Source detection of rumor in social network – A review

Bayes-optimal inference for spreading processes on random networks

We consider a class of spreading processes on networks, which generalize commonly used epidemic models such as the SIR model or the SIS model with a bounded number of reinfections. We analyze the related problem of inference of the dynamics based on its partial observations. We analyze these inference problems on random networks via a message-passing inference algorithm derived from the belief propagation (BP) equations. We investigate whether said algorithm solves the problems in a Bayes-optimal way, i.e., no other algorithm can reach a better performance. For this, we leverage the so-called Nishimori conditions that must be satisfied by a Bayes-optimal algorithm. We also probe for phase transitions by considering the convergence time and by initializing the algorithm in both a random and an informed way and comparing the resulting fixed points. We present the corresponding phase diagrams. We find large regions of parameters where even for moderate system sizes the BP algorithm converges and satisfies closely the Nishimori conditions, and the problem is thus conjectured to be solved optimally in those regions. In other limited areas of the space of parameters, the Nishimori conditions are no longer satisfied and the BP algorithm struggles to converge. No sign of a phase transition is detected, however, and we attribute this failure of optimality to finite-size effects. The article is accompanied by a Python implementation of the algorithm that is easy to use or adapt.

Source identification via contact tracing in the presence of asymptomatic patients

Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent implementations of contact tracing algorithms, we propose a new framework, which we call Source Identification via Contact Tracing Framework (SICTF). In the SICTF, the source identification task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way, i.e., both contact and test queries can depend on the outcome of previous queries. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We implement two local search algorithms for the SICTF: the LS algorithm, which has recently been proposed by Waniek et al. in a similar framework, is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform previously proposed adaptive and non-adaptive source identification algorithms adapted to the SICTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the source identification probability of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator (DCS) developed by Lorch et al., which is the first time source identification algorithms are tested on such a complex dataset.

Detecting rumor outbreaks in online social networks

Social media platforms are broadly used to exchange information by milliards of people worldwide. Each day people share a lot of their updates and opinions on various types of topics. Moreover, politicians also use it to share their postulates and programs, shops to advertise their products, etc. Social media are so popular nowadays because of critical factors, including quick and accessible Internet communication, always available. These conditions make it easy to spread information from one user to another in close neighborhoods and around the whole social network located on the given platform. Unfortunately, it has recently been increasingly used for malicious purposes, e.g., rumor propagation. In most cases, the process starts from multiple nodes (users). There are numerous papers about detecting the real source with only one initiator. There is a lack of solutions dedicated to problems with multiple sources. Most solutions that meet those criteria need an accurate number of origins to detect them correctly, which is impossible to obtain in real-life usage. This paper analyzes the methods to detect rumor outbreaks in online social networks that can be used as an initial guess for the number of real propagation initiators.

Dissemination and Acceptance of COVID-19 Misinformation in Iran: A Qualitative Study

INTRODUCTION

Misinformation refers to unclear information from unreliable resources, and it is a common phenomenon of society; it can even constitute a part of family and social conversations due to the relative damages. Misinformation dissemination under the circumstances of the COVID-19 pandemic can be considered a cause of health-related anxieties and severe harm to the countries. The present study aims to explain factors related to the dissemination and acceptance of coronavirus misinformation in Iran.

MATERIAL AND METHODS

This qualitative study was carried out through content analysis in the year 2020. The population included Iranian experts in Information and Communication Sciences, among whom 19 experts were selected through the purposive sampling method. The data were collected through semi-structured interviews and analyzed through Continuous Thematic Analysis. Such criteria authenticated data trustworthiness as credibility, confirmability, dependability, and transferability proposed by Guba and Lincoln.

FINDINGS

Based on the experiences of participants, six major categories and 18 subcategories were identified regarding the major theme. The major categories included the official news and informing system, misinformation nature, users, media, misinformation source, and social determinants.

CONCLUSION

Concurrent with the Covid-19 outbreak, a sort of information tsunami occurs. Unavoidably, a significant amount of misinformation regarding the various aspects of the virus, such as its origin, transmission methods, control, and treatment, are published. In many cases, the spreading information is not checked and verified in terms of accuracy and authenticity. The spread and acceptance of Covid-19 related misinformation happen under the impact of various factors. Being familiar with these factors will boost the culture of health and pave the path toward establishing evidence-based information public awareness schemes.

Trading contact tracing efficiency for finding patient zero

As the COVID-19 pandemic has demonstrated, identifying the origin of a pandemic remains a challenging task. The search for patient zero may benefit from the widely-used and well-established toolkit of contact tracing methods, although this possibility has not been explored to date. We fill this gap by investigating the prospect of performing the source detection task as part of the contact tracing process, i.e., the possibility of tuning the parameters of the process in order to pinpoint the origin of the infection. To this end, we perform simulations on temporal networks using a recent diffusion model that recreates the dynamics of the COVID-19 pandemic. We find that increasing the budget for contact tracing beyond a certain threshold can significantly improve the identification of infected individuals but has diminishing returns in terms of source detection. Moreover, disease variants of higher infectivity make it easier to find the source but harder to identify infected individuals. Finally, we unravel a seemingly-intrinsic trade-off between the use of contact tracing to either identify infected nodes or detect the source of infection. This trade-off suggests that focusing on the identification of patient zero may come at the expense of identifying infected individuals.

