Showing papers on "Social network published in 2018"

Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks

Abstract: Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, sensitive information may be predicted out of released data through data mining techniques. Therefore, sanitizing network data prior to release is necessary. In this paper, we explore how to launch an inference attack exploiting social networks with a mixture of non-sensitive attributes and social relationships. We map this issue to a collective classification problem and propose a collective inference model. In our model, an attacker utilizes user profile and social relationships in a collective manner to predict sensitive information of related victims in a released social network dataset. To protect against such attacks, we propose a data sanitization method collectively manipulating user profile and friendship relations. Besides sanitizing friendship relations, the proposed method can take advantages of various data-manipulating methods. We show that we can easily reduce adversary’s prediction accuracy on sensitive information, while resulting in less accuracy decrease on non-sensitive information towards three social network datasets. This is the first work to employ collective methods involving various data-manipulating methods and social relationships to protect against inference attacks in social networks.

DeepInf: Social Influence Prediction with Deep Learning

Abstract: Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.

Attributed Social Network Embedding

Abstract: Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, we explore the rich evidence source of attributes in social networks to improve network embedding. We propose a generic Attributed Social Network Embedding framework ( ASNE ), which learns representations for social actors (i.e., nodes) by preserving both the structural proximity and attribute proximity . While the structural proximity captures the global network structure, the attribute proximity accounts for the homophily effect. To justify our proposal, we conduct extensive experiments on four real-world social networks. Compared to the state-of-the-art network embedding approaches, ASNE can learn more informative representations, achieving substantial gains on the tasks of link prediction and node classification. Specifically, ASNE significantly outperforms node2vec with an 8.2 percent relative improvement on the link prediction task, and a 12.7 percent gain on the node classification task.

Consensus Reaching in Social Network Group Decision Making: Research Paradigms and Challenges

Abstract: In social network group decision making (SNGDM), the consensus reaching process (CRP) is used to help decision makers with social relationships reach consensus. Many CRP studies have been conducted in SNGDM until now. This paper provides a review of CRPs in SNGDM, and as a result it classifies them into two paradigms: (i) the CRP paradigm based on trust relationships, and (ii) the CRP paradigm based on opinion evolution. Furthermore, identified research challenges are put forward to advance this area of research.

Social Connectedness: Measurement, Determinants, and Effects

Abstract: Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors. Traditionally, the unavailability of large-scale and representative data on social connectedness between individuals or geographic regions has posed a challenge for empirical research on social networks. More recently, a body of such research has begun to emerge using data on social connectedness from online social networking services such as Facebook, LinkedIn, and Twitter. To date, most of these research projects have been built on anonymized administrative microdata from Facebook, typically by working with coauthor teams that include Facebook employees. However, there is an inherent limit to the number of researchers that will be able to work with social network data through such collaborations. In this paper, we therefore introduce a new measure of social connectedness at the US county level. Our Social Connectedness Index is based on friendship links on Facebook, the global online social networking service. Specifically, the Social Connectedness Index corresponds to the relative frequency of Facebook friendship links between every county-pair in the United States, and between every US county and every foreign country. Given Facebook's scale as well as the relative representativeness of Facebook's user body, these data provide the first comprehensive measure of friendship networks at a national level.

Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship

Abstract: Echo chambers, i.e., situations where one is exposed only to opinions that agree with their own, are an increasing concern for the political discourse in many democratic countries. This paper studies the phenomenon of political echo chambers on social media. We identify the two components in the phenomenon: the opinion that is shared, and the »chamber» (i.e., the social network) that allows the opinion to »echo» (i.e., be re-shared in the network) -- and examine closely at how these two components interact. We define a production and consumption measure for social-media users, which captures the political leaning of the content shared and received by them. By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own. We also find that users who try to bridge the echo chambers, by sharing content with diverse leaning, have to pay a »price of bipartisanship» in terms of their network centrality and content appreciation. In addition, we study the role of »gatekeepers,» users who consume content with diverse leaning but produce partisan content (with a single-sided leaning), in the formation of echo chambers. Finally, we apply these findings to the task of predicting partisans and gatekeepers from social and content features. While partisan users turn out relatively easy to identify, gatekeepers prove to be more challenging.

Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate

Abstract: When a message, such as a piece of news, spreads in social networks, how can we classify it into categories of interests, such as genuine or fake news? Classification of social media content is a fundamental task for social media mining, and most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags. However, for many emerging applications like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to make it look like real news. To address this problem, this paper concentrates on modeling the propagation of messages in a social network. Specifically, we propose a novel approach, TraceMiner, to (1) infer embeddings of social media users with social network structures; and (2) utilize an LSTM-RNN to represent and classify propagation pathways of a message. Since content information is sparse and noisy on social media, adopting TraceMiner allows to provide a high degree of classification accuracy even in the absence of content information. Experimental results on real-world datasets show the superiority over state-of-the-art approaches on the task of fake news detection and news categorization.

Study on centrality measures in social networks: a survey

Abstract: Social networks are absolutely a useful and important place for connecting people within the world. A basic issue in a social network is to identify the key persons within it. This is why different centrality measures have been found over the years. In this survey paper, we present past and present research works on measures of centrality in social network. For this plan, we discuss mathematical definitions and different developed centrality