Online Social Networks Users Research Articles

Facebook Tells Me Your Gender: An Exploratory Study of Gender Prediction for Turkish Facebook Users

Online Social Networks (OSNs) are very popular platforms for social interaction. Data posted publicly over OSNs pose various threats against the individual privacy of OSN users. Adversaries can try to predict private attribute values, such as gender, as well as links/connections. Quantifying an adversary’s capacity in inferring the gender of an OSN user is an important first step towards privacy protection. Numerous studies have been made on the problem of predicting the gender of an author/user, especially in the context of the English language. Conversely, studies in this field are quite limited for the Turkish language and specifically in the domain of OSNs. Previous studies for gender prediction of Turkish OSN users have mostly been performed by using the content of tweets and Facebook comments. In this article, we propose using various features, not just user comments, for the gender prediction problem over the Facebook OSN. Unlike existing studies, we exploited features extracted from profile, wall content, and network structure, as well as wall interactions of the user. Therefore, our study differs from the existing work in the broadness of the features considered, machine learning and deep learning methods applied, and the size of the OSN dataset used in the experimental evaluation. Our results indicate that basic profile information provides better results; moreover, using this information together with wall interactions improves prediction quality. We measured the best accuracy value as 0.982, which was obtained by combining profile data and wall interactions of Turkish OSN users. In the wall interactions model, we introduced 34 different features that provide better results than the existing content-based studies for Turkish.

On Modeling Influence Maximization in Social Activity Networks under General Settings

Finding the set of most influential users in online social networks (OSNs) to trigger the largest influence cascade is meaningful, e.g., companies may leverage the “word-of-mouth” effect to trigger a large cascade of purchases by offering free samples/discounts to those most influential users. This task is usually modeled as an influence maximization problem, and it has been widely studied in the past decade. However, considering that users in OSNs may participate in various online activities, e.g., joining discussion groups and commenting on same pages or products, influence diffusion through online activities becomes even more significant. In this article, we study the impact of online activities by formulating social-activity networks which contain both users and online activities, and thus induce two types of weighted edges, i.e., edges between users and edges between users and activities. To address the computation challenge, we define an influence centrality via random walks, and use the Monte Carlo framework to efficiently estimate the centrality. Furthermore, we develop a greedy-based algorithm with novel optimizations to find the most influential users for node recommendation. Experiments on real-world datasets show that our approach is very computationally efficient under different influence models, and also achieves larger influence spread by considering online activities.

Deployment of Information Diffusion for Community Detection in Online Social Networks: A Comprehensive Review

The flow of information through active users in online social networks (OSNs) plays a major role in forming natural social groups, popularly known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">communities</i> . Although structural and topological aspects of the network had been central to most of the community detection approaches, incorporation of information flow for community detection has been an emerging topic in the recent past. Often, the flow of information is studied as a traceable process called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information diffusion</i> . The flow of information in the network affects various factors like temporal characteristics, network attributes, or social attributes. The information diffusion process helps to extract this information including where and when information is generated and in what fashion the dispersion occurs. Thus, it has the potential to aid the community detection process in social networks. In this article, the deployment of the information diffusion process for community detection has been studied extensively. The study is mainly focused on how information flow affects various network properties and social facets and explored the possibility of deployment for community detection. Various information diffusion models and community detection algorithms have been discussed in the context of network properties and social facets. Current challenges, future directions, and modalities for the deployment of information diffusion in community detection have been discussed. In addition, various widely used datasets, evaluation metrics as well as evaluation methods for evaluating community detection algorithms are also detailed.

Cross-site Prediction on Social Influence for Cold-start Users in Online Social Networks

Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs. Descriptive features are extracted from the public data on a user’s homepage. In particular, to extract useful information from the fine-grained dynamic activities that cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.

