Information In Online Social Networks Research Articles

Modeling the dissemination of privacy information in online social networks

AbstractDriven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

Enriching the Fake News Process of Classifying Model using Deep Learning and Machine Learning Algorithm

Fake news detection has become a pressing issue due to the rapid propagation of misinformation through various online platforms. In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. Information carefulness on Internet, especially on social media, is an increasingly important concern, but web-scale data hampers, ability to identify, evaluate and correct such data, or so called "fake news," present in these platforms. In this paper, we propose a method for "fake news" detection and ways to apply it on Facebook, one of the most popular online social media platforms. This method uses Naive Bayes classification model to predict whether a post on Facebook will be labelled as real or fake.

A Game Theory-Based Model for the Dissemination of Privacy Information in Online Social Networks

Online social networks (OSNs) have experienced rapid growth in recent years, and an increasing number of people now use OSNs, such as Facebook and Twitter, to share and spread information on a daily basis. As a special type of information, user personal information is also widely disseminated in such networks, posing threats to user privacy. The study on privacy information dissemination is thus useful for the development of mechanisms and tools for the effective protection of privacy information in OSNs. In this paper, we propose to apply the game theory to establish a sender–receiver game model and the Nash equilibrium to describe the behavioral strategies of users in disseminating privacy information. Factors that affect the dissemination of privacy information are also analyzed with two important aspects: intimacy and popularity of the privacy-concerning subject. Simulation experiments were conducted based on real data sets from scale-free networks and real social networks to compare and analyze the effectiveness of the model. Results show that the proposed game theory is applicable to the privacy information dissemination model, which implements intimacy and popularity in the modeling of the dissemination of privacy information in OSNs. Both the impact of the macro-level OSNs and the micro-relationships between users are evaluated on the dissemination of privacy information, which provides a new perspective for exploring the dissemination of privacy information and facilitates the development of effective mechanisms for privacy protection in OSNs.

Controlling of Fake Information Dissemination in Online Social Networks: An Epidemiological Approach

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) is calculated, which is an important parameter in the study of message propagation in OSN. If <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> < 1, the propagation of rumor in the OSN will be minimal; nevertheless, if <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> > 1, the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models. Extensive theoretical study and computation analysis have also been used to validate the proposed model.

Dynamics of a Discrete Lotka–Volterra Information Diffusion Model

To explore the process of online social network information interaction, in this paper, we analyze the dynamics of a discrete Lotka–Volterra information diffusion model. Using the center manifold theorem, the conditions for transcritical bifurcation and flip bifurcation are obtained. With the help of approximation by a flow and Picard iteration, we explore the qualitative structures and stability of degenerate fixed point of the model with eigenvalues [Formula: see text]. What’s interesting is that our results reveal a new and complex qualitative structure for fixed point, which are different from the previous reports and called degenerate saddle point. Additionally, the qualitative structures provide a new idea for investigation the stability of degenerate fixed point. Meanwhile, near the maximum user density, the dynamic results of degenerate fixed point indicate that if the intervention rate is greater than the inverse of the maximum user density, then the higher user density decreases, the lower user density increases when intrinsic growth rates are small (between 0 and 2). However, when the intrinsic growth rate is greater than 2, the high user density will continue to increase until it approaches the maximum user density indefinitely, while the small user density will approach 0, which provide us with new insights into information diffusion. Finally, we show the results of the model by numerical simulations, and the characteristics of information diffusion near the degenerate fixed point are predicted by theoretical analysis.

A Differential Epidemic Model for Information, Misinformation, and Disinformation in Online Social Networks

These days the online social network has become a huge source of data. People are actively sharing information on these platforms. The data on online social networks can be misinformation, information, and disinformation. Because online social networks have become an important part of our lives, the information on online social networks makes a great impact on us. Here a differential epidemic model for information, misinformation, and disinformation on online social networks is proposed. The expression for basic reproduction number has been developed. Again, the stability condition for the system at both infection-free and endemic equilibriums points has been discussed. The numerical simulation has been performed to validate the theoretical results. Data available on Twitter related to COVID-19 vaccination is used to perform the experiment. Finally, the authors discuss the control strategy to minimize the misinformation and disinformation related to vaccination.

