User Linkage Across Anonymizd Social Networks

User profile data (for example, age and sex) is usually self-reported by users, so it is prone to human errors or biases. For example, a user can be reluctant to provide a company with private information such as his/her actual age upon subscription, thus the user either does not fill in the age column or put in some random numbers to avoid unwanted privacy intrusion. However, inaccurate or uncertain user profile data undermines the integrity of a company's marketing or operational intelligence. Targeting customers based on uncertain user profile data will not be as effective as targeting customers based on accurate user profile data. Thus companies perform preprocessing on user profile data as part of effort to maintain the accuracy of their user profile data. This paper presents a study of preprocessing uncertain user profile data based on a proposed simple collaborative learning algorithm. We demonstrate that a user's accurate profile information can be inferred from profile information of the user's social network neighbors. Particularly, we address the issue of how a communication service company can verify whether a user's reported age is true or not. We implement a simple collaborative learning algorithm using mobile network data. The dataset contains anonymized user data from a large Korean mobile company, capturing 174,071 users' demographic profiles and their communication histories. To construct a mobile social network among users, we collect 3G voice call histories including 561,787 unique call receivers who belong to the same service carrier. Results reveal that the prediction accuracy of the proposed method based on voice network data is 97% which is very high compared to 53%, the best accuracy by among competing methods and indicates that our method effectively detects users with great discrepancy between self-reported age and actual age.

Users' addiction to online social networks is discovered to be highly correlated with their social connections in the networks. Dense social connections can effectively help online social networks retain their active users and improve the social network services. Therefore, it is of great importance to make a good prediction of the social links among users. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously. Formally, the social networks which share a number of common users are defined as the "aligned networks". With the information transferred from multiple aligned social networks, we can gain a more comprehensive knowledge about the social preferences of users in the pre-specified target network, which will benefit the social link prediction task greatly. However, when transferring the knowledge from other aligned source networks to the target network, there usually exists a shift in information distribution between different networks, namely domain difference. In this paper, we study the social link prediction problem of the target network, which is aligned with multiple social networks concurrently. To accommodate the domain difference issue, we project the features extracted for links from different aligned networks into a shared lower-dimensional feature space. Moreover, users in social networks usually tend to form communities and would only connect to a small number of users. Thus, the target network structure has both the low-rank and sparse properties. We propose a novel optimization framework, SLAMPRED, to combine both these two properties aforementioned of the target network and the information of multiple aligned networks with nice domain adaptations. Since the objective function is a linear combination of convex and concave functions involving nondifferentiable regularizers, we propose a novel optimization method to iteratively solve it. Extensive experiments have been done on real-world aligned social networks, and the experimental results demonstrate the effectiveness of the proposed model.

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

The purpose of research was to study the existing methods of determining the degree of cohesion of two users of social network, identifying their shortcomings and developing a new method. The research identified shortcomings of existing methods and proposed a new method for assessing the degree of cohesion of social network profiles based on open data from a social network. Under the degree of cohesion of users’ profiles is understood the probability of communication (interaction) of profile owners in real life, it is calculated for two users of the social network and expressed in percent. The work of the method is demonstrated on the example of the social network “In contact”. This method includes the sequence of the following stages: the first stage is data collection about users of the social network with API and the formation of tuples of users’ profile characteristics. A tuple of characteristics of social network profiles is the data, collected for each user, stored in a structured form. The next step is the analysis of the collected information. For each characteristic of the tuple of profiles, i.e. the possible element of interaction of users in the social network, the coefficient of cohesion by the characteristic is calculated. In addition, for each feature, its informativeness is calculated, i.e. how important is this or that feature in this social network. At the final stage, the results are generated, using the formula for the probability of communication between two users, derived during the investigation. Obtained as a result of the application of the method, the probability of communication between two users can be used to optimize the activities of the operative-search services and special bodies. In addition, the received degree of cohesion of two users can be interpreted as the probability of a channel of information leakage between them. The role of the user of the method can be any private or state organization that cares about the security of corporate data and commercial secrets, the operative-search service, as well as an organization that investigates cybercrimes and information security incidents.