Mutual Influence of Users Credibility and News Spreading in Online Social Networks

A real-time news spreading is now available for everyone, especially thanks to Online Social Networks (OSNs) that easily endorse gate watching, so the collective intelligence and knowledge of dedicated communities are exploited to filter the news flow and to highlight and debate relevant topics. The main drawback is that the responsibility for judging the content and accuracy of information moves from editors and journalists to online information users, with the side effect of the potential growth of fake news. In such a scenario, trustworthiness about information providers cannot be overlooked anymore, rather it more and more helps in discerning real news from fakes. In this paper we evaluate how trustworthiness among OSN users influences the news spreading process. To this purpose, we consider the news spreading as a Susceptible-Infected-Recovered (SIR) process in OSN, adding the contribution credibility of users as a layer on top of OSN. Simulations with both fake and true news spreading on such a multiplex network show that the credibility improves the diffusion of real news while limiting the propagation of fakes. The proposed approach can also be extended to real social networks.

A joint sharing approach for online privacy preservation

Online social networks (OSNs) have shown rapid growth in the past decade and are consistently growing at a high rate. OSNs provide an efficient way of performing online social interactions, data sharing, and multimedia communications with millions of internet users. Along with the advantages, OSNs also raise some security and privacy challenges. Currently, available sharing mechanisms can protect data associated with a single user but lack in protecting data associated with multiple users. Considering privacy issues related to multiparty online objects, this research proposes a collaborative sharing model that works on the principles of joint sharing to fulfill privacy needs of OSN users on shared objects. The proposed model follows multiparty authorizations, the socio-technical concepts of joint sharing, and joint-ownership policy enforcement mechanism. It also supports a fine-grained automated conflict resolution method for jointly owned objects. Formal verification of the proposed model is performed through Petri-nets, its semantics are validated through ontologies, while a third-party Facebook application is developed for proof of concept. The analysis of users’ response, performed through the technology acceptance model (TAM), reflects an 81% acceptance of the proposed joint sharing model.

Finding influential users in microblogs: state-of-the-art methods and open research challenges

ABSTRACT Social networks are online platforms that people use for interaction, information sharing and propagation of new ideas. Finding influential users in online social networks is a significant research problem due to its vast research applications in information diffusion, marketing and advertising. The relevant literature presents several models proposed for identifying influential users in social networks. In this survey, we present a review of the most relevant studies on influential users mining in microblog networks. First, we propose a new taxonomy by classifying the influence finding algorithms into five main categories based on their underlying framework and baseline methods. Second, each study is analysed according to the proposed framework, experimental datasets, validation approaches and evaluation results. Finally, the survey concludes with discussion on applications from the relevant literature, exploring open research challenges and presenting possible future research directions. The findings of this survey indicate that influential users mining in microblogs has many applications in marketing, advertising and information diffusion. In addition, this survey can be used as a guideline, particularly by young researchers, for establishing a baseline before initiating a research or identifying attractive as well as relevant research insights.

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.

Usable Security and Privacy on Online Social Networks

Hundreds of millions of people use online social networks (OSNs), and this use is growing rapidly. On the one hand, users of OSNs have created accounts, which include their personal information, interests, associations, postings, daily activities, sharing of social media, and so forth. In addition, users want to protect these information and activities. But on the other hand, they want to use the services, which are provided by OSNs in order to stay in touch with others. Therefore, for achieving this desire, users reveal their personal information intentionally or accidentally. Why? Because users of OSNs do not have adequate information that helps them in making security and privacy decisions effectively in addition to inadequate feedback about the implications of user decisions. This means users do not know what to reveal and how. In order to cope with this problem, a systematic literature review of the usable security and privacy on OSNs was performed. This review is an attempt to pursue the contours of research in OSNs. Therefore, this paper discusses the existing research work, highlights the main research challenges and existing solutions, identifies open research issues, and proposes some directions for future research in the usable security and privacy on OSNs.