FAKE NEWS DETECTION USING MACHINE LEARNING

Fake news has been increasingly prevalent in recent years due to the quick growth of online social networks, which are used for a variety of political and commercial goals. Users of online social networks can easily become infected by these online fake news with deceptive language, and this has already had a significant impact on offline culture. Finding bogus news quickly is a crucial step in enhancing the reliability of information in online social networks. In order to identify false news pieces, creators, and subjects from online social networks and assess the performance of these methods and algorithms, this research looks at them. The report also discusses the difficulties posed by the varied linkages across news sources and the unknowable characteristics of fake news.

Research on older people's health information search behavior based on risk perception in social networks-A case study in China during COVID-19.

ObjectiveCOVID-19 has caused great loss of human life and livelihoods. The dissemination of health information in online social networks increased during the pandemic's quarantine. Older people are the most vulnerable group in sudden public health emergencies, and they have the disadvantage of infection rates and online search for health information. This study explores the relationship between the health risk perception and health information search behavior of older people in social networks, to help them make better use of the positive role of social networks in public health emergencies.MethodBased on the Risk Information Search and Processing model, and in the specific context of COVID-19, this study redefines health risk perception as a second-order construct of four first-order factors (perceived probability, perceived severity, perceived controllability, and perceived familiarity), and constructs a research model of the health risk perception and health information search behavior of older people. An online survey of people over 55 years old was conducted through convenience sampling in China from February 2020 to March 2020.ResultsA total of 646 older adults completed the survey. The structural equation model showed that health risk perception is a second-order factor (H1), that health risk perception has significant positive effects on health information search behavior (H2: β = 0.470, T = 11.577, P < 0.001), and that health risk perception has significant positive effects on affective response (H3: β = 0.536, T = 17.356, P < 0.001). In addition, affective response has a significant positive mediating effect on information sufficiency (H4: β = 0.435, T = 12.231, P < 0.001), and information sufficiency has a significant positive mediating effect on health information search behavior (H5: β = 0.136, T = 3.081, P = 0.002).ConclusionThe study results indicate that the health risk perception of older people during the COVID-19 outbreak not only directly affected their health information search behavior, but also had an indirect impact on their health information search behavior by affecting affective response and information sufficiency.

Web of things based social media fake news classification with feature extraction using pre-trained convoluted recurrent network with deep fuzzy learning

Fake news has been surfacing often and in great quantity online recently due to burgeoning development of online social networks for various economic and political goals. Online social network users can quickly become infected by this fake news by using deceptive language, and it has already had a significant impact on offline culture. Finding bogus news quickly is a crucial step in raising the credibility of information in online social networks. This study proposes a fuzzy logic-based web of things categorization system for detection of fake news. Deep learning algorithms were used to perform the feature extraction. The first feature extraction step has been completed. Convoluted recurrent network that has been pre-trained (Pre-Tr_Conv_ReNet). High dimensionality indexing was applied following the extraction of the characteristics. The feature, whether it be an image or text, is then identified using a ranking method based on indexing metrics. The learned file has been annotated using deep fuzzy learning (De_Fuz_Lear) to distinguish fraudulent web content after this training. Then decision was taken and the fake news was detected. The simulation results give the classified outputs. For this obtained output the parametric analysis has been done.

Research on Online Social Network Information Leakage-Tracking Algorithm Based on Deep Learning.

