Online Social Networks Users Research Articles

A nature - inspired approach based on Forest Fire model for modeling rumor propagation in social networks

Rumor propagation is a serious menace faced by online social network users. Modeling rumor propagation helps analyze the diffusion pattern of rumors in social networks and thereby limit the spread of rumors to a considerable extent. There are several factors which affect the extensive spread of rumors in social networks, but the identification of prime features is crucial in modeling the spread of rumors in a faster and more efficient way. In this paper, we propose a novel nature - inspired approach that focuses on the identification of best features to model the diffusion of rumors via online social networks. The proposed approach maps the spread of rumors in social networks to the spread of wildfire in a forest, and the prominent features for modeling rumor dissemination are determined by analyzing the main factors which contribute to the rapid spread of forest fire. We have developed a novel nature - inspired algorithm based on the Forest Fire model that utilizes the identified prominent features to calculate the probability of a node to share a rumor, and it measures the extent of rumor spread across a network by detecting the rumor - affected nodes in the network. The proposed algorithm also serves to examine the circulation path of rumors, the nodes which shared the rumor, and identify the nodes which played a major role in the rumor dissemination process. We evaluated the performance of the proposed method using two datasets obtained from Twitter, and the experimental results illustrate the efficiency of our proposed method and selected features. We also present a rumor propagation graph which aids in the analysis of the rumor diffusion pattern and discovers the key spreaders who are involved in the rumor dispersal. Furthermore, we also provide a feature - level comparison with the various existing approaches for rumor modeling to show how effectively the proposed approach maps the forest fire spread with the problem of rumor diffusion.

Ranking Authors in an Academic Network Using Social Network Measures

Online social networks are widely used platforms that enable people to connect with each other. These social media channels provide an active communication platform for people, and they have opened new venues of research for the academic world and business. One of these research areas is measuring the influential users in online social networks; and the same is true for academic networks where finding influential authors is an area of interest. In an academic network, citation count, h-index and their variations are used to find top authors. In this article, we propose the adoption of established social network measures, including centrality and prestige, in an academic network to compute the rank of authors. For the empirical analysis, the widely-used dataset of the Digital Bibliography and Library Project (DBLP) is exploited in this research, and the micro-level properties of the network formed in the DBLP co-authorship network are studied. Afterwards, the results are computed using social network measures and evaluated using the standard ranking performance evaluation measures, including Kendall correlation, Overlapping Similarlity (OSim) and Spearman rank-order correlation. The results reveal that the centrality measures are significantly correlated with the citation count and h-index. Consequently, social network measures have potential to be used in an academic network to rank the authors.

Understanding Cross-Site Linking in Online Social Networks

As a result of the blooming of online social networks (OSNs), a user often holds accounts on multiple sites. In this article, we study the emerging “cross-site linking” function available on mainstream OSN services including Foursquare, Quora, and Pinterest. We first conduct a data-driven analysis on crawled profiles and social connections of all 61.39 million Foursquare users to obtain a thorough understanding of this function. Our analysis has shown that the cross-site linking function is adopted by 57.10% of all Foursquare users, and the users who have enabled this function are more active than others. We also find that the enablement of cross-site linking might lead to privacy risks. Based on cross-site links between Foursquare and external OSN sites, we formulate cross-site information aggregation as a problem that uses cross-site links to stitch together site-local information fields for OSN users. Using large datasets collected from Foursquare, Facebook, and Twitter, we demonstrate the usefulness and the challenges of cross-site information aggregation. In addition to the measurements, we carry out a survey collecting detailed user feedback on cross-site linking. This survey studies why people choose to or not to enable cross-site linking, as well as the motivation and concerns of enabling this function.

Do personality characteristics explain the associations between self-esteem and online social networking behaviour?

The relationships between online social networking (OSN) behaviour and users' self-esteem are as important as well as ambiguous: Both positive and negative self-esteem can encourage users to engage in OSNs. This work examined whether personality traits and attitudes toward traits can explain this controversy. Data from 830 users of Vk.com, a Russian OSN were analysed. I hypothesised that extraversion and attitudes toward extraversion eliminated correlations between positive self-esteem and users' popularity (the number of friends and likes). In contrast, neuroticism and attitudes toward neuroticism failed to eliminate a negative correlation between self-esteem and an indicator of users' self-validation (the number of impersonal avatars). This association also remained significant when conscientiousness as well as negative attitudes toward conscientiousness and agreeableness were controlled. However, self-esteem did not correlate with the two other self-validation indicators―the number of posts and portraits. This study casts doubt on the possibility of direct associations between positive self-esteem and users' popularity beyond such factors as extraversion. Nevertheless, it lends partial support to the association between negative self-esteem and users' self-validation such as the use of impersonal avatars even when other personality characteristics are considered.

