In this work, we study the privacy risk due to profile matching across online social networks (OSNs), in which anonymous profiles of OSN users are matched to their real identities using auxiliary information about them. We consider different attributes that are publicly shared by users. Such attributes include both strong identifiers such as user name and weak identifiers such as interest or sentiment variation between different posts of a user in different platforms. We study the effect of using different combinations of these attributes to profile matching in order to show the privacy threat in an extensive way. The proposed framework mainly relies on machine learning techniques and optimization algorithms. We evaluate the proposed framework on three datasets (Twitter - Foursquare, Google+ - Twitter, and Flickr) and show how profiles of the users in different OSNs can be matched with high probability by using the publicly shared attributes and/or the underlying graphical structure of the OSNs. We also show that the proposed framework notably provides higher precision values compared to state-of-the-art that relies on machine learning techniques. We believe that this work will be a valuable step to build a tool for the OSN users to understand their privacy risks due to their public sharings.
Today, Online Social Networks such as Facebook, LinkedIn and Twitter are the most popular platforms on the Internet, on which millions of users register to share personal information with their friends. A large amount of data, social links and statistics about users are collected by Online Social Networks services and they create big digital mines of various statistical data. Leakage of personal information is a significant concern for social network users. Besides information propagation, some new attacks on Online Social Networks such as Identity Clone attack (ICA) have been identified. ICA …
Online Social Networks (OSN) are a permanent presence in today's personal and professional lives of a huge segment of the population, with direct consequences to offline activities. Built on a foundation of trust-users connect to other users with common interests or overlapping personal trajectories-online social networks and the associated applications extract an unprecedented volume of personal information. Unsurprisingly, serious privacy and security risks emerged, positioning themselves along two main types of attacks: attacks that exploit the implicit trust embedded in declared social rel…
Online social networks (OSN) like Twitter or Facebook are popular and powerful since they allow reaching millions of users online. They are also a popular target for socialbot attacks. Without a deep understanding of the impact of such attacks, the potential of online social networks as an instrument for facilitating discourse or democratic processes is in jeopardy. In this extended abstract we present insights from a live lab experiment in which social bots aimed at manipulating the social graph of an online social network, in our case Twitter. We explored the link creation behavior between t…
In this study, we consider users' online communication rhythms (online social rhythms) as coupled oscillators in a complex social network. Users' rhythms may be entrained onto those of their friends, and macro-scale pattern of such rhythms can emerge. We investigated the entrainment in online social rhythms and long-range correlations of the rhythms using an avatar communication dataset. We indicated entrainment in online social rhythms to emerge if the strength of a new connection reaches a threshold. This entrainment spread via densely-connected clusters. Consequently, long-range correlation…
Online social networks such as Twitter and Facebook have gained tremendous popularity for information exchange. The availability of unprecedented amounts of digital data has accelerated research on information diffusion in online social networks. However, the mechanism of information spreading in online social networks remains elusive due to the complexity of social interactions and rapid change of online social networks. Much of prior work on information diffusion over online social networks has based on empirical and statistical approaches. The majority of dynamical models arising from infor…
Understanding of evolutionary mechanism of online social networks is greatly significant for the development of network science. However, present researches on evolutionary mechanism of online social networks are neither deep nor clear enough. In this study, we empirically showed the essential evolution characteristics of Renren online social network. From the perspective of Pareto wealth distribution and bidirectional preferential attachment, the origin of online social network evolution is analyzed and the evolution mechanism of online social networks is explained. Then a novel model is prop…
An increasing fraction of today social interactions occur using online social media as communication channels. Recent worldwide events, such as social movements in Spain or revolts in the Middle East, highlight their capacity to boost people coordination. Online networks display in general a rich internal structure where users can choose among different types and intensity of interactions. Despite of this, there are still open questions regarding the social value of online interactions. For example, the existence of users with millions of online friends sheds doubts on the relevance of these r…
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 factors considered in standard diagnostic criteria (questionnaire) cannot be observed from online social activity logs. Our approa…
Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are helpful for user characterization as online users may not always reveal their true identity or attributes. We especially focused on user attribute determination such as gender, age, etc.; user behavior analysis such as motives for deception; mental models that are indicators of user behavior; user categorization such as bots vs. humans; and entity matching on dif…
The proposal of Six Degrees of Separation makes people realize that the world is not as big as we imagined. Even if the world's population now exceeds 7 billion, two strangers can still get in touch through a limited intermediary. When online social networks have taken the world by storm, can people connect through shorter distances? This issue is worth thinking about. This paper describes Six Degrees of Separation and the limitations of this theory in practical applications. Combined with online social networks, the paper analyzes the development and change of the degrees of connection betwee…
Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Online hate speech is an archetypal example of such challenges. Despite its magnitude and scale, there is a significant gap in understanding the nature of hate speech on social media. In this paper, we provide the first of a kind systematic large scale measurement study of the main targets of hate speech in online social media. To do that, we gather traces fro…
The present study uses the Kohonen self organizing map (SOM) to represent the popularity patterns of Myspace music artists from their attributes on the platform and their position in the social network. The method is applied to cluster the profiles (the nodes of the social network) and the best friendship links (the edges). It shows that the SOM is an efficient tool to interpret the complex links between the audience and the influence of the musicians. It finally provides a robust classifier of the online social network behaviors.