Detection of Malicious Profiles and Protecting Users in Online Social Networks

Everyone today actively uses online social networks to get in touch with their friends, for career opportunities, and business also. Some of the most popular social networks are Facebook, Instagram, LinkedIn, and Twitter. But the question is how much their authentication systems are built in a secured way. The authentication systems mostly depend on the user's general details such as name, photo, and location. In such systems, mischievous persons can easily create fake profiles by cloning the user’s identity to abuse the user’s information. Using fake profiles they can misuse original user’s photos, contacts, and videos. This kind of mischievous person can be identified by the use of privacy detection mechanisms. So, this research emphasizes some data mining techniques for protecting the original user’s information and to identify the malicious accounts in social network sites. By the assessment of 3PS (Publically Privacy Protection System), this work employs the malicious account detection method in OSN depending upon the mischievous person’s uncountable shared posts in a day and latest activity and behaviors. Examining the network similarity and comparison of attributes threshold values referred to the original user’s profile can be used to identify the malicious accounts. For this E_SVM-NN classifier is used based on the feature reduction techniques. This work involves in creating OSN accounts for experiments and investigates the latest updates, posts, comments, photos, and performing online search etc. which are used to evaluate the effectiveness and significance of the proposed work in contrast to the previous works.

Information Disclosure and Control on Online Social Network Sites among Medical Undergraduates

Introduction: Internet usage has become a big part of many people’s lives, and among the many uses SocialNetworking Sites (SNS) which became very prevalent. How privacy concerns, trust and control are affectingSNS usage and self-disclosure has become an important question for many people.Aim: Aim of the study is to assess the information disclosure and control on online social network sitesamong medical undergraduates.Methodology: The study was conducted at Saveetha medical college and hospital among medicalundergraduates. A questionnaire on usage of online social networks and information disclosures wasadministered and analysed it for reliability, social network site usage, privacy concerns, and disclosure ofautobiographical memories.Result: Almost 81.1% of the people feel that their browsing history were tracked by social networking sites.Instagram is mainly used app by medical undergraduates (40%).Conclusion: There is an excess usage of social networking sites and easy information disclosure is seen inthe population.

Discovering Hidden Topical Hubs and Authorities Across Multiple Online Social Networks

Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users' relationship and textual content. However, many of the previous works are confined to identifying influential users within a single OSN. These models, when applied to multiple OSNs, could not learn influential users under a common set of topics nor address platform preferences. In this paper, we therefore propose the MPHAT model, an extension of HAT, to jointly model the topic-specific hub users, authority users, their topical interests and platform preferences. We evaluate MPHAT against several existing state-of-the-art methods in three tasks: (i) modeling of topics, (ii) platform choice prediction, and (iii) link recommendation. Based on our extensive experiments in multiple OSNs settings using synthetic datasets and real-world datasets from Twitter and Instagram, we show that MPHAT is comparable to state-of-the-art topic models in learning topics but outperforms the state-of-the-art models in platform prediction and link recommendation tasks. We also empirically demonstrate the ability of MPHAT to determine influential users within and across multiple OSNs.

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

With the rapid development of social networks, users pay more and more attention to the protection of personal information. However, the transmission of users’ personal information through social networks will inevitably lead to privacy leakage and make users attacked. A large amount of privacy information can be inferred from the content and social traces published by users, which leads to the rise of privacy inference technology for users in social networks. Social relationship inference and attribute inference are two basic attacks on users’ privacy in social networks. This is the first systematic review of privacy inference attacks in social networks. The purpose of this retrospective study is to provide a global summary, summarizing the time trend of the topic, and show the evolution of the topic in the past 15 years. In addition, this paper discusses the trend and development of this topic and finally looks forward to future work. The contribution of our work is helpful for researchers to continue their research in this field.

Hybrid graph-based Sybil detection with user behavior patterns

Online social networks (OSNs) are known to be vulnerable to Sybil Attack, where attackers leverage the openness to create multiple fake identities for launching many malicious activities. In this paper, we define a weighted-strong-social (WSS) graph that integrates the OSN structure and user behavior patterns and propose a novel hybrid graph-based sybil detection approach. The hybrid approach estimates the trustworthiness of users and user pairs based on user behaviors that can be obtained locally and add them to the OSN structure to construct a WSS graph for sybil detection. The evaluation results show that the AUC of the hybrid approach is 0.954, which is significantly higher than that of previous sybil detection methods.