The rapid iteration of information technology makes the development of online social networks increasingly rapid, and its corresponding network scale is also increasingly large and complex. The corresponding algorithms to deal with social networks and their corresponding related problems are also increasing. The corresponding privacy protection algorithms such as encryption algorithm, access control strategy algorithm, and differential privacy protection algorithm have been studied and analyzed, but these algorithms do not completely solve the problem of privacy disclosure. Based on this, this article first searches and accurately filters the relevant information and content of online social networks based on the deep convolution neural network algorithm, so as to realize the perception and protection of users' safe content. For the corresponding graphics and data, this article introduces the compressed sensing technology to randomly disturb the corresponding graphics and data. At the level of tracking network information leakage algorithm, this article proposes a network information leakage-tracking algorithm based on digital fingerprint, which mainly uses relevant plug-ins to realize the unique identification processing of users, uses the uniqueness of digital fingerprint to realize the tracking processing of leakers, and formulates the corresponding coding scheme based on the social network topology, and at the same time, the network information leakage-tracking algorithm proposed in this article also has high efficiency in the corresponding digital coding efficiency and scalability. In order to verify the advantages of the online social network information leakage-tracking algorithm based on deep learning, this article compares it with the traditional algorithm. In the experimental part, this article mainly compares the accuracy index, recall index, and performance index. At the corresponding accuracy index level, it can be seen that the maximum improvement of the algorithm proposed in this article is about 10% compared with the traditional algorithm. At the corresponding recall index level, the proposed algorithm is about 5–8% higher than the traditional algorithm. Corresponding to the overall performance index, it improves the performance by about 50% compared with the traditional algorithm. The comparison results show that the proposed algorithm has higher accuracy and the corresponding source tracking is more accurate.

Social network analysis and applications: A review of the broad research aspects of social network structure

The use of social media has generated huge Online Social Networks (OSN). This network exhibits nice structural properties and has opened door to various research tasks like community detection, link prediction and influence modeling in OSN. There are several models such as linear threshold and independent cascade model proposed in the literature that models the influence diffusion process. Depending on the use-case one may want to amplify or attenuate information in OSN called influence maximization (IM) and rumor blocking (RB), respectively. In terms of marketing, it is called viral marketing. This paper extensively reviews the fundamental research tasks associated with the structure of OSN, groups them into different categories based on the fundamental research area discussed along with its applications. This paper also surveys RB and the various variants of IM found in the literature.

Ultra-efficient information detection on large-scale online social networks

As a powerful and low-cost instant information dissemination platform, large-scale online social networks (OSNs) play a pivotal role in shaping our modern information age. The efficient detection of wide-spreading information in OSNs is very important in many aspects including public opinion supervision, social governance, stock markets, counter-terrorism and presidential election. However, real-world OSNs have gigantic sizes and thus their full structural data are usually unavailable, making this problem extremely challenging. In this work, we illustrate the close mapping between efficient detection and optimal spreading from the perspective of network percolation theory. This analogy inspires us to propose a theory of using only limited local network information to select the optimal set of information sensors. Through extensive simulations on both synthetic and real-world networks, we find that for networks with theoretically infinite size, only a finite and small number of sensor nodes are needed to detect the global spreading information with almost certainty. Most importantly, we empirically confirm the utility of our theory on the largest micro blog in China by crawling almost the full Sina Weibo social network with 99,546,027 users in 2014 and the real spreading data of Weibo messages.

Is Hidden Safe? Location Protection against Machine-Learning Prediction Attacks in Social Networks

User privacy protection is a vital issue of concern for online social networks (OSNs). Even though users often intentionally hide their private information in OSNs, since adversaries may conduct prediction attacks to predict hidden information using advanced machine learning techniques, private information that users intend to hide may still be at risk of being exposed. Taking the current city listed on Facebook profiles as a case, we propose a solution that estimates and manages the exposure risk of users’ hidden information. First, we simulate an aggressive prediction attack using advanced state-of-the-art machine learning algorithms by proposing a new current city prediction framework that integrates location indications based on various types of information exposed by users, including demographic attributes, behaviors, and relationships. Second, we study prediction attack results to model patterns of prediction correctness (as correct predictions lead to information exposures) and construct an exposure risk estimator. The proposed exposure risk estimator has the ability not only to notify users of exposure risks related to their hidden current city but can also help users mitigate exposure risks by overhauling and selecting countermeasures. Moreover, our exposure risk estimator can improve the privacy management of OSNs by facilitating empirical studies on the exposure risks of OSN users as a group. Taking the current city as a case, this work offers insight on how to protect other types of private information against machine-learning prediction attacks and reveals several important implications for both practice management and future research.

Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules

A malicious data miner can infer users’ private information in online social networks (OSNs) by data mining the users’ disclosed information. By exploring the public information about a target user (i.e. an individual or a group of OSN users whose privacy is under attack), attackers can prepare a training data set holding similar information about other users who openly disclosed their data. Using a machine learning classifier, the attacker can input released information about users under attack as non-class attributes and extract the private information as a class attribute. Some techniques offer some privacy protection against specific classifiers;, however, the provided privacy can be under threat if an attacker uses a different classifier (rather than the one used by the privacy protection techniques) to infer sensitive information. In reality, it is difficult to predict the classifiers involved in a privacy attack. In this study, we propose a privacy-preserving technique which first prepares a training data set in a similar way that an attacker can prepare and then takes an approach independent of the classifiers to extract patterns (or logic rules) from the training data set. Based on the extracted rule set, it then suggests the target users to hide some non-class attribute values and/or modify some friendship links for protecting their privacy. We apply our proposed technique on two OSN data sets containing users’ attribute values and their friendship links. For evaluating the performance of the proposed technique, we use conventional classifiers such as Na $$\ddot{\text {i}}$$ ve Bayes, Support Vector Machine and Random Forest on the privacy-protected data sets. The experimental results show that our proposed technique outperforms the existing privacy-preserving algorithms in terms of securing privacy while maintaining the data utility.

An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

Online Social Network Information Dissemination Integrating Overconfidence and Evolutionary Game Theory

Public opinion inversion and other nonlinear phenomena often occur during online social network information dissemination (OSNID) in modern society. To explore the influence of overconfidence on OSNID, we develop a multi-agent simulation model integrating overconfidence and evolutionary games, as previous research has scarcely paid any attention to irrational behavior in information dissemination. This integrated paradigm provides an effective tool for managers to control the spread of OSNID and reduce the harm caused by rumors. The theoretical model is constructed from the perspective of an evolutionary game and, combined with overconfidence theory, three overconfidence scenarios are designed: benefit overconfidence, cost overconfidence, and both benefit and cost overconfidence. Then, a multi-agent simulation model of OSNID under different overconfidence scenarios is realized. The proposed simulation model exhibited better performance (e.g., faster diffusion speed) than the traditional Bass model; its performance was also validated by comparison with real-world cases. The results demonstrate that (1) with increasing benefit overconfidence, the convergence speed of OSNID will be accelerated, and the user group will reach stability at a faster speed. (2) With increasing cost overconfidence, the user's decision will change from adopting dominant to tit-for-tat, and the adoption ratio will gradually decrease. (3) Compared with overconfidence in earnings, overconfidence in costs is more conducive to improving stability. This study attempts to provide new ideas for OSNID and attempts to propose an integration framework for behavioral theory and simulation methods.