In the past, online social networks (OSN) like Facebook and Twitter became powerful instruments for communication and networking. Unfortunately, they have also become a welcome target for socialbot attacks. Therefore, a deep understanding of the nature of such attacks is important to protect the Eco-System of OSNs. In this extended abstract we propose a categorization scheme of social bot attacks that aims at providing an overview of the state of the art of techniques in this emerging field. Finally, we demonstrate the usefulness of our categorization scheme by characterizing recent socialbot …
Online dating is an increasingly thriving business which boasts billion-dollar revenues and attracts users in the tens of millions. Notwithstanding its popularity, online dating is not impervious to worrisome trust and privacy concerns raised by the disclosure of potentially sensitive data as well as the exposure to self-reported (and thus potentially misrepresented) information. Nonetheless, little research has, thus far, focused on how to enhance privacy and trustworthiness. In this paper, we report on a series of semi-structured interviews involving 20 participants, and show that users are …
Despite the several advantages commonly attributed to social networks such as easiness and immediacy to communicate with acquaintances and friends, significant privacy threats provoked by unexperienced or even irresponsible users recklessly publishing sensitive material are also noticeable. Yet, a different, but equally hazardous privacy risk might arise from social networks profiling the online activity of their users based on the timestamp of the interactions between the former and the latter. In order to thwart this last type of commonly neglected attacks, this paper presents a novel, smart…
Hashkat (http://hashkat.org) is a free, open source, agent based simulation software package designed to simulate large-scale online social networks (e.g. Twitter, Facebook, LinkedIn, etc). It allows for dynamic agent generation, edge creation, and information propagation. The purpose of hashkat is to study the growth of online social networks and how information flows within them. Like real life online social networks, hashkat incorporates user relationships, information diffusion, and trending topics. Hashkat was implemented in C++, and was designed with extensibility in mind. The software i…
Online social networks have become incredibly popular in recent years, which prompts an increasing number of companies to promote their brands and products through social media. This paper presents an approach for identifying influential nodes in online social network for brand communication. We first construct a weighted network model for the users and their relationships extracted from the brand-related contents. We quantitatively measure the individual value of the nodes in the community from both the network structure and brand engagement aspects. Then an algorithm for identifying the infl…
The online communities available on the Web have shown to be significantly interactive and capable of collectively solving difficult tasks. Nevertheless, it is still a challenge to decide how a task should be dispatched through the network due to the high diversity of the communities and the dynamically changing expertise and social availability of their members. We introduce CrowdSTAR, a framework designed to route tasks across and within online crowds. CrowdSTAR indexes the topic-specific expertise and social features of the crowd contributors and then uses a routing algorithm, which suggest…
The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework where individuals can adopt different strategies of socialization. Our main result is that the spreading of self-protective behaviors to cope with hostile social environments can lead the economy to non-socially optimal stationary states.
A profile matching algorithm takes as input a user profile of one social network and returns, if existing, the profile of the same person in another social network. Such methods have immediate applications in Internet marketing, search, security, and a number of other domains, which is why this topic saw a recent surge in popularity. In this paper, we present a user identity resolution approach that uses minimal supervision and achieves a precision of 0.98 at a recall of 0.54. Furthermore, the method is computationally efficient and easily parallelizable. We show that the method can be used to…
We study the locally-defined social capital metric of Palasek (2013) for determining individuals' prestige within an online social network. From it we derive an equivalent global measure by considering random walks over the network itself. This result inspires a novel expression quantifying the strategic desirability of a potential social connection. We show in silico that ideal social neighbors tend to satisfy a "big fish in a small pond" criterion and that the distribution of neighbor-desirability throughout a network is governed by anti-homophily.
