Modeling Evolutionary Dynamics of Lurking in Social Networks

Lurking is a complex user-behavioral phenomenon that occurs in all large-scale online communities and social networks. It generally refers to the behavior characterizing users that benefit from the information produced by others in the community without actively contributing back to the production of social content. The amount and evolution of lurkers may strongly affect an online social environment, therefore understanding the lurking dynamics and identifying strategies to curb this trend are relevant problems. In this regard, we introduce the Lurker Game, i.e., a model for analyzing the transitions from a lurking to a non-lurking (i.e., active) user role, and vice versa, in terms of evolutionary game theory. We evaluate the proposed Lurker Game by arranging agents on complex networks and analyzing the system evolution, seeking relations between the network topology and the final equilibrium of the game. Results suggest that the Lurker Game is suitable to model the lurking dynamics, showing how the adoption of rewarding mechanisms combined with the modeling of hypothetical heterogeneity of users' interests may lead users in an online community towards a cooperative behavior.Comment: 13 pages, 5 figures. Accepted at CompleNet 201

Network Alignment In Heterogeneous Social Networks

Online Social Networks (OSN) have numerous applications and an ever growing user base. This has led to users being a part of multiple social networks at the same time. Identifying a similar user from one social network on another social network will give in- formation about a user’s behavior on different platforms. It further helps in community detection and link prediction tasks. The process of identifying or aligning users in multiple networks is called Network Alignment. More the information we have about the nodes / users better the results of Network Alignment. Unlike other related work in this field that use features like location, timestamp, bag of words, our proposed solution to the Network Alignment problem primarily uses information that is easily available which is the topology of the given network. We look to improve the alignment results by using more information on users like username and profile image features. Experiments are performed to compare the proposed solution in both unsupervised and supervised setting

Find me if You Can: Aligning Users in Different Social Networks

Online Social Networks allow users to share experiences with friends and relatives, make announcements, find news and jobs, and more. Several have user bases that number in the hundred of millions and even billions. Very often many users belong to multiple social networks at the same time under possibly different user names. Identifying a user from one social network on another social network gives information about a user\u27s behavior on each platform, which in turn can help companies perform graph mining tasks, such as community detection and link prediction. The process of identifying or aligning users in multiple networks is called network alignment. These similar (or same) users on different networks are called anchor nodes and the edges between them are called anchor links. The network alignment problem aims at finding these anchor links. In this work we propose two supervised algorithms and one unsupervised algorithm using thresholds. All these algorithms use local structural graph features of users and some of them use additional information about the users. We present the performance of our models in various settings using experiments based on Foursquare-Twitter and Facebook-Twitter data (User Identity Linkage Dataset). We show that our approaches perform well even when we use the neighborhood of the users only, and the accuracy improves even more given additional information about a user, such as the username and the profile image. We further show that our best approaches perform better at the HR@1 task than unsupervised and semi-supervised factoid embedding approaches considered earlier for these datasets

COMMUNITY DETECTION AND INFLUENCE MAXIMIZATION IN ONLINE SOCIAL NETWORKS

The detecting and clustering of data and users into communities on the social web are important and complex issues in order to develop smart marketing models in changing and evolving social ecosystems. These marketing models are created by individual decision to purchase a product and are influenced by friends and acquaintances. This leads to novel marketing models, which view users as members of online social network communities, rather than the traditional view of marketing to individuals. This thesis starts by examining models that detect communities in online social networks. Then an enhanced approach to detect community which clusters similar nodes together is suggested. Social relationships play an important role in determining user behavior. For example, a user might purchase a product that his/her friend recently bought. Such a phenomenon is called social influence and is used to study how far the action of one user can affect the behaviors of others. Then an original metric used to compute the influential power of social network users based on logs of common actions in order to infer a probabilistic influence propagation model. Finally, a combined community detection algorithm and suggested influence propagation approach reveals a new influence maximization model by identifying and using the most influential users within their communities. In doing so, we employed a fuzzy logic based technique to determine the key users who drive this influence in their communities and diffuse a certain behavior. This original approach contrasts with previous influence propagation models, which did not use similarity opportunities among members of communities to maximize influence propagation. The performance results show that the model activates a higher number of overall nodes in contemporary social networks, starting from a smaller set of key users, as compared to existing landmark approaches which influence fewer nodes, yet employ a larger set of key users