Social diffusion sources can escape detection

Influencing others through social networks is fundamental to all human societies. Whether this happens through the diffusion of rumors, opinions, or viruses, identifying the diffusion source (i.e., the person that initiated it) is a problem that has attracted much research interest. Nevertheless, existing literature has ignored the possibility that the source might strategically modify the network structure (by rewiring links or introducing fake nodes) to escape detection. Here, without restricting our analysis to any particular diffusion scenario, we close this gap by evaluating two mechanisms that hide the source—one stemming from the source’s actions, the other from the network structure itself. This reveals that sources can easily escape detection, and that removing links is far more effective than introducing fake nodes. Thus, efforts should focus on exposing concealed ties rather than planted entities; such exposure would drastically improve our chances of detecting the diffusion source.

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We study the problem of hiding the diffusion source from source detection algorithms

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Finding an optimal way of hiding the source is usually computationally intractable

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In many cases, the source is well hidden without any strategic modifications

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If the source is exposed, simple heuristic solutions allow it to avoid detection

Combating multimodal fake news on social media: methods, datasets, and future perspective

The growth in the use of social media platforms such as Facebook and Twitter over the past decade has significantly facilitated and improved the way people communicate with each other. However, the information that is available and shared online is not always credible. These platforms provide a fertile ground for the rapid propagation of breaking news along with other misleading information. The enormous amounts of fake news present online have the potential to trigger serious problems at an individual level and in society at large. Detecting whether the given information is fake or not is a challenging problem and the traits of social media makes the task even more complicated as it eases the generation and spread of content to the masses leading to an enormous volume of content to analyze. The multimedia nature of fake news on online platforms has not been explored fully. This survey presents a comprehensive overview of the state-of-the-art techniques for combating fake news on online media with the prime focus on deep learning (DL) techniques keeping multimodality under consideration. Apart from this, various DL frameworks, pre-trained models, and transfer learning approaches are also underlined. As till date, there are only limited multimodal datasets that are available for this task, the paper highlights various data collection strategies that can be used along with a comparative analysis of available multimodal fake news datasets. The paper also highlights and discusses various open areas and challenges in this direction.

Blockchain-based rumor detection approach for COVID-19

The ubiquity of handheld devices and easy access to the Internet help users get easy and quick updates from social media. Generally, people share information with their friends and groups without inspecting the posts' veracity, which causes false information propagation in the network. Moreover, detecting false news and rumors in such a massive load of unstructured information is a very tedious task. Results, many literature papers explored different machine learning and deep learning approaches to detect the presence of rumors on social media networks. Although detection of misleading news and rumors is not sufficient, therefore, we have proposed a model for the detection and prevention of transmitted rumors in this paper. In this paper, we use blockchain technology to verify the credibility of information and design a framework with four layers: network layer, blockchain layer, machine layer, and device layer, to prevent the propagation of rumors in the network. We also use deep learning techniques to identify the anomalies in the network. The Bi-directional Long Short Term Memory (Bi-LSTM) model is used to prevent the introduction of new rumors by continuously monitoring incoming messages in the network. The experimental results demonstrate that the proposed Bi-LSTM model outperforms state-of-the-art machine learning methods and recent baseline work. Performance is compared over different metrics such as accuracy, precision, recall, f1-score, and specificity. Experiment results show that our Bi-LSTM model outperforms all the other approaches and achieved 99.63 % accuracy. Additionally, the probability of incorrect detection is significantly low with only 0.13% false positive.

Cross-social networks analysis: building me-edge centered BUNet dataset based on implicit bridge users

Purpose

The intensive blooming of social media, specifically social networks, pushed users to be integrated into more than one social network and therefore many new “cross-network” scenarios have emerged, including cross-social networks content posting and recommendation systems. For this reason, it is mightily a necessity to identify implicit bridge users across social networks, known as social network reconciliation problem, to deal with such scenarios.

Design/methodology/approach

We propose the BUNet (Bridge Users for cross-social Networks analysis) dataset built on the basis of a feature-based approach for identifying implicit bridge users across two popular social networks: Facebook and Twitter. The proposed approach leverages various similarity measures for identity matching. The Jaccard index is selected as the similarity measure outperforming all the tested measures for computing the degree of similarity between friends’ sets of two accounts of the same real person on two different social networks. Using “cross-site” linking functionality, the dataset is enriched by explicit me-edges from other social media websites.

Findings

Using the proposed approach, 399,407 users are extracted from different social platforms including an important number of bridge users shared across those platforms. Experimental results demonstrate that the proposed approach achieves good performance on implicit bridge users’ detection.

Originality/value

This paper contributes to the current scarcity of literature regarding cross-social networks analysis by providing researchers with a huge dataset of bridge users shared between different types of social media platforms.