An online user joins multiple social networks in order to enjoy different services. On each joined social network, she creates an identity and constitutes its three major dimensions namely profile, content and connection network. She largely governs her identity formulation on any social network and therefore can manipulate multiple aspects of it. With no global identifier to mark her presence uniquely in the online domain, her online identities remain unlinked, isolated and difficult to search. Earlier research has explored the above mentioned dimensions, to search and link her multiple ident…
The study explores whether the use of Twitter in Massive Open Online Courses (MOOCs) promotes the interaction among learners. The social network analysis shows that instructors still play a very central role in the social media communication and the communication network between students shrinking over time. The mere use of social media fails to promote learner-learner interaction. More research is needed for understanding learner motivation and how instructional design can help increase their engagement and participation.
In online social network (OSN), understanding the factors bound to the role and strength of interaction(tie) are essential to model a wide variety of network-based applications. The recognition of these interactions can enhance the accuracy of link prediction, improve in ranking of dominants, reliability of recommendation and enhance targeted marketing as decision support system. In recent years, research interest on tie strength measures in OSN and its applications to diverse areas have increased, therefore it needs a comprehensive review covering tie strength estimation systematically. The o…
Anomalies in online social networks can signify irregular, and often illegal behaviour. Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, and as being labelled or unlabelled, and survey methods for detecting these different types of anomalies. We suggest…
This report contains results of an experimental study of the distribution of misinformation in online social networks (OSNs). We consider the classification of the topologies of OSNs and analyze the parameters identified in order to relate the topology of a real network with one of the classes. We propose an algorithm for conducting a search for the percolation cluster in the social graph.
Different measures have been proposed to predict whether individuals will adopt a new behavior in online social networks, given the influence produced by their neighbors. In this paper, we show one can achieve significant improvement over these standard measures, extending them to consider a pair of time constraints. These constraints provide a better proxy for social influence, showing a stronger correlation to the probability of influence as well as the ability to predict influence.
Many of today's most widely used computing applications utilize social networking features and allow users to connect, follow each other, share content, and comment on others' posts. However, despite the widespread adoption of these features, there is little understanding of the consequences that social networking has on user retention, engagement, and online as well as offline behavior. Here, we study how social networks influence user behavior in a physical activity tracking application. We analyze 791 million online and offline actions of 6 million users over the course of 5 years, and show…
Homophily, ranging from demographics to sentiments, breeds connections in social networks, either offline or online. However, with the prosperous growth of music streaming service, whether homophily exists in online music listening remains unclear. In this study, two online social networks of a same group of active users are established respectively in Netease Music and Weibo. Through presented multiple similarity measures, it is evidently demonstrated that homophily does exist in music listening of both online social networks. The unexpected music similarity in Weibo also implies that knowled…
User dynamics in online social networks have a significant impact on not only the online community but also real-world activities. As examples, we can mention explosive user dynamics triggered by social polarization, echo chamber phenomena, fake news, etc. Explosive user dynamics are frequently called online flaming. The wave equation-based model for online social networks (called the oscillation model) is a theoretical model proposed to describe user dynamics in online social networks. This model can be used to understand the relationship between explosive user dynamics and the structure of s…
Online social media (OSM) has a enormous influence in today's world. Some individuals view OSM as fertile ground for abuse and use it to disseminate misinformation and political propaganda, slander competitors, and spread spam. The crowdturfing industry employs large numbers of bots and human workers to manipulate OSM and misrepresent public opinion. The detection of online discussion topics manipulated by OSM \emph{abusers} is an emerging issue attracting significant attention. In this paper, we propose an approach for quantifying the authenticity of online discussions based on the similarity…
Although the effects of the social norm on mitigating misinformation are identified, scant knowledge exists about the patterns of social norm emergence, such as the patterns and variations of social tipping in online communities with diverse characteristics. Accordingly, this study investigates the features of social tipping in online communities and examines the correlations between the tipping features and characteristics of online communities. Taking the side effects of COVID-19 vaccination as the case topic, we first track the patterns of tipping features in 100 online communities, which a…
Social networks are rich source of data to analyze user habits in all aspects of life. User's behavior is decisive component of a health system in various countries. Promoting good behavior can improve the public health significantly. In this work, we develop a new model for social network analysis by using text analysis approach. We define each user reaction to global pandemic with analyzing his online behavior. Clustering a group of online users with similar habits, help to find how virus spread in different societies. Promoting the healthy life style in the high risk online users of social …
Social bots can affect online communication among humans. We study this phenomenon by focusing on #YaMeCanse, the most active protest hashtag in the history of Twitter in Mexico. Accounts using the hashtag are classified using the BotOrNot bot detection tool. Our preliminary analysis suggests that bots played a critical role in disrupting online communication about the protest movement.