Identification of influential users by neighbors in online social networks

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.

The use of social media, such as social networking sites continues to grow in everyday life, along with their use in the workplace. So, a concern raised about how the personal use of social networking sites like Facebook, WhatsApp, Instagram, etc., at work affects employee job performance. Moreover, the purpose of this study is to explore the relationship between social networking usage and employee job performance through the mediating role of work–life balance. The quantitative methodology was employed in this study. Data was collected using a self-administered questionnaire adapted from a previously validated survey. The target population consisted of Gen-Y employees of the corporate sector in Chandigarh, Delhi, and Haryana (UTs/states) of India. Judgemental sampling was applied to collect data from 224 employees of the corporate sector. The study results indicated that time spent, frequency of using social networking sites has significant relationship with job performance and work–life balance of employees while work–life balance and employee job performance are significantly positively related. However, social networking use has a significant negative relationship with job performance and work–life balance. Additionally, work–life balance significantly mediates the relationship between social networking usage and job performance. Therefore, the study concluded that employees struggle to set boundaries since they cannot distinguish between work and family life due to the rise in technology and social media use. Therefore, managers and HR professionals need to consider the negative aspect of online social networking at work. Further, the implications for managers and organizations are discussed.

The appearance of social networks has been one of the most important events in the last decade. A social network is a network of interaction and communication whose nodes are its members and its edges are the relation between them. An outstanding concept in social networks is the concept of social influence which is of great significance. Social influence is the behavioral transformation in a person, caused by his/her relationship with other people, organizations and the network. Today, the concept of social influence is used in viral marketing. The aim of viral marketing is to find a small subset of influential users in the social networks for marketing a product. The aim of this paper is to offer an algorithm in order to choose expert and influential users in social networks. This algorithm uses leader discovery method in social networks for choosing expert and influential users. This method chooses those users as the expert and influential leaders of the social networks, who are influential among the other members and also those users that have enough specialty and knowledge about the marketing product. Consequently, the process of selecting expert and influential users for the algorithm would be quick and also the selected leaders are influential and expert enough to do marketing and advertising in the social network.

Using multimodalities, the field of biometric is receiving widespread acceptance for efficient communication between users in social network. Hence, suitable designing of authentication and access control is highly required to perform analysis of system in social network domain. In this work, a Proximal Spatial Vector and Affinity Coefficient (PSV-AC) framework is designed with the objective of ensuring authentic communication between users in social network using face and fingerprint. Initially, multimodal user biometric features with proximal feature pixel stored in spatial vector are gene encoded to obtain biometric identity keys. User authenticity is authorized by decoding gene encoded biometric features using biometric identity keys. User Affinity Coefficient is calculated via antecedent instances shared between different social users. The social network user’s affinity coefficient is ordered in a matrix. Matrix ranking is then performed to identify whether authentic user social communication happened in network. Experiments have been carried out using BioSecure datasets to measure the performance in terms of number of social users, social network authentication time, access control time, multimodal biometric feature template size, and true positive rate.

In recent years, social networks have shown diversity in function and applications. People begin to use multiple online social networks simultaneously for different demands. The ability to uncover a user’s latent topic and social network preference is critical for community detection, recommendation, and personalized service across social networks. Unfortunately, most current works focus on the single network, necessitating new technology and models to address this issue. This paper proposes a user preference discovery model on multiple social networks. Firstly, the global and local topic concepts are defined, then a latent semantic topic discovery method is used to obtain global and local topic word distributions, along with user topic and social network preferences. After that, the topic distribution characteristics of different social networks are examined, as well as the reasons why users choose one network over another to create a post. Next, a Gibbs sampling algorithm is adopted to obtain the model parameters. In the experiment, we collect data from Twitter, Instagram, and Tumblr websites to build a dataset of multiple social networks. Finally, we compare our research to previous works, and both qualitative and quantitative evaluation results have demonstrated the effectiveness.

