Rumour Source Identification in Static Network
15 Jul 2020-pp 525-529
TL;DR: In the study, the time-varying networks are reduced to a series of static networks by introducing a time-integrating window and a reverse dissemination traversal algorithm is used to specify a set of suspects in the source.
Abstract: Rumour is a vital problem for modern techniques of communication. A piece of unauthenticated information travels around the social network creating chaos. In this study, based upon an integrated window prototype model network, N number of nodes are generated. In the study first, the time-varying networks are reduced to a series of static networks by introducing a time-integrating window. Second, instead of inspecting every individual, a reverse dissemination traversal algorithm is used to specify a set of suspects in the source. Third, to determine the real source from the suspects, a novel microscopic rumour spreading model is used to calculate the data counts for each suspect. The one who gets the largest count estimate is considered as to the real source. The proposed work develops a built-in dataset other than the cases, if a user sends existing information in the network it will not allow it to pass on and the fault information creator will block in the network and won't be allowed to send more information. Thus, the approaches help in building reliability in the system.
References
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TL;DR: A maximum a posteriori (MAP) estimator is constructed to identify the rumor source using the susceptible-infected (SI) model and sheds insight into the behavior of the rumor spreading process not only in the asymptotic regime but also for the general finite-n regime.
Abstract: Suppose that a rumor originating from a single source among a set of suspects spreads in a network, how to root out this rumor source? With the a priori knowledge of suspect nodes and an observation of infected nodes, we construct a maximum a posteriori (MAP) estimator to identify the rumor source using the susceptible-infected (SI) model. The a priori suspect set and its associated connectivity bring about new ingredients to the problem, and thus we propose to use local rumor center, a generalized concept based on rumor centrality, to identify the source from suspects. For regular tree-type networks of node degree {\delta}, we characterize Pc(n), the correct detection probability of the estimator upon observing n infected nodes, in both the finite and asymptotic regimes. First, when every infected node is a suspect, Pc(n) asymptotically grows from 0.25 to 0.307 with {\delta} from 3 to infinity, a result first established in Shah and Zaman (2011, 2012) via a different approach; and it monotonically decreases with n and increases with {\delta}. Second, when the suspects form a connected subgraph of the network, Pc(n) asymptotically significantly exceeds the a priori probability if {\delta}>2, and reliable detection is achieved as {\delta} becomes large; furthermore, it monotonically decreases with n and increases with {\delta}. Third, when there are only two suspects, Pc(n) is asymptotically at least 0.75 if {\delta}>2; and it increases with the distance between the two suspects. Fourth, when there are multiple suspects, among all possible connection patterns, that they form a connected subgraph of the network achieves the smallest detection probability. Our analysis leverages ideas from the Polya's urn model in probability theory and sheds insight into the behavior of the rumor spreading process not only in the asymptotic regime but also for the general finite-n regime.
144 citations
TL;DR: A new approach that jointly learns word embeddings and trains a recurrent neural network with two different objectives to automatically identify rumors is proposed that outperforms state-of-the-art methods in terms of precision, recall, and F1.
Abstract: Users of social media websites tend to rapidly spread breaking news and trending stories without considering their truthfulness. This facilitates the spread of rumors through social networks. A rumor is a story or statement for which truthfulness has not been verified. Efficiently detecting and acting upon rumors throughout social networks is of high importance to minimizing their harmful effect. However, detecting them is not a trivial task. They belong to unseen topics or events that are not covered in the training dataset. In this paper, we study the problem of detecting breaking news rumors, instead of long-lasting rumors, that spread in social media. We propose a new approach that jointly learns word embeddings and trains a recurrent neural network with two different objectives to automatically identify rumors. The proposed strategy is simple but effective to mitigate the topic shift issues. Emerging rumors do not have to be false at the time of the detection. They can be deemed later to be true or false. However, most previous studies on rumor detection focus on long-standing rumors and assume that rumors are always false. In contrast, our experiment simulates a cross-topic emerging rumor detection scenario with a real-life rumor dataset. Experimental results suggest that our proposed model outperforms state-of-the-art methods in terms of precision, recall, and F1.
136 citations
TL;DR: The proposed method is the first that can be used to identify rumor sources in time-varying social networks and addresses the scalability issue of source identification problems, and therefore dramatically promotes the efficiency of rumor source identification.
Abstract: Identifying rumor sources in social networks plays a critical role in limiting the damage caused by them through the timely quarantine of the sources. However, the temporal variation in the topology of social networks and the ongoing dynamic processes challenge our traditional source identification techniques that are considered in static networks. In this paper, we borrow an idea from criminology and propose a novel method to overcome the challenges. First, we reduce the time-varying networks to a series of static networks by introducing a time-integrating window. Second, instead of inspecting every individual in traditional techniques, we adopt a reverse dissemination strategy to specify a set of suspects of the real rumor source. This process addresses the scalability issue of source identification problems, and therefore dramatically promotes the efficiency of rumor source identification. Third, to determine the real source from the suspects, we employ a novel microscopic rumor spreading model to calculate the maximum likelihood (ML) for each suspect. The one who can provide the largest ML estimate is considered as the real source. The evaluations are carried out on real social networks with time-varying topology. The experiment results show that our method can reduce 60 - 90 percent of the source seeking area in various time-varying social networks. The results further indicate that our method can accurately identify the real source, or an individual who is very close to the real source. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in time-varying social networks.
80 citations
01 Nov 2019
TL;DR: An overview of the recent studies in the rumor detection field is given, a comprehensive list of datasets used for rumor detection is provided, and the important studies based on what types of information they exploit and the approaches they take are reviewed.
Abstract: Social media platforms have been used for information and news gathering, and they are very valuable in many applications. However, they also lead to the spreading of rumors and fake news. Many efforts have been taken to detect and debunk rumors on social media by analyzing their content and social context using machine learning techniques. This paper gives an overview of the recent studies in the rumor detection field. It provides a comprehensive list of datasets used for rumor detection, and reviews the important studies based on what types of information they exploit and the approaches they take. And more importantly, we also present several new directions for future research.
47 citations
"Rumour Source Identification in Sta..." refers methods in this paper
...In [4] the author has discussed about techniques for detecting rumor in the network....
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01 Jan 2019
TL;DR: This paper is a primer on rumor detection on social media which presents the basic terminology and types of rumors and the generic process of rumor detection, and a state-of-the-art depicting the use of supervised machine learning (ML) algorithms for rumor detectionon social media is presented.
Abstract: Information overload on the Web has been a well-identified challenge which has amplified with the advent of social web. Good, bad, true, false, useful, and useless are all kinds of information that disseminates through the social web platforms. It becomes exceedingly imperative to resolve rumors and inhibit them from spreading among the Internet users as it can jeopardize the well-being of the citizens. Rumor is defined as an unverified statement initiating from a single or multiple sources and eventually proliferates across meta-networks. The task for rumor detection intends to identify and classify a rumor either as true (factual), false (nonfactual), or unresolved. This can immensely benefit the society by preventing the spreading of such incorrect and inaccurate information proactively. This paper is a primer on rumor detection on social media which presents the basic terminology and types of rumors and the generic process of rumor detection. A state-of-the-art depicting the use of supervised machine learning (ML) algorithms for rumor detection on social media is presented. The key intent is to offer a stance to the amount and type of work conducted in the area of ML-based rumor detection on social media, to identify the research gaps within the domain.
33 citations
"Rumour Source Identification in Sta..." refers background in this paper
...to the amount and type of work conducted in the area [9]....
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