Multi-source detection based on neighborhood entropy in social networks

The rapid development of social networking platforms has accelerated the spread of false information. Effective source location methods are essential to control the spread of false information. Most existing methods fail to make full use of the infection of neighborhood information in nodes, resulting in a poor source localization effect. In addition, most existing methods ignore the existence of multiple source nodes in the infected cluster and hard to identify the source nodes comprehensively. To solve these problems, we propose a new method about the multiple sources location with the neighborhood entropy. The method first defines the two kinds of entropy, i.e. infection adjacency entropy and infection intensity entropy, depending on whether neighbor nodes are infected or not. Then, the possibility of a node is evaluated by the neighborhood entropy. To locate the source nodes comprehensively, we propose a source location algorithm with the infected clusters. Other unrecognized source nodes in the infection cluster are identified by the cohesion of nodes, which can deal with the situation in the multiple source nodes in an infected cluster. We conduct experiments on various network topologies. Experimental results show that the two proposed algorithms outperform the existing methods.

Rumor detection in social network based on user, content and lexical features

Emergence in the social network leads to the extensive and faster diffusion of news than conventional news channels. Verification of data is challenging due to massive information on a social network. Unverified information can be a rumor or fake news that causes damage to an individuals and organizations, revealing the harmful impact on humanity. Therefore, it is vital to combat rumor diffusion to minimize the adverse effects on society. Despite vigorous efforts to deal with this issue, researchers mainly focussed on temporal dynamics of posts and other features like a user, network, content-based, which demonstrate a moderate accuracy. The time series features are associated with an event that suppresses the other quality features related to each post. There is a scope for improvement in the accuracy, so this paper focuses on post-wise features such as user-based, content-based and lexical-based features along with post sequences. We proposed a framework that uses various essential features and combines two deep learning models. Word embedding is utilized with bidirectional long short-term memory (BiLSTM) and combined with post-wise features using a multilayer perceptron (MLP), which improves accuracy. The experiments on the real-world dataset of Twitter demonstrate a notable improvement in accuracy compared to state-of-the-art approaches.

An Approach to Ranking the Sources of Information Dissemination in Social Networks

The problem of countering the spread of destructive content in social networks is currently relevant for most countries of the world. Basically, automatic monitoring systems are used to detect the sources of the spread of malicious information, and automated systems, operators, and counteraction scenarios are used to counteract it. The paper suggests an approach to ranking the sources of the distribution of messages with destructive content. In the process of ranking objects by priority, the number of messages created by the source and the integral indicator of the involvement of its audience are considered. The approach realizes the identification of the most popular and active sources of dissemination of destructive content. The approach does not require the analysis of graphs of relationships and provides an increase in the efficiency of the operator. The proposed solution is applicable both to brand reputation monitoring systems and for countering cyberbullying and the dissemination of destructive information in social networks.

Rumor surveillance methods in outbreaks: A systematic literature review

Background: The spreading of health-related rumors can profoundly put society at risk, and the investigation of strategies and methods can efficiently prevent the dissemination of hazardous rumor is necessary, especially during a public health emergency including disease outbreaks. In this article we review the studies that implicated the surveillance system in identifying rumors and discuss the different aspects of current methods in this field. Methods: We searched PubMed, EMBASE, Scopus, and Web of Science databases for relevant publications in English from 2000 to 2020. The PICOS approach was used to select articles, and two reviewers extracted the data. Findings were categorized as a source of rumors, type of systems, data collection, and data transmission methods. The quality of the articles was assessed using the Mixed Method Appraisal Tool (MMAT) checklist. Results: Five studies that presented the methods used for rumor detection in different outbreaks were included in the critical appraisal process. Findings were grouped into four categories: source of rumors, type of systems, data collection, and data transmission methods. The source of rumors in most studies was media, including new social and traditional media. The most used data collection methods were human-computer interaction technique, and automatic and manual methods each were discussed in one study. Also, the data transmission method was asynchronous in the majority of studies. Conclusion: Based on our findings, the most common rumor detection systems used in the outbreaks were manual and/or human-computer methods which are considered to be time-consuming processes. Due to the ever-increasing amount of modern social media platforms and the fast-spreading of misinformation in the times of outbreaks, developing the automatically and real-time tools for rumor detection is a vital need.

Integrating machine learning and open data into social Chatbot for filtering information rumor

Social networks have become a major platform for people to disseminate information, which can include negative rumors. In recent years, rumors on social networks has caused grave problems and considerable damages. We attempted to create a method to verify information from numerous social media messages. We propose a general architecture that integrates machine learning and open data with a Chatbot and is based cloud computing (MLODCCC), which can assist users in evaluating information authenticity on social platforms. The proposed MLODCCC architecture consists of six integrated modules: cloud computing, machine learning, data preparation, open data, chatbot, and intelligent social application modules. Food safety has garnered worldwide attention. Consequently, we used the proposed MLODCCC architecture to develop a Food Safety Information Platform (FSIP) that provides a friendly hyperlink and chatbot interface on Facebook to identify credible food safety information. The performance and accuracy of three binary classification algorithms, namely the decision tree, logistic regression, and support vector machine algorithms, operating in different cloud computing environments were compared. The binary classification accuracy was 0.769, which indicates that the proposed approach accurately classifies using the developed FSIP.

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.

Text Mining in Big Data Analytics

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.