DeepSBD: A Deep Neural Network Model With Attention Mechanism for SocialBot Detection

Online Social Networks (OSNs) are witnessing sophisticated cyber threats, that are generally conducted using fake or compromised profiles. Automated agents ( aka socialbots), a category of sophisticated and modern threat entities, are the native of the social media platforms and responsible for various modern weaponized information-related attacks, such as astroturfing, misinformation diffusion, and spamming. Detecting socialbots is a challenging and vital task due to their deceiving character of imitating human behavior. To this end, this paper presents an attention-aware deep neural network model, DeepSBD , for detecting socialbots on OSNs. The DeepSBD models users’ behavior using profile , temporal , activity , and content information. It jointly models OSN users’ behavior using Bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures. It models profile , temporal , and activity information as sequences, which are fed to a two-layers stacked BiLSTM, whereas content information is fed to a deep CNN. We have evaluated DeepSBD over five real-world benchmark datasets and found that it performs significantly better in comparison to the state-of-the-arts and baseline methods. We have also analyzed the efficacy of DeepSBD at different ratios of socialbots and benign users and found that an imbalanced dataset moderately affects the classification accuracy. Finally, we have analyzed the discrimination power of different behavioral components, and it is found that both profile characteristics and content behavior are most impactful, whereas diurnal temporal behavior is the least effective for detecting socialbots on OSNs.

Impact of Unreliable Content on Social Media Users during COVID-19 and Stance Detection System

The abundant dissemination of misinformation regarding coronavirus disease 2019 (COVID-19) presents another unprecedented issue to the world, along with the health crisis. Online social network (OSN) platforms intensify this problem by allowing their users to easily distort and fabricate the information and disseminate it farther and rapidly. In this paper, we study the impact of misinformation associated with a religious inflection on the psychology and behavior of the OSN users. The article presents a detailed study to understand the reaction of social media users when exposed to unverified content related to the Islamic community during the COVID-19 lockdown period in India. The analysis was carried out on Twitter users where the data were collected using three scraping packages, Tweepy, Selenium, and Beautiful Soup, to cover more users affected by this misinformation. A labeled dataset is prepared where each tweet is assigned one of the four reaction polarities, namely, E (endorse), D (deny), Q (question), and N (neutral). Analysis of collected data was carried out in five phases where we investigate the engagement of E, D, Q, and N users, tone of the tweets, and the consequence upon repeated exposure of such information. The evidence demonstrates that the circulation of such content during the pandemic and lockdown phase had made people more vulnerable in perceiving the unreliable tweets as fact. It was also observed that people absorbed the negativity of the online content, which induced a feeling of hatred, anger, distress, and fear among them. People with similar mindset form online groups and express their negative attitude to other groups based on their opinions, indicating the strong signals of social unrest and public tensions in society. The paper also presents a deep learning-based stance detection model as one of the automated mechanisms for tracking the news on Twitter as being potentially false. Stance classifier aims to predict the attitude of a tweet towards a news headline and thereby assists in determining the veracity of news by monitoring the distribution of different reactions of the users towards it. The proposed model, employing deep learning (convolutional neural network(CNN)) and sentence embedding (bidirectional encoder representations from transformers(BERT)) techniques, outperforms the existing systems. The performance is evaluated on the benchmark SemEval stance dataset. Furthermore, a newly annotated dataset is prepared and released with this study to help the research of this domain.

Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem

Online social network (OSN) users are increasingly interacting with each other via articles, comments, and responses. When access control mechanisms are weak or absent, OSNs are perceived by attackers as rich environments for influencing public opinions via fake news posts or influencing commercial transactions via practices such as phishing. This has led to a body of research looking at potential ways to predict OSN user behavior using social science concepts such as conformity and the bandwagon effect. In this paper, we address the question of how social recommendation systems affect the occurrence of malicious URLs on Facebook, based on the assumption that there are no differences among recommendation systems in terms of delivering either legitimate or harmful information to users. Next, we use temporal features to build a prediction framework with &gt;75% accuracy to predict increases in certain user group behaviors. Our effort involves the demarcation of URL classes, from malicious URLs viewed as causing significant damage to annoying spam messages and advertisements. We offer this analysis to better understand OSN user sensors reactions to various categories of malicious URLs in order to mitigate their effects.