Purpose As social networking services are popular, the need for recognizing reliable people has become the main concern. Quantifying the relationships between the users of social networks is also one of the important challenges with the increasing number of users. So, trust plays an important role in social networks in order to recognize trustworthy people. The purpose of this paper is to propose an approach to recognize trustworthy users and protect users from abuse by untrustable users. Design/methodology/approach In this paper, the authors suggest a method to measure the trust value between users of social networks based on call log histories such as abundance, novelty, and sincerity and quality of service (QoS) requirements such as accessibility, response ability, tend to respond, and agility. After that, the authors calculate error-hit, precision, and recall to evaluate the trust value. Findings The results indicate that the proposed approach has better performance in terms of error-hit, precision, and recall than FIFO, combined, QoS-based, and call log-based method. Originality/value In this paper, the trust issue in social networks is pointed out and a new approach to evaluate the trust value is proposed based on call log histories and QoS requirements.

One form of online misbehaviour which has deeply affected society with harmful consequences is known as cyberbullying. Cyberbullying can simply be defined as an intentional act that is conducted through digital technology to hurt someone. Cyberbullying is a widely covered topic in the social sciences. There are many studies in which the problem of cyberbullying has been introduced and its origins and consequences have been explored in detail. There are also studies which have investigated the intervention and prevention strategies and have proposed guidelines for parents and adults in this regard. However, studies on the technical dimensions of this topic are relatively rare. In this research the overall goal was to bridge the gap between social science approaches and technical solutions. In order to be able to suggest solutions that could contribute to minimizing the risk and impact of cyberbullying we have investigated the phenomenon of cyberbullying from different angles. We have thoroughly studied the origin of cyberbullying and its growth over time, as well as the role of technology in the emergence of this type of virtual behaviour and in the potential for reducing the extent of the social concern it raises. First we introduced a novel outlook towards the cyberbullying phenomenon. We looked into the gradual changes which have occurred in relationships and social communication with the emergence of the Internet. We argued that one should look at virtual environments as virtual communities, because the human needs projected on these environments, the relationships, human concerns and misbehaviour have the same nature as in real-life societies. Therefore, to make virtual communities safe, we need to take safety measures and precautions that are similar to the ones that are common in non-virtual communities. We derived the assumption that if cyberbullying is recognized and treated as a social problem and not just seen as some random mischief conducted by individuals with the use of technology, the methods for handling its consequences are likely to be more realistic, effective and comprehensive. This part of our study led to the conviction that for combating cyberbullying, behavioural and psychological studies, and the study of technical solutions should go hand in hand. One of the main limitations that we faced when we started our research was the lack of a comprehensive dataset for cyberbullying studies. We needed a dataset which included real instances of bullying incidents. Moreover, it was essential for our studies to also have the demographic information of the social media users as well as the history of their activities. We started our preliminary experiments using a dataset that was collected from MySpace forums. This dataset did not meet all the requirements for our experiment, namely in terms of size and sufficiency of information. Therefore we developed our own YouTube dataset, with the aim to encompass extensive information about the users and their activities as well as larger numbers of bullying comments. We collected information on user activities and posted textual comments as well as personal and demographic details of the users involved. Detecting a bullying comment or post at the earliest possible moment in time can substantially decrease the negative effects of cyberbullying incidents. We started our experiments by showing that besides the conventional features used for text mining methods such as sentiment analysis and specifically bullying detection, more personal features, in this experiment gender, can improve the accuracy of the detection models. As expected the models which were optimized accordingly resulted in a more accurate classification. The improved outcome motivated us to look into other personal features as well, such as age and the writing style of users. By adding more personal information, the previous classification results were outperformed and the detection accuracy enhanced even further. In the last experiment we made use of experts’ knowledge to identify potential bully users in social networks. To better understand and interpret the intentions underlying the online activities of users of social media, we decided to incorporate human reasoning and knowledge into a bulliness rating system by developing a Multi-Criteria Evaluation System. Moreover, to have more sources of information and to make use of the potential of both human and machine, we designed a hybrid approach, incorporating machine learning models on top of the expert system. The hybrid approach reached an optimum model which outperformed the results obtained from the machine learning models and the expert system individually. Our hybrid model illustrates the added value of integrating technical solutions with insights from the social sciences for the first time. As argued in this thesis, the integration of social studies into a software-enhanced monitoring workflow could pave the way towards the tackling of this kind of online misbehaviour. The ideas and algorithms proposed for fulfilling this purpose can be a stepping stone for future research in this direction. The work carried out is also a demonstration of the added value of frameworks for text categorization, sentiment mining and user profiling in applications addressing societal issues. This work can be viewed as a contribution to the more general societal challenge of increasing the level of cybersecurity, in particular for the younger generations of social network users. By turning the internet into a safer place for children, the chances increase that they will be able to benefit from the informational richness that it also offers.