User identification and community exploration via mining big personal data in online platforms

User-generated big data mining is vital important for large online platforms in terms of security, profits improvement, products recommendation and system management. Personal attributes recognition, user behavior prediction, user identification, and community detection are the most critical and interesting issues that remain as challenges in many real applications in terms of accuracy, efficiency and data security. For an online platform with tens of thousands of users, it is always vulnerable to malicious users who pose a threat to other innocent users and consume unnecessary resources, where accurate user identification is urgently required to prevent corresponding malicious attempts. Meanwhile, accurate prediction of user behavior will help large platforms provide satisfactory recommendations to users and efficiently allocate different amounts of resources to different users. In addition to individual identification, community exploration of large social networks that formed by online databases could also help managers gain knowledge of how a community evolves. And such large scale and diverse social networks can be used to validate network theories, which are previously developed from synthetic networks or small real networks. In this thesis, we study several specific cases to address some key challenges that remain in different types of large online platforms, such as user behavior prediction for cold-start users, privacy protection for user-generated data, and large scale and diverse social community analysis. In the first case, as an emerging business, online education has attracted tens of thousands users as it can provide diverse courses that can exactly satisfy whatever demands of the students. Due to the limitation of public school systems, many students pursue private supplementary tutoring for improving their academic performance. Similar to online shopping platform, online education system is also a user-product based service, where users usually have to select and purchase the courses that meet their demands. It is important to construct a course recommendation and user behavior prediction system based on user attributes or user-generated data. Item recommendation in current online shopping systems is usually based on the interactions between users and products, since most of the personal attributes are unnecessary for online shopping services, and users often provide false information during registration. Therefore, it is not possible to recommend items based on personal attributes by exploiting the similarity of attributes among users, such as education level, age, school, gender, etc. Different from most online shopping platforms, online education platforms have access to a large number of credible personal attributes since accurate personal information is important in education service, and user behaviors could be predicted with just user attribute. Moreover, previous works on learning individual attributes are based primarily on panel survey data, which ensures its credibility but lacks efficiency. Therefore, most works simply include hundreds or thousands of users in the study. With more than 200,000 anonymous K-12 students' 3-year learning data from one of the world's largest online extra-curricular education platforms, we uncover students' online learning behaviors and infer the impact of students' home location, family socioeconomic situation and attended school's reputation/rank on the students' private tutoring course participation and learning outcomes. Further analysis suggests that such impact may be largely attributed to the inequality of access to educational resources in different cities and the inequality in family socioeconomic status. Finally, we study the predictability of students' performance and behaviors using machine learning algorithms with different groups of features, showing students' online learning performance can be predicted based on personal attributes and user-generated data with MAE$<10\%$. As mentioned above, user attributes are usually fake information in most online platforms, and online platforms are usually vulnerable of malicious users. It is very important to identify the users or verify their attributes. Many researches have used user-generated mobile phone data (which includes sensitive information) to identify diverse user attributes, such as social economic status, ages, education level, professions, etc. Most of these approaches leverage original sensitive user data to build feature-rich models that take private information as input, such as exact locations, App usages and call detailed records. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g. GDPR). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the user attributes without privacy concern. Typically, identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Due to limited data protection of various popular online services in some countries such as taxi hailing or takeouts ordering, many users nowadays encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, bringing threats to the user individuals as well as the society. Additionally, more and more people suffer from excessive digital marketing and fraud phone calls because of personal information leakage. Therefore, a real time identification of unfamiliar caller is urgently needed. We explore the feasibility of user identification with privacy-preserved user-generated mobile, and we develop CPFinder, a system which implements automatic user identification callers on end devices. The system could mainly identify four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City shows that the CPFinder can achieve an accuracy of 75+\% for multi-class classification and 92.35+\% for binary classification. In addition to the mining of personal attributes and behaviors, the community mining of a large group of people based on online big data also attracts lots of attention due to the accessibility of large scale social network in online platforms. As one of the very important branch of social network, scientific collaboration network has been studied for decades as online big publication databases are easy to access and many user attribute are available. Academic collaborations become regular and the connections among researchers become closer due to the prosperity of globalized academic communications. It has been found that many computer science conferences are closed communities in terms of the acceptance of newcomers' papers, especially are the well-regarded conferences~\cite{cabot2018cs}. However, an in-depth study on the difference in the closeness and structural features of different conferences and what caused these differences is still missing. %Also, reviewing the strong and weak tie theories, there are multifaceted influences exerted by the combination of this two types of ties in different context. More analysis is needed to determine whether the network is closed or has other properties. We envision that social connections play an increasing role in the academic society and influence the paper selection process. The influences are not only restricted within visible links, but also extended to weak ties that connect two distanced node. Previous studies of coauthor networks did not adequately consider the central role of some authors in the publication venues, such as \ac{PC} chairs of the conferences. Such people could influence the evolutionary patterns of coauthor networks due to their authorities and trust for members to select accepted papers and their core positions in the community. Thus, in addition to the ratio of newcomers' papers it would be interesting if the PC chairs' relevant metrics could be quantified to measure the closure of a conference from the perspective of old authors' papers. Additionally, the analysis of the differences among different conferences in terms of the evolution of coauthor networks and degree of closeness may disclose the formation of closed communities. Therefore, we will introduce several different outcomes due to the various structural characteristics of several typical conferences. In this paper, using the DBLP dataset of computer science publications and a PC chair dataset, we show the evidence of the existence of strong and weak ties in coauthor networks and the PC chairs' influences are also confirmed to be related with the tie strength and network structural properties. Several PC chair relevant metrics based on coauthor networks are introduced to measure the closure and efficiency of a conference.2021-10-2