Dynamic model with super spreaders and lurker users for preferential information propagation analysis

Publication of preferential information via online social networks has recently increased in business promotional activities. This activity involves consideration of the propagation of preferential information in online social networks as well as the effects of such propagation on shopping choices. This paper proposes the susceptible-lurker-super-normal-recovered (SEAIR) dynamics model to address the propagation process of preferential information in the Weibo network. In the proposed SEAIR model, super spreader and lurker user compartments are adopted, and entry and exit are available to and from every compartment, which more realistically reflects circumstances in Weibo. Dynamic equations of the SEAIR model are proposed based on the mean-field theory, and the basic reproductive ratio (threshold) R0 derived. Furthermore, it is proved that the information-free equilibrium state of the SEAIR model is locally and globally stable when R0 is less than one, at which point the propagation process tends to disappear; there is a unique endemic equilibrium state when R0 is greater than one. To verify the performance of the SEAIR model, a Baidu App promotion activity in Weibo is used as a real case study. Compared to the conventional susceptible–infected–recovered (SIR) model, numerical results show that the SEAIR model reduces the root mean square error (RMSE) and the mean absolute error (MAE) by 33.2% and 24.74%, respectively. The influence of the main parameters on the performance of the SEAIR model are also analyzed.

Extent prediction of the information and influence propagation in online social networks

We present a new mathematical model that predicts the number of users informed and influenced by messages that are propagated in an online social network. Our model is based on a new way of quantifying the tie-strength, which in turn considers the affinity and relevance between nodes. We could verify that the messages to inform and influence, as well as their importance, produce different propagation behaviors in an online social network. We carried out laboratory tests with our model and with the baseline models Linear Threshold and Independent Cascade, which are currently used in many scientific works. The results were evaluated by comparing them with empirical data. The tests show conclusively that the predictions of our model are notably more accurate and precise than the predictions of the baseline models. Our model can contribute to the development of models that maximize the propagation of messages; to predict the spread of viruses in computer networks, mobile telephony and online social networks.

An Efficient Feedback Control Mechanism for Positive/Negative Information Spread in Online Social Networks.

The wide availability of online social networks (OSNs) facilitates positive information spread and sharing. However, the high autonomy and openness of the OSNs also allow for the rapid spread of negative information, such as unsubstantiated rumors and other forms of misinformation that often elicit widespread public cognitive misleads and huge economic losses. Therefore, how to effectively control the negative information spread accompanied by positive information has emerged as a challenging issue. Unfortunately, this issue still remains largely unexplored to date. To fill this gap, we propose an efficient feedback control mechanism for the simultaneous spread of the positive and negative information in OSNs. Specifically, a novel computational model is first proposed to present the temporal dynamics of the positive and negative information spread. Furthermore, the proposed mechanism restrains the negative information spread with minimal system expenses by devising and performing three synergetic intervention strategies. Technically, this mechanism intensively evaluates the number of seed users performing three intervention strategies. Besides, each seed user performs the received control task independently, and then the control plan for the next time step is adjusted dynamically according to the previous feedback results. Finally, we evaluate the efficiency of the proposed mechanism based on the extensive experimental results obtained from two real-world networks.

Efficiently discovering users connectivity with local information in online social networks

People’s activities in Online Social Networks (OSNs) have generated a massive volume of data to which tremendous attention has been paid in academia and industry. With such data, researchers and third-parties can analyze human beings’ behaviors in social communities and develop more user-friendly services and applications to meet people’s needs. However, often times, they face a big challenge of acquiring the data, as the access to such data is restricted by their collectors (e.g., Facebook and Twitter), due to various reasons, such as their user’s privacy. In this paper, we intend to shed light on leveraging limited local social network topological properties to effectively and efficiently conduct search in OSNs. The problem we focus on is to discover the connectivity of a group of target users in an OSN, particularly from the perspective of a third-party analyst who does not have full access to the network. For the analyst, even discovering a user’s local connections requires issuing a query through OSN APIs (e.g., Facebook Friendlist API or Twitter Followerlist API). We develop searching techniques which demand only a few number of queries for the connectivity discovery.After conducting an intensive set of experiments on both real-world and synthetic data sets, we found that our proposed techniques perform as well as the centralized detection algorithm, which assumes the availability of the entire data set, in terms of the size of the discovered subgraph connecting all target users as well as the number of queries made in the search. The experiment results demonstrate the effectiveness of incorporating topological properties of social networks into searching in the OSNs.