The era of today’s world is based on multiple online social network such as twitter, Facebook, LinkedIn and many more. User’s use this online social network for commination in terms of text, audio, video, images, gif’s and so on which leads to enormous amount of unstructured data generation. Hence, it becomes inevitable to analyze these unstructured data into meaningful knowledge which can be applied to various applications such as link prediction, criminology, public health, recommendation system and many more. Most applications of social networks require user profile data to analyze the data. In this paper, we propose a graph based methodology to connect user profiles based on their attributes similarity and build a social network graph of connected users. The methodology is tested on LinkedIn data set and results are promising. The methodology addresses various issues associated with unstructured data analysis.

Social networks have been part of people's daily life and plenty of users have registered accounts in multiple social networks. Interconnections among multiple social networks add a multiplier effect to social applications when fully used. With the sharp expansion of network size, traditional standalone algorithms can no longer support computing on large scale networks while alternatively, distributed and parallel computing become a solution to utilize the data-intensive information hidden in multiple social networks. As such, synergistic partitioning, which takes the relationships among different networks into consideration and focuses on partitioning the same nodes of different networks into same partitions. With that, the partitions containing the same nodes can be assigned to the same server to improve the data locality and reduce communication overhead among servers, which are very important for distributed applications. To date, there have been limited studies on multiple large scale network partitioning due to three major challenges: 1) the need to consider relationships across multiple networks given the existence of intricate interactions, 2) the difficulty for standalone programs to utilize traditional partitioning methods, 3) the fact that to generate balanced partitions is NP-complete. In this paper, we propose a novel framework to partition multiple social networks synergistically. In particular, we apply a distributed multilevel k-way partitioning method to divide the first network into k partitions. Based on the given anchor nodes which exist in all the social networks and the partition results of the first network, using MapReduce, we then develop a modified distributed multilevel partitioning method to divide other networks. Extensive experiments on two real data sets demonstrate that our method can significantly outperform baseline independent-partitioning method in accuracy and scalability.

Information diffusion prediction is a crucial task for comprehending the dissemination process of information. Although this problem has received significant attention recently, most of the state-of-the-arts primarily focus on the modelling of information cascades, while neglecting the implicit social relations between users in the social network and failing to adequately model the interrelations between the user social network and information cascades. To tackle the aforementioned issues, in this work, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b>ual-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b>tate <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b>ypergraph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b>ontrastive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b>earning model (DSHCL). Specifically, we first propose to construct a social hypergraph based on the social network to capture the implicit social relations. Then, for capturing the cascade level correlations among users, we generate the dual-state (i.e., static and dynamic) user representations from the user social hypergraph and information cascades. Finally, we exploit contrastive learning to model the interplay between the social network and information cascades by discriminating the dual-state representations generated from them. We conduct an empirical assessment of DSHCL across four publicly available datasets, and the findings underscore the DSHCL's superiority and the efficacy of its components.