Community-Centric Brokerage-Aware Access Control for Online Social Networks

Widespread use and global outreach of Online Social Networks (OSNs) has made it indispensable for everyone to be a part of this global community. Users of OSNs want to share and disseminate some information to the general public, on the other hand, they also want to keep certain information hidden or restricted to a few people. An OSN allows users to join different groups based on their interests, however, some irrelevant people may also join these groups. This brings forth the challenge of privacy to OSNs. Therefore, an effective access control system is highly desirable that can manage the access control of incoming and outgoing users of a group in OSNs. This study deals with privacy issues of online communities and aims to support access control in community-based OSNs. We propose a Community-Centric Brokerage-Aware Access Control (CBAC) model that utilizes important concepts from Social Network Analysis (SNA) which are brokerage, one-to-many relationships, temporary relationships, as well as from emerging access control models such as attribute-based and trust-based access control along with decentralized policies. User attributes can play a vital role in detecting communities in a graph. For community detection, we propose Attribute-Based Community Detection (ABCD) algorithm and compare its results with Louvain, Newman’s Eigenvector, and Clauset algorithms. The results of the ABCD algorithm outperform other methods in terms of modularity and the number of communities for large and dense networks. The contribution of this study is to propose a CBAC model, its mathematical modeling and the ABCD algorithm for community detection. The performance of the CBAC model was evaluated in terms of grants, denials, and response time. The performance of the proposed the CBAC model is compared with the Role-based Access Control (RBAC) model and the Team-based Access Control (TMAC) model. The CBAC model offers the best data sharing capabilities and provides comparatively reasonable response time.

For whom is social-network usage associated with anxiety? The moderating role of neural working-memory filtering of Facebook information.

Is Facebook usage bad for mental health? Existing studies provide mixed results, and direct evidence for neural underlying moderators is lacking. We suggest that being able to filter social-network information from accessing working memory is essential to preserve limited cognitive resources to pursue relevant goals. Accordingly, among individuals with impaired neural social-network filtering ability, enhanced social-network usage would be associated with negative mental health. Specifically, participants performed a novel electrophysiological paradigm that isolates neural Facebook filtering ability. Participants' actual Facebook behavior and anxious symptomatology were assessed. Confirming evidence showed that enhanced Facebook usage was associated with anxious symptoms among individuals with impaired neural Facebook filtering ability. Although less robust and tentative, additional suggestive evidence indicated that this specific Facebook filtering impairment was not better explained by a general filtering deficit. These results involving a neural social-network filtering moderator, may help understand for whom increased online social-network usage is associated with negative mental health.

A Privacy-Enhanced Friending Approach for Users on Multiple Online Social Networks

Online social network users share their information in different social sites to establish connections with individuals with whom they want to be a friend. While users share all their information to connect to other individuals, they need to hide the information that can bring about privacy risks for them. As user participation in social networking sites rises, the possibility of sharing information with unknown users increases, and the probability of privacy breaches for the user mounts. This work addresses the challenges of sharing information in a safe manner with unknown individuals. Currently, there are a number of available methods for preserving privacy in order to friending (the act of adding someone as a friend), but they only consider a single source of data and are more focused on users’ security rather than privacy. Consequently, a privacy-preserving friending mechanism should be considered for information shared in multiple online social network sites. In this paper, we propose a new privacy-preserving friending method that helps users decide what to share with other individuals with the reduced risk of being exploited or re-identified. In this regard, the first step is to calculate the sensitivity score for individuals using Bernstein’s polynomial theorem to understand what sort of information can influence a user’s privacy. Next, a new model is applied to anonymise the data of users who participate in multiple social networks. Anonymisation helps to understand to what extent a piece of information can be shared, which allows information sharing with reduced risks in privacy. Evaluation indicates that measuring the sensitivity of information besides anonymisation provides a more accurate outcome for the purpose of friending, in a computationally efficient manner.

Impact of Content Popularity on Information Coverage in Online Social Networks

Online Social Networks (OSNs) have become an efficient media to disseminate content like news and advertisements. In this paper, we are interested in investigating the open problem of how does the content popularity influence the information coverage exactly. Specifically, we assume that each user has $q$ friends and forwards the received content to each of his friends with the forwarding probability $p$ , where $p$ is only determined by the content popularity. Based on a geography-aware social model leveraging the forwarding behavior of OSNs users, we explore the exact relationship between the content popularity and the information coverage by the method of network partitions. Then, we mainly have the following observations: 1) The information coverage is only related to $p$ and $q$ ; 2) In order to disseminate the content to a constant portion of all users within the network, the forwarding probability $p$ should be no less than $\Theta \left(\frac{1}{q}\right)$ . Extensive evaluations on both the generated data and the real OSN topology validate our observations.