Natural Disaster Resilience Approach (NDRA) to Online Social Networks

Numerous disasters impact people around the world, including floods, cyclones, tsunamis and so on. Disaster resilience (recovery) is a realtime challenge. The research goal of the proposed Natural Disaster Resilience Approach (NDRA) is to fasten and automate disaster resilience within the affected region. Prioritization for saving lives must be provided to the region most affected. Currently almost all people use the Online Social Networks (OSNs) to share their daily activities. Actually only their friends in OSN can note the photos posted by OSN users in the disaster-affected region and in turn can support the friends in need. Our research goal is to conduct speedier and prioritized disaster resilience processes by directly addressing honest disaster help requests from images posted by OSN users. To achieve the above goal, we suggest an approach known as the Natural Disaster Resilience Approach (NDRA). The NDRA is a three-tiered framework: Tier-1: sybil(malicious) user avoidance engine: prevents fake OSN user profile creation; Tier-2: ‘D’- attributed image classifier: determines the originality of the images posted and Tier-3: sybil user prediction: uses an Advanced Sybil Node Prediction Algorithm (ASYNPA) to check the authenticity of the user and priorities for faster resilience requests. In NDRA we use Advogato dataset with 6541 users and 51,127 edges. Ultimately, the comparison is only made between the proposed ASYNPA tier-3 and the existing VoteTrust algorithm, and the graph is plotted against the False Positive (FP) rate, taking into account the precision and recall metrics. Around 99.84% of the expected sybils were confirmed in ASYNPA, which is 3.49% higher than VoteTrust.

Privacy preservation auction in a dynamic social network

SummaryThe growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent methods even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers' information has not known by the seller, current mechanism still cannot preserve the information such as prices. In this paper, we propose a novel mechanism which modeled the auction process in online social network and preserved users' privacy by using differential privacy mechanism. Our mechanism can successfully process the auction and at the same time preserve clients' price information from neighbors. We achieved these by adding Laplace noise for its valuation and the number of valuation seller received in the auction process. We also formulate this mechanism on the real network to show the feasibility and effective of the proposed mechanism.

Defensive Modeling of Fake News Through Online Social Networks

Online social networks (OSNs) have become an integral mode of communication among people and even nonhuman scenarios can also be integrated into OSNs. The ever-growing rise in the popularity of OSNs can be attributed to the rapid growth of Internet technology. OSN becomes the easiest way to broadcast media (news/content) over the Internet. In the wake of emerging technologies, there is dire need to develop methodologies, which can minimize the spread of fake messages or rumors that can harm society in any manner. In this article, a model is proposed to investigate the propagation of such messages currently coined as fake news. The proposed model describes how misinformation gets disseminated among groups with the influence of different misinformation refuting measures. With the onset of the novel coronavirus-19 pandemic, dubbed COVID-19, the propagation of fake news related to the pandemic is higher than ever. In this article, we aim to develop a model that will be able to detect and eliminate fake news from OSNs and help ease some OSN users stress regarding the pandemic. A system of differential equations is used to formulate the model. Its stability and equilibrium are also thoroughly analyzed. The basic reproduction number ( $R_{0}$ ) is obtained which is a significant parameter for the analysis of message spreading in the OSNs. If the value of $R_{0}$ is less than one ( $R_{0} ), then fake message spreading in the online network will not be prominent, otherwise if $R_{0}> 1$ the rumor will persist in the OSN. Real-world trends of misinformation spreading in OSNs are discussed. In addition, the model discusses the controlling mechanism for untrusted message propagation. The proposed model has also been validated through extensive simulation and experimentation.