Recent years have witnessed the explosion of online social networks (OSNs). They provide powerful IT-innovations for online social activities such as organizing contacts, publishing contents, and sharing interests between friends who may never meet before. As more and more people become the active users of online social networks, one may ponder questions such as: (1) Do OSNs indeed improve our sociability? (2) To what extent can we expand our offline social spectrum in OSNs? (3) Can we identify some interesting user behaviors in OSNs? Our work in this paper just aims to answer these interestin…
We analyze the problem of majority sentiment detection in Online Social Networks (OSN), and relate the detection error probability to the underlying graph of the OSN. Modeling the underlying social network as an Ising Markov random field prior based on a given graph, we show that in the case of the empty graph (independent sentiments) and the chain graph, the detection is always inaccurate, even when the number of users grow to infinity. In the case of the complete graph, the detection is inaccurate if the connection strength is below a certain critical value, while it is asymptotically accura…
This paper has been withdrawn by the author due to a crucial sign error in equation 1. With the advance of online social networks, there has been extensive research on how to spread influence in online social networks, and many algorithms and models have been proposed. However, many fundamental problems have also been overlooked. Among those, the most important problems are the incentive aspect and the privacy aspect (eg, nodes' relationships) of the influence propagation in online social networks. Bearing these defects in mind, and incorporating the powerful tool from differential privacy, we…
Guiding users to actively expanding their online social circles is one of the primary strategies for enhancing user participation and growing online social networks. In this paper, we study the active friending problem which aims at providing users with the strategy for methodically sending invitations to successfully build a friendship with target users. We consider the prominent linear threshold model for the friending process and formulate the active friending problem as an optimization problem. The key observation is the relationship between the active friending problem and the minimum sub…
Recent research has established both a theoretical basis and strong empirical evidence that effective social behavior plays a beneficial role in the maintenance of physical and psychological well-being of people. To test whether social behavior and well-being are also associated in online communities, we studied the correlations between the recovery of patients with mental disorders and their behaviors in online social media. As the source of the data related to the social behavior and progress of mental recovery, we used PatientsLikeMe (PLM), the world's first open-participation research plat…
Online social networks represent a main source of communication and information exchange in today's life. They facilitate exquisitely news sharing, knowledge elicitation, and forming groups of same interests. Researchers in the last two decades studied the growth dynamics of the online social networks extensively questing a clear understanding of the behavior of humans in online social networks that helps in many directions, like engineering better recommendation systems and attracting new members. However, not all of social networks achieved the desired growth, for example, online social netw…
Social networking services like Twitter have been playing an import role in people's daily life since it supports new ways of communicating effectively and sharing information. The advantages of these social network services enable them rapidly growing. However, the rise of social network services is leading to the increase of unwanted, disruptive information from spammers, malware discriminators, and other content polluters. Negative effects of social spammers do not only annoy users, but also lead to financial loss and privacy issues. There are two main challenges of spammer detection on Twi…
Online Social Network (OSN) is one of the most hottest services in the past years. It preserves the life of users and provides great potential for journalists, sociologists and business analysts. Crawling data from social network is a basic step for social network information analysis and processing. As the network becomes huge and information on the network updates faster than web pages, crawling is more difficult because of the limitations of band-width, politeness etiquette and computation power. To extract fresh information from social network efficiently and effectively, this paper presen…
An increasing number of today's social interactions occurs using online social media as communication channels. Some online social networks have become extremely popular in the last decade. They differ among themselves in the character of the service they provide to online users. For instance, Facebook can be seen mainly as a platform for keeping in touch with close friends and relatives, Twitter is used to propagate and receive news, LinkedIn facilitates the maintenance of professional contacts, Flickr gathers amateurs and professionals of photography, etc. Albeit different, all these online …
Looking from a global perspective, the landscape of online social networks is highly fragmented. A large number of online social networks have appeared, which can provide users with various types of services. Generally, the information available in these online social networks is of diverse categories, which can be represented as heterogeneous social networks (HSN) formally. Meanwhile, in such an age of online social media, users usually participate in multiple online social networks simultaneously to enjoy more social networks services, who can act as bridges connecting different networks tog…