EXPLORING DEPENDENCIES ACROSS MULTIPLE ONLINE SOCIAL NETWORKS

About 35% of online social network users belong to more than one networking platform such as Facebook, Instagram, and Twitter. The interplay among these various social networking platforms implies that several sources may induce interrelationships among the platforms. To understand such relationships, the authors propose an integrated visit model that accommodates communicating activities across social network platforms and test the model using data from two social network games that have considerable overlap among network members. The model captures various sources of dependencies across network platforms, including coincidental visitation, correlated heterogeneity and experience spillover across network platforms. More important, the model discovers a new source of dependencies that stems from communications with common network members overlapping in different network platforms. The model finds that the spillover effects are asymmetric across networks. The simulation study provides managerial implications for organizations attempting to target valuable users and allocate resources across multiple network platforms.

Influence maximization in large social networks: Heuristics, models and parameters

Online social networks play a major role not only in socio psychological front, but also in the economic aspect. The way social network serves as a platform of information spread, has attracted a wide range of applications at its doorstep. In recent years, lot of efforts are directed to use the phenomenon of vast spread of information, via social networks, in various applications, ranging from poll analysis, product marketing, identifying influential users and so on. One such application that has gained research attention is the influence maximization problem. The influence maximization problem aims to fetch the top influential users in the social networks. The aim of the paper is to provide a comprehensive analysis on the state of art approaches towards identifying influential users. In this review, we discuss various challenges and approaches to identify influential users in online social networks. This review concludes with future research direction, helping researchers to bring possible improvements to the existing body of work.

A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining

The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD) , that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3,126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs.

Deep learning-based personality recognition from text posts of online social networks

Personality is an important psychological construct accounting for individual differences in people. Computational personality recognition from online social networks is gaining increased research attention in recent years. However, the majority of existing methodologies mainly focused on human-designed shallow statistical features and didn’t make full use of the rich semantic information in user-generated texts, while those texts are exactly the most direct way for people to translate their internal thoughts and emotions into a form that others can understand. This paper proposes a deep learning-based approach for personality recognition from text posts of online social network users. We first utilize a hierarchical deep neural network composed of our newly designed AttRCNN structure and a variant of the Inception structure to learn the deep semantic features of each user’s text posts. Then we concatenate the deep semantic features with the statistical linguistic features obtained directly from the text posts, and feed them into traditional regression algorithms to predict the real-valued Big Five personality scores. Experimental results show that the deep semantic feature vectors learned from our proposed neural network are more effective than the other four kinds of non-trivial baseline features; the approach that utilizes the concatenation of our deep semantic features and the statistical linguistic features as the input of the gradient boosting regression algorithm achieves the lowest average prediction error among all the approaches tested by us.

Profit Maximization for Viral Marketing in Online Social Networks: Algorithms and Analysis

Information can be disseminated widely and rapidly through Online Social Networks (OSNs) with “word-of-mouth” effects. Viral marketing is such a typical application in which new products or commercial activities are advertised by some seed users in OSNs to other users in a cascading manner. The selection of initial seed users yields a tradeoff between the expense and reward of viral marketing. In this paper, we define a general profit metric that naturally combines the benefit of influence spread with the cost of seed selection in viral marketing. We carry out a comprehensive study on finding a set of seed nodes to maximize the profit of viral marketing. We show that the profit metric is significantly different from the influence metric in that it is no longer monotone. This characteristic differentiates the profit maximization problem from the traditional influence maximization problem. We develop new seed selection algorithms for profit maximization with strong approximation guarantees. We also derive several upper bounds to benchmark the practical performance of an algorithm on any specific problem instance. Experimental evaluations with real OSN datasets demonstrate the effectiveness of our algorithms and techniques.

Determination of Friendship Intensity between Online Social Network Users Based on their Interaction

Online social networks (OSN) are one of the most popular forms of modern communication and among the best known is Facebook. Information about the connection between users on the OSN is often very scarce. It's only known if users are connected, while the intensity of the connection is unknown. The aim of the research described was to determine and quantify friendship intensity between OSN users based on analysis of their interaction. We built a mathematical model, which uses: supervised machine learning algorithm Random Forest, experimentally determined importance of communication parameters and coefficients for every interaction parameter based on answers of research conducted through a survey. Taking user opinion into consideration while designing a model for calculation of friendship intensity is a novel approach in opposition to previous researches from literature. Accuracy of the proposed model was verified on the example of determining a better friend in the offered pair.