Detection of Advanced Bots in Smartphones through User Profiling

abstract: This thesis addresses the ever increasing threat of botnets in the smartphone domain and focuses on the Android platform and the botnets using Online Social Networks (OSNs) as Command and Control (C&C;) medium. With any botnet, C&C; is one of the components on which the survival of botnet depends. Individual bots use the C&C; channel to receive commands and send the data. This thesis develops active host based approach for identifying the presence of bot based on the anomalies in the usage patterns of the user before and after the bot is installed on the user smartphone and alerting the user to the presence of the bot. A profile is constructed for each user based on the regular web usage patterns (achieved by intercepting the http(s) traffic) and implementing machine learning techniques to continuously learn the user's behavior and changes in the behavior and all the while looking for any anomalies in the user behavior above a threshold which will cause the user to be notified of the anomalous traffic. A prototype bot which uses OSN s as C&C; channel is constructed and used for testing. Users are given smartphones(Nexus 4 and Galaxy Nexus) running Application proxy which intercepts http(s) traffic and relay it to a server which uses the traffic and constructs the model for a particular user and look for any signs of anomalies. This approach lays the groundwork for the future host-based counter measures for smartphone botnets using OSN s as C&C; channel.Dissertation/ThesisM.S. Computer Science 201

Privacy and Online Social Networks: A Systematic Literature Review of Concerns, Preservation, and Policies

Background: Social media usage is one of the most popular online activities, but with it comes privacy concerns due to how personal data are handled by these social networking sites. Prior literature aimed at identifying users’ privacy concerns as well as user behavior associated with privacy mitigation strategies and policies. However, OSN users continue to divulge private information online and privacy remains an issue. Accordingly, this review aims to present extant research on this topic, and to highlight any potential research gaps. Method: The paper presents a systematic literature review for the period 2006 - 2021, in which 33 full papers that explored privacy concerns in online social networks (OSN), users’ behavior associated with privacy preservation strategies and OSN privacy policies were examined. Results: The findings indicate that users are concerned about their identity being stolen, the disclosure of sensitive information by third-party applications and through data leakage and the degree of control users have over their data. Strategies such as encryption, authentication, and privacy settings configuration, can be used to address users’ concerns. Users generally do not leverage privacy settings available to them, or read the privacy policies, but will opt to share information based on the benefits to be derived from OSNs. Conclusion: OSN users have specific privacy concerns due primarily to the inherent way in which personal data are handled. Different preservation strategies are available to be used by OSN users. Policies are provided to inform users, however, these policies at times are difficult to read and understand, but studies show that there is no direct effect on the behavior of OSN users. Further research is needed to elucidate the correlation between the relative effectiveness of different privacy preservation strategies and the privacy concerns exhibited by users. Extending the research to comparatively assess different social media sites could help with better awareness of the true influence of privacy policies on user behavior

An Army of Me: Sockpuppets in Online Discussion Communities

In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as "I", and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.Comment: 26th International World Wide Web conference 2017 (WWW 2017