Online social networks facilitate the diffusion of misinformation. Some theorists construe the problem of misinformation as a problem of knowledge, hence of ignorance. This assumption leads to solutions in which misinformation (false belief) is resisted by good information (true belief). We argue that information is better understood as gossip. We believe that gossip spreads as part of an economy of social capital that has a specific discursive grammar that mimics ordinary human gossip. But there are some critical differences. These differences have immense and divisive social and political ef…
Matching the profiles of a user across multiple online social networks brings opportunities for new services and applications as well as new insights on user online behavior, yet it raises serious privacy concerns. Prior literature has proposed methods to match profiles and showed that it is possible to do it accurately, but using evaluations that focused on sampled datasets only. In this paper, we study the extent to which we can reliably match profiles in practice, across real-world social networks, by exploiting public attributes, i.e., information users publicly provide about themselves. T…
Many decentralized online social networks (DOSNs) have been proposed due to an increase in awareness related to privacy and scalability issues in centralized social networks. Such decentralized networks transfer processing and storage functionalities from the service providers towards the end users. DOSNs require individualistic implementation for services, (i.e., search, information dissemination, storage, and publish/subscribe). However, many of these services mostly perform social queries, where OSN users are interested in accessing information of their friends. In our work, we design a soc…
In this paper, we consider how to maximize users' influence in Online Social Networks (OSNs) by exploiting social relationships only. Our first contribution is to extend to OSNs the model of Kempe et al. [1] on the propagation of information in a social network and to show that a greedy algorithm is a good approximation of the optimal algorithm that is NP-hard. However, the greedy algorithm requires global knowledge, which is hardly practical. Our second contribution is to show on simulations on the full Twitter social graph that simple and practical strategies perform close to the greedy algo…
Information flows are the result of a constant exchange in Online Social Networks (OSNs). OSN users create and share varying types of information in real-time throughout a day. Virality is introduced as a term to describe information that reaches a wide audience within a small time-frame. As a case, we measure propagation of information submitted in Reddit, identify different patterns and present a multi OSN diffusion analysis on Twitter, Facebook, and 2 hosting domains for images and multimedia, ImgUr and YouTube. Our results indicate that positive content is the most shared and presents the …
It is common practice nowadays to use multiple social networks for different social roles. Although this, these networks assume differences in content type, communications and style of speech. If we intend to understand human behaviour as a key-feature for recommender systems, banking risk assessments or sociological researches, this is better to achieve using a combination of the data from different social media. In this paper, we propose a new approach for user profiles matching across social media based on embeddings of publicly available users' face photos and conduct an experimental study…
How is online social media activity structured in the geographical space? Recent studies have shown that in spite of earlier visions about the "death of distance", physical proximity is still a major factor in social tie formation and maintenance in virtual social networks. Yet, it is unclear, what are the characteristics of the distance dependence in online social networks. In order to explore this issue the complete network of the former major Hungarian online social network is analyzed. We find that the distance dependence is weaker for the online social network ties than what was found ear…
The importance of modeling and analyzing Social Networks is a consequence of the success of Online Social Networks during last years. Several models of networks have been proposed, reflecting the different characteristics of Social Networks. Some of them fit better to model specific phenomena, such as the growth and the evolution of the Social Networks; others are more appropriate to capture the topological characteristics of the networks. Because these networks show unique and different properties and features, in this work we describe and exploit several models in order to capture the struct…
Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to dismantling strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory co…
The emergence and popularization of online social networks suddenly made available a large amount of data from social organization, interaction and human behavior. All this information opens new perspectives and challenges to the study of social systems, being of interest to many fields. Although most online social networks are recent (less than fifteen years old), a vast amount of scientific papers was already published on this topic, dealing with a broad range of analytical methods and applications. This work describes how computational researches have approached this subject and the methods…
Benford's Law states that the frequency of first digits of numbers in naturally occurring systems is not evenly distributed. Numbers beginning with a 1 occur roughly 30\% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. We consider social data from five major social networks: Facebook, Twitter, Google Plus, Pinterest, and Live Journal. We show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford'…
Social networks tend to disproportionally favor connections between individuals with either similar or dissimilar characteristics. This propensity, referred to as assortative mixing or homophily, is expressed as the correlation between attribute values of nearest neighbour vertices in a graph. Recent results indicate that beyond demographic features such as age, sex and race, even psychological states such as "loneliness" can be assortative in a social network. In spite of the increasing societal importance of online social networks it is unknown whether assortative mixing of psychological sta…