Discovering Homophily in Online Social Networks

During the last ten years, Online Social Networks (OSNs) have increased their popularity by becoming part of the real life of users. Despite their tremendous widespread, OSNs have introduced several privacy issues as a consequence of the nature of the information involved in these services. Indeed, the huge amount of private information produced by users of current OSNs expose the users to a number of risks. The analysis of the users’ similarity in OSNs is attracting the attention of researchers because of its implications on privacy and social marketing. In particular, the homophily between users could be used to reveal important characteristics that users would like to keep hidden, hence violating the privacy of OSNs’ users. Homophily has been well studied in existing sociology literature, however, it is not easily extensible in OSNs due to the lack of real datasets. In this paper, we propose an analysis of similarity of social profiles in terms of movie preferences. Results reveal the presence of homophily between users and its dependence from the tie strength. Moreover, we show that it is possible to profile a user (in our case by considering the age attribute) by exploiting movie preferences.

Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks

Billions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity.

Knowledge‐based approaches for identity management in online social networks

When we meet a new person, we start by introducing ourselves. We share our names, and other information about our jobs, cities, family status, and so on. This is how socializing and social interactions can start: we first need to identify each other. Identification is a cornerstone in establishing social contacts. We identify ourselves and others by a set of civil (e.g., name, nationality, ID number, gender) and social (e.g., music taste, hobbies, religion) characteristics. This seamlessly carried out identification process in face‐to‐face interactions is challenged in the virtual realms of socializing, such as in online social network (OSN) platforms. New identities (i.e., online profiles) could be created without being subject to any level of verification, making it easy to create fake information and forge fake identities. This has led to a massive proliferation of accounts that represent fake identities (i.e., not mapping to physically existing entities), and that poison the online socializing environment with fake information and malicious behavior (e.g., child abuse, information stealing). Within this milieu, users in OSNs are left unarmed against the challenging task of identifying the real person behind the screen. OSN providers and research bodies have dedicated considerable effort to the study of the behavior and features of fake OSN identities, trying to find ways to detect them. Some other research initiatives have explored possible techniques to enable identity validation in OSNs. Both kinds of approach rely on extracting knowledge from the OSN, and exploiting it to achieve identification management in their realms. We provide a review of the most prominent works in the literature. We define the problem, provide a taxonomy of related attacks, and discuss the available solutions and approaches for knowledge‐based identity management in OSNs.This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Application Areas> Internet and Web‐Based Applications Application Areas> Society and Culture

Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets

Social interactions take place in environments that influence people’s behaviours and perceptions. Nowadays, the users of Online Social Network (OSN) generate a massive amount of content based on social interactions. However, OSNs wide popularity and ease of access created a perfect scenario to practice malicious activities, compromising their reliability. To detect automatic information broadcast in OSN, we developed a wavelet-based model that classifies users as being human, legitimate robot, or malicious robot, as a result of spectral patterns obtained from users’ textual content. We create the feature vector from the Discrete Wavelet Transform along with a weighting scheme called Lexicon-based Coefficient Attenuation. In particular, we induce a classification model using the Random Forest algorithm over two real Twitter datasets. The corresponding results show the developed model achieved an average accuracy of 94.47% considering two different scenarios: single theme and miscellaneous one.

Identifying trusted similar users using stochastic model and next-closure based knowledge model in online social networks

The social network is a medium where people of different religions, races, languages, hobbies, etc. come together and exchange information and knowledge without any social restrictions. Though they are divergent in many areas of their existence, they are all brought together by their interests. They tend to have closer communication only with those who have similar interests and trust. So identifying users of similar interest and evaluating their trustworthiness is mandatory in an online social network (OSN). As the user behavior in social network is dynamic, the stochastic process which evolves the randomness over the period is employed to compute the closeness between users in OSN. The current work proposes to identify the similar interested groups, based on the knowledge-based model through the provenance factors using formal concept analysis (FCA) and Jaccard index. The probability of closeness is derived using stochastic process reflecting the trustability of the user with respect to the different type of interaction behavior such as like, comment and chat.

Information privacy in online social networks: Uses and gratification perspective

This research investigates privacy attitudes and behaviors in online social networks (OSN). For this purpose, two separate studies were conducted. Study 1 employed the theory of uses and gratifications to identify the motives for using OSN. Surveying 918 OSN users and employing the content analysis method, the predominant motives for using OSN were identified as: 1-relationship maintenance; 2-entertainment; 3-relationship building; and 4-information seeking. Study 2, inspired by the framework of Smith, Dinev, & Xu’s (2011), investigates both the antecedents to and outcomes of privacy concerns. Survey data were collected from 521 OSN users. The results of regression analysis suggested that the motives for OSN use do not serve as antecedents of privacy concerns. Minimal impact of the motives on privacy concerns indicated that the degree of users’ concerns for privacy is not related to the reasons why people use OSN. The findings also showed that privacy concerns did not inhibit self-disclosure, whereas the motives fostered it. No dichotomy was observed between privacy concerns and the use of privacy protective measures. Overall, the findings regarding the outcomes of privacy concerns indicated that privacy concerns align with privacy behaviors; therefore a privacy paradox or inconsistency between privacy attitudes and behavior was not supported.