Large-scale Online Social Networks Research Articles

Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs

Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation of sensitive attributes such as specific profiles becomes harder compared to these models. This problem can result in queries and subsequent learnings from social network profiles of specific individuals, which may be of personal, political or otherwise concern. Online social networks (OSNs) grapple with significant privacy challenges due to the extensive dimensions and vast quantities of data involved. This research fills the void in current privacy-preserving community detection methodologies, which face problems in computational complexity and memory usage in large-scale OSNs. The proposed framework seeks to bolster privacy preservation through a comprehensive multi-step process. Data filtering uses a blended data filter system to remove outliers and irrelevant data, thus enhancing the quality of the input data. Density-Oriented Clustering phase employs a density-oriented clustering model to identify communities, with each cluster representing a distinct community. Privacy Preservation component introduces a new privacy preservation technique for sensitive OSN attributes, surpassing existing k-anonymization methods. The developed density-based social network community detection model and its novel privacy-preserving scheme are evaluated using the datasets Yelp, Football, Zachary and Dolphin from the SNAP dataset. Experimental results on these datasets embed a comprehensive evaluation based on the order of each node and the graph-based networks, where each node is laden with weights as proximity values, indicating the semantic proximity between communities and individuals. The proposed framework employs the normalized mutual information (NMI), modularity (Q), Rand index and runtime measurements to demonstrate widespread advantages in the multi-dimensional functional space, including greater accuracy, cluster compatibility, and computational tractability over the existing prominent traditional models for OSNs.

Unveiling Qzone: A measurement study of a large-scale online social network

Nowadays, Online Social Network (OSN) is very important in maintaining relationships and disseminating information. Qzone, as one of the most popular OSNs in China, lacks detailed quantitative measurement and analysis. To the best of our knowledge, we are the first to conduct a large-scale measurement on Qzone. Firstly, we develop a dedicated Qzone Crawler System (Qzone-CS) and collect over a billion user and topology data. Subsequently, we measure user behavior and network topology characteristics by analyzing their posting and interactive behaviors. The results show that: 1) the temporal distribution of user behavior follows the periodic distribution, while user posting behavior follows the segmented power-law distribution. Among all the user behaviors on Qzone, the probability of posting Shuoshuo is the highest. Meanwhile, users prefer to comment on others’ blogs than to forward them. Unlike other OSNs, Qzone is a relatively closed system, which is not conducive to the dissemination of information. 2) Qzone is a small-world and scale-free community; the friendship network has higher clustering characteristic compared with the interactive network; out-degree in the interactive network has an obvious correlation with in-degree; and unlike other OSNs, Qzone is a heterogeneous network. Our results can help researchers better understand the user behavior and network topology characteristics of Qzone.

Public perception of electric vehicles on reddit over the past decade

Understanding public perception of electric vehicles (EVs) is imperative for increasing EV adoption, which can significantly reduce greenhouse gas (GHG) emissions, thereby mitigating climate change and global warming. While most existing research characterizes public perception of EV in the context of surveys, questionnaires, or interviews, our study leverages Reddit online social network (OSN) data to capture EV public perception at a much larger scale. We have collected 3,437,917 Reddit posts (including 274,979 submissions and 3,162,938 comments) between January 2011 and December 2020 relevant to EVs and analyzed them along several axes to understand how EVs are perceived by the public on Reddit through the following research questions: (1) What EV-related topics have been discussed by Reddit users? Whether/how Reddit users’ interest in different topics has changed during 2011–2020? (2) What sentiment do Reddit users hold towards EVs? Whether public sentiment on Reddit has shifted over the past 10 years? (3) Whether/how do various Reddit communities (i.e., subreddits) have different perceptions of EVs? Our analysis evinces the potential of utilizing a large-scale OSN dataset for demonstrating a much wider spectrum of topics that the public is interested in than previous studies show, reveals fringe communities including r/conspiracy have many (controversial) discussions on EVs, especially on the environmental impacts of EVs, and one political community (r/The_Donald) has similar patterns with fringe communities in both sentiment and topic aspects. By answering these research questions, we aim to develop a more comprehensive understanding of the public perception of EVs in the past decade.

