contingencies that lead a user to change her/his adoption of one or alternative information items, and leveraging trust/distrust in the users' relationships and its effect of influence on the users' decisions. To the best of our knowledge, no diffusion model unifying all of the above requirements has been developed so far. In this work, we address such a challenge and propose a novel class of diffusion models, inspired by the classic linear threshold model, which are designed to deal with trust-aware, non-competitive as well as competitive time-varying propagation scenarios. Our theoretical inspection of the proposed models unveils important findings on the relations with existing linear threshold models for which properties are known about whether monotonicity and submodularity hold for the corresponding activation function. We also propose strategies for the selection of the initial spreaders of the propagation process, for both non-competitive and competitive influence propagation tasks, whose goal is to mimic contexts of misinformation spread. Our extensive experimental evaluation, which was conducted on publicly available networks and included comparison with competing methods, provides evidence on the meaningfulness and uniqueness of our models.

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“…Time constraints of misinformation propagation are also studied. Litou et al (2016) propose an information propagation model, called the Dynamic Linear Threshold (DLT) model, to distinguish credible information from misinformation. This work models an individual user's different information propagation time window and susceptibility to new information on whether to propagate it over a network or not.…”

Section: Network Analysis-based Approachmentioning

confidence: 99%

Untitled

2019

Trans. Soc. Comput.

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Many network scientists have investigated the problem of mitigating or removing false information propagated in social networks. False information falls into two broad categories: disinformation and misinformation. Disinformation represents false information that is knowingly shared and distributed with malicious intent. Misinformation in contrast is false information shared unwittingly, without any malicious intent. Many existing methods to mitigate or remove false information in networks concentrate on methods to find a set of seeding nodes (or agents) based on their network characteristics (e.g., centrality features) to treat. The aim of these methods is to disseminate correct information in the most efficient way. However, little work has focused on the role of uncertainty as a factor in the formulation of agents' opinions. Uncertainty-aware agents can form different opinions and eventual beliefs about true or false information resulting in different patterns of information diffusion in networks.In this work, we leverage an opinion model, called Subjective Logic (SL), which explicitly deals with a level of uncertainty in an opinion where the opinion is defined as a combination of belief, disbelief, and uncertainty, and the level of uncertainty is easily interpreted as a person's confidence in the given belief or disbelief. However, SL considers the dimension of uncertainty only derived from a lack of information (i.e., ignorance), not from other causes, such as conflicting evidence. In the era of Big Data, where we are flooded with information, conflicting information can increase uncertainty (or ambiguity) and have a greater effect on opinions than a lack of information (or ignorance). To enhance the capability of SL to deal with ambiguity as well as ignorance, we propose an SL-based opinion model that includes a level of uncertainty derived from both causes. By developing a variant of the Susceptible-Infected-Recovered epidemic model that can change an agent's status based on the state of their opinions, we capture the evolution of agents' opinions over time.We present an analysis and discussion of critical changes in network outcomes under varying values of key design parameters, including the frequency ratio of true or false information propagation, centrality metrics used for selecting seeding false informers and true informers, an opinion decay factor, the degree of agents' prior belief, and the percentage of true informers. We validated our proposed opinion model using both the synthetic network environments and realistic network environments considering a real network topology, 5:2 J.-H. Cho et al.user behaviors, and the quality of news articles. The proposed agent's opinion model and corresponding strategies to deal with false information can be applicable to combat the spread of fake news in various social media platforms (e.g., Facebook).

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Real-Time and Cost-Effective Limitation of Misinformation Propagation

Cited by 19 publications

References 18 publications

A Brief Overview on the Strategiesto Fight Back the Spreadof False Information

A Brief Overview on the Strategiesto Fight Back the Spreadof False Information

Complex influence propagation based on trust-aware dynamic linear threshold models

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