A hybrid soft computing with big data analytics based protection and recovery strategy for security enhancement in large scale real world online social networks

In recent years, Online Social Networks (OSN) have become an integral part of human society. OSN has become an important and crucial mode of interaction among people around the world. For the past two decades, the need for data privacy security among the participants of an OSN has been challenged as technological advances have provided a lot of benefits and exposure to all active members sharing content in various online communities. Large amounts of data are usually stored on different network nodes, which can lead to malicious data security and privacy issues. When OSN users share any private content with the other participants of same network like with their friends and colleagues, their information may be inadvertently leaked to various other individuals to whom it is not supposed to be shared. Those individuals may include one's enemies, social robots, and users with fake credentials, spammers or data cutters. In addition, the spread of malicious through OSN can lead to massive damage to the network and privacy leakage. To overcome this problem, we propose a hybrid soft computing with big data analytics based protection and recovery strategy (HSBD-PR) for security enhancement in large scale OSNs. First, we introduce a modified teaching-learning with fish swarm optimization (MTL-FS) technique to classify the abnormal network users as five classes are susceptive, contagious, doubt, immune and recoverable users. Second, we fuse a block window optimization with Bayesian belief network (BWB2) for analyze the propagation mechanism of abnormal users in network. After that, deep belief neural network (DBNN) based protection and recovery strategy is developed to improve the immunity of abnormal users and reduce the count of it. Finally, the performance of proposed HSBD-PR approach evaluate by standard benchmark datasets is Enron email, Epinions and Facebook. The results of this proposed method are compared with the existing state-of-art methods in terms of different performance metrics are detection failure rate, detection success rate, marginal detection gain and detection accuracy respectively.

Detection of fickle trolls in large-scale online social networks

Online social networks have attracted billions of active users over the past decade. These systems play an integral role in the everyday life of many people around the world. As such, these platforms are also attractive for misinformation, hoaxes, and fake news campaigns which usually utilize social trolls and/or social bots for propagation. Detection of so-called social trolls in these platforms is challenging due to their large scale and dynamic nature where users’ data are generated and collected at the scale of multi-billion records per hour. In this paper, we focus on fickle trolls, i.e., a special type of trolling activity in which the trolls change their identity frequently to maximize their social relations. This kind of trolling activity may become irritating for the users and also may pose a serious threat to their privacy. To the best of our knowledge, this is the first work that introduces mechanisms to detect these trolls. In particular, we discuss and analyze troll detection mechanisms on different scales. We prove that the order of centralized single-machine detection algorithm is O(n^3) which is slow and impractical for early troll detection in large-scale social platforms comprising of billions of users. We also prove that the streaming approach where data is gradually fed to the system is not practical in many real-world scenarios. In light of such shortcomings, we then propose a massively parallel detection approach. Rigorous evaluations confirm that our proposed method is at least six times faster compared to conventional parallel approaches.

Diversity Preference-Aware Link Recommendation for Online Social Networks

Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recommend similar friends to a user but overlook the user's diversity preference, although social psychology theories suggest the criticality of diversity preference to link recommendation performance. In recommender systems, a field related to link recommendation, a number of diversification methods have been proposed to improve the diversity of recommended items. Nevertheless, diversity preference is distinct from diversity studied by diversification methods. To address these research gaps, we define and operationalize the concept of diversity preference for link recommendation and propose a new link recommendation problem: the diversity preference-aware link recommendation problem. We then analyze key properties of the new link recommendation problem and develop a novel link recommendation method to solve the problem. Using two large-scale online social network data sets, we conduct extensive empirical evaluations to demonstrate the superior performance of our method over representative diversification methods adapted for link recommendation as well as state-of-the-art link recommendation methods.

Discovering social media topics and patterns in the coronavirus and election era

PurposeThis study aims to understand the relationship between politics and pandemics in shaping the characteristics and themes of people’s Tweets during the US 2020 presidential election. Additionally, the purpose is to detect misinformation and extremism, not only to help online social networks (OSN) to target such content more rapidly but also to provide a close to real-time picture of trending topics, misinformation, and extremism flowing on OSN. This could help authorities to identify the intents behind them and find out how and when they should address such content.Design/methodology/approachThis study focuses on extracting and verifying knowledge from large-scale OSN data, at the intersection of the Coronavirus pandemic and the US 2020 presidential election. More specifically, this study makes manual, statistical and automatic inferences and extracts knowledge from over a million Tweets related to the two aforementioned major events. On the other hand, disinformation operations intensified in 2020 with the coincidence of the Coronavirus pandemic and presidential election. This study applies machine learning to detect misinformation and extreme opinions on OSN. Over one million Tweets have been collected by our server in real-time from the beginning of April 2020 to the end of January 2021, using six keywords, namely, Covid, Corona, Trump, Biden, Democrats and Republicans. These Tweets are inspected with regard to their topics, opinions, news, and political affiliation, along with misinformation and extremism.FindingsOur analyses showed that the majority of these Tweets concern death tolls, testing, mask, drugs, vaccine, and travel bans. The second concern among these Tweets is reopening the economy and schools, unemployment, and stimulus bills. The third concern is related to the Coronavirus pandemic’s impacts on politics, voting, and misinformation. This highlights the topics that US voters on Twitter were most concerned about during this time period, among the multitude of other topics that politicians and news media were reporting or discussing. Automatic classification of these Tweets using a long short-term memory network revealed that Tweets containing misinformation formed between 0.5% and 1.1% of Coronavirus-related Tweets every month and Tweets containing extreme opinions formed between 0.5% and 3.1% of them every month, with its pick in October 2020, coinciding with the US presidential election month.Originality/valueThe originality of this study lies in establishing a framework to collect, process, and classify OSN data to detect misinformation and extremism and to provide a close to real-time picture of trending topics, misinformation, and extremism flowing on OSN.

Socio-Spatial Properties of Online Location-Based Social Networks

The spatial structure of large-scale online social networks has been largely unaccessible due to the lack of available and accurate data about people’s location. However, with the recent surging popularity of location-based social services, data about the geographic position of users have been available for the first time, together with their online social connections. In this work we present a comprehensive study of the spatial properties of the social networks arising among users of three main popular online location-based services. We observe robust universal features across them: while all networks exhibit about 40% of links below 100 km, we further discover strong heterogeneity across users, with different characteristic spatial lengths of interaction across both their social ties and social triads. We provide evidence that mechanisms akin to gravity models may influence how these social connections are created over space. Our results constitute the first large-scale study to unravel the socio-spatial properties of online location-based social networks.

Ultra-efficient information detection on large-scale online social networks

As a powerful and low-cost instant information dissemination platform, large-scale online social networks (OSNs) play a pivotal role in shaping our modern information age. The efficient detection of wide-spreading information in OSNs is very important in many aspects including public opinion supervision, social governance, stock markets, counter-terrorism and presidential election. However, real-world OSNs have gigantic sizes and thus their full structural data are usually unavailable, making this problem extremely challenging. In this work, we illustrate the close mapping between efficient detection and optimal spreading from the perspective of network percolation theory. This analogy inspires us to propose a theory of using only limited local network information to select the optimal set of information sensors. Through extensive simulations on both synthetic and real-world networks, we find that for networks with theoretically infinite size, only a finite and small number of sensor nodes are needed to detect the global spreading information with almost certainty. Most importantly, we empirically confirm the utility of our theory on the largest micro blog in China by crawling almost the full Sina Weibo social network with 99,546,027 users in 2014 and the real spreading data of Weibo messages.

Recommender system: An analytical report on decision making for large scale online social networks

Social networking playing a very important role in communication, business, and sharing of information. During these activities, a huge amount of data it generates. Organizations need to analyze, process, and then find patterns to take the decision. A recommender system can analyze Online Social Network (OSN) and recommends that leads to help the organization to take the decision. Ever growing Size of OSNs required a more efficient recommender system that provides more accurate results. The study reviews the different design issues of the recommender system and compares them with the real data set. This study analyses the different recommender systems and compares them to identify challenges and future scope for the design of the recommender system.

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

The growing popularity of Online Social Networks (OSN) have prompted an increasing number of companies to promote their brands and products through social media. This paper presents a topological potential scheme for predicting influential nodes from large scale OSNs to support more intelligent brand communication. We first construct a weighted network model for the users and their relationships extracted from the brand-related content in OSNs. We quantitatively measure the individual value of the nodes from both the network structure and brand engagement aspects. Moreover, we have addressed the problem of influence decay along with information propagation in social networks and use the topological potential theory to evaluate the importance of the nodes by their individual values as well as the individual values of their surrounding nodes. The experimental results have shown that the proposed method is able to predict influential nodes in large-scale OSNs. We investigate the top-k influential nodes identified by our method in detail, which are quite different from those identified by using pure network structure or individual value. We can obtain an identification result with a higher ratio of verified users and user coverage by using our method compared to existing typical approaches.

Robust Size Estimation of Online Social Networks via Subgraph Sampling

Estimating the number of nodes in a large-scale online social network is a fundamental problem in the field of network science. However, it is observed that the performance of traditional RW (Random Walk)-based network size estimators is not stable enough. The relative error of estimated network size can be extremely high for social networks with high skewness of degree distribution, even when the sampling rate is over 50%. In this paper, we propose a robust network size estimation algorithm called Graser , which significantly improves the robustness of size estimation on different types of online social networks. To understand the problem, we first conduct an in-depth study to examine influential factors that lead to inaccuracy of traditional RW-based estimators. Our study identifies a few key factors (e.g., degree distribution) closely related to the inaccuracy of size estimation. Next, we design an efficient network size estimator called Graser, which can iteratively reduce the estimation error by subgraph sampling. We also perform theoretical analysis on the performance of Graser, and prove that our algorithm can guarantee to achieve a relative error that is much smaller than that of traditional RW-based estimators. Finally, we conduct extensive experiments using real datasets to evaluate the performance of our proposed algorithm and the results show that Graser performs much better than traditional RW-based estimators on different types of social networks, and can reduce the relative error of size estimation significantly even with an extremely low sampling rate.

A social network based approach to identify and rank influential nodes for smart city

Purpose In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes. Design/methodology/approach The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features. Findings The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines. Originality/value Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

QuickPoint: Efficiently Identifying Densest Sub-Graphs in Online Social Networks for Event Stream Dissemination

Efficient event stream dissemination is a challenging problem in large-scale Online Social Network (OSN) systems due to the costly inter-server communications caused by the per-user view data storage. To solve the problem, previous schemes mainly explore the structures of social graphs to reduce the inter-server traffic. Based on the observation of high cluster coefficients in OSNs, a state-of-the-art social piggyback scheme can save redundant messages by exploiting an intrinsic hub-structure in an OSN graph for message piggybacking. Essentially, finding the best hub-structure for piggybacking is equivalent to finding a variation of the densest sub-graph. The existing scheme computes the best hub-structure by iteratively removing the node with the minimum weighted degree. Such a scheme incurs a worst computation cost of $O(n^2)$ O ( n 2 ) , making it not scalable to large-scale OSN graphs. Using alternative hub-structure instead of the best hub-structure can speed up the piggyback assignment. However, they greatly sacrifice the communication efficiency of the assignment schedule. Different from the existing designs, in this work, we propose a QuickPoint algorithm, which removes a fraction of nodes in each iteration in finding the best hub-structure. We mathematically prove that QuickPoint converges in $O(log_an) (a>1)$ O ( l o g a n ) ( a > 1 ) iterations in finding the best hub-structure for efficient piggyback. We implement QuickPoint in parallel atop Pregel, a vertex-centric distributed graph processing platform. Comprehensive experiments using large-scale data from Twitter and Flickr show that our scheme is 38.8× more efficient compared to existing schemes.

Fast Representative Sampling in Large-Scale Online Social Networks

Online social networks (OSNs) have become important platforms for efficiently connecting people and promoting information dissemination, which is of great importance to our social life and society. However, due to privacy concerns, and access limitations, it is difficult to obtain the whole network of OSNs for analysis, so it is critical to have a representative subgraph. Yet due to the same reasons, we are in lack of the original network as the ground truth which poses great challenges on evaluating sampling methods on the performance on unbiasedness, let alone representativeness. Thus uniform sampling (UNI) [Gjoka et al. 2010] was proposed to obtain an unbiased nodal property distribution as of the original network to evaluate the degree of bias of other methods. Yet UNI sampling suffers from its low efficiency, and the representativeness and connectivity of the obtained subgraph, which is formed by the sampled nodes and connections between them, are rarely studied. We propose an adaptive UNI sampling (adpUNI) method to overcome previously mentioned disadvantages of UNI by dividing the userID space into several intervals, whose sampling probability adaptively changes based on its target rate. By adding its neighbors of the targeted node into the sample set (adpUNI+N), we can further improve the performance on sampling efficiency and obtain a more connective and representative subgraph. When applied to Sina Weibo and Twitter, our methods over-perform other classical methods on sampling efficiency, and always have a better performance on connectivity and representativeness than UNI sampling. And we also find that an unbiased sample doesn’t guarantee a more representative subgraph.

Efficient Coupling Diffusion of Positive and Negative Information in Online Social Networks

The increasing popularization of large-scale online social networks (OSNs) facilitates information sharing. Users are able to diffuse positive and negative information independently owing to the high openness of the OSNs. Due to the intervention of user’s emotions and social relationships, the positive and negative information diffusion exhibits a complicated dynamic coupling diffusion process, in which the negative information diffusion can cause social panic and confusion. However, prior works mainly focus on the diffusion of single type of information. To fill this gap, this paper aims to investigate the dynamic diffusion process of the positive and negative information and control the negative information diffusion timely. Specifically, we first establish a coupling diffusion model to characterize the dynamic diffusion process under the coexistence of positive and negative information, then derive the critical condition for the negative information diffusion and certify the stability of the diffusion model. Furthermore, we propose two collaborative control strategies to persuade and guide users to diffuse the positive information simultaneously. Then, the issue of minimizing the total system costs is transformed to an optimal control problem. Finally, we prove the existence and uniqueness of optimal control solutions and obtain the dynamic distribution of optimal control strategies over time to minimize system costs. The experimental results obtained from two real-world datasets verify the effectiveness of our model and the high efficiency of the collaborative control strategies.

A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks

Analysis of opinion dynamics in social networks plays an important role in today’s life. For predicting users’ political preference, it is particularly important to be able to analyze the dynamics of competing polar opinions, such as pro-Democrat vs. pro-Republican. While observing the evolution of polar opinions in a social network over time, can we tell when the network evolved abnormally? Furthermore, can we predict how the opinions of the users will change in the future? To answer such questions, it is insufficient to study individual user behavior, since opinions can spread beyond users’ ego-networks. Instead, we need to consider the opinion dynamics of all users simultaneously and capture the connection between the individuals’ behavior and the global evolution pattern of the social network. In this work, we introduce the Social Network Distance (SND)—a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen model of polar opinion dynamics. SND has a rich semantics of a transportation problem, yet, is computable in time linear in the number of users and, as such, is applicable to large-scale online social networks. In our experiments with synthetic and Twitter data, we demonstrate the utility of our distance measure for anomalous event detection. It achieves a true positive rate of 0.83, twice as high as that of alternatives. The same predictions presented in precision-recall space show that SND retains perfect precision for recall up to 0.2. Its precision then decreases while maintaining more than 2-fold improvement over alternatives for recall up to 0.95. When used for opinion prediction in Twitter data, SND’s accuracy is 75.6%, which is 7.5% higher than that of the next best method.

Social Networks and Health: New Developments in Diffusion, Online and Offline

The relationship between social networks and health encompasses everything from the flow of pathogens and information to the diffusion of beliefs and behaviors. This review addresses the vast and multidisciplinary literature that studies social networks as a structural determinant of health. In particular, we report on the current state of knowledge on how social contagion dynamics influence individual and collective health outcomes. We pay specific attention to research that leverages large-scale online data and social network experiments to empirically identify three broad classes of contagion processes: pathogenic diffusion, informational and belief diffusion, and behavioral diffusion. We conclude by identifying the need for more research on ( a) how multiple contagions interact within the same social network, ( b) how online social networks impact offline health, and ( c) the effectiveness of social network interventions for improving population health.

The Relationship between Online Social Network Ties and User Attributes

The distance between users has an effect on the formation of social network ties, but it is not the only or even the main factor. Knowing all the features that influence such ties is very important for many related domains such as location-based recommender systems and community and event detection systems for online social networks (OSNs). In recent years, researchers have analyzed the role of user geo-location in OSNs. Researchers have also attempted to determine the probability of friendships being established based on distance, where friendship is not only a function of distance. However, some important features of OSNs remain unknown. In order to comprehensively understand the OSN phenomenon, we also need to analyze users’ attributes. Basically, an OSN functions according to four main user properties: user geo-location, user weight, number of user interactions, and user lifespan. The research presented here sought to determine whether the user mobility pattern can be used to predict users’ interaction behavior. It also investigated whether, in addition to distance, the number of friends (known as user weight) interferes in social network tie formation. To this end, we analyzed the above-stated features in three large-scale OSNs. We found that regardless of a high degree freedom in user mobility, the fraction of the number of outside activities over the inside activity is a significant fraction that helps us to address the user interaction behavior. To the best of our knowledge, research has not been conducted elsewhere on this issue. We also present a high-resolution formula in order to improve the friendship probability function.

Piggyback Game: Efficient Event Stream Dissemination in Online Social Network Systems

Event stream dissemination dominates the workloads in large-scale Online Social Network (OSN) systems. Based on the de facto per-user view data storage, event stream dissemination raises a large amount of inter-server traffic due to the complex interconnection among OSN users. The state-of-the-art schemes mainly explore the structure features of social graphs to reduce the inter-server communications for event stream dissemination. Different sub-graph structures are exploited for achieving approximated optimal solutions. However, such schemes incur prohibitively high cost of either computation or communication. In this work, we follow a different design philosophy by using a game theoretic approach, which decomposes the highly complex graph computation problem into rational decision making of every individual social link. Specifically, we propose a novel social piggyback game to achieve a more efficient solution. We mathematically prove the existence of the Nash Equilibrium of the social piggyback game. Moreover, we propose an efficient best response dynamic algorithm to achieve the Nash Equilibrium, which quickly converges in a small number of iterations for large-scale OSNs. We further show that the communication cost of this design achieves a 1.5-approximation of the theoretical social optimum. We conduct comprehensive experiments using large-scale real-world traces from popular OSN systems as well as implement a prototype system to evaluate the performance of this design. Results show that the social piggyback game achieves a significant 302× improvement in system efficiency compared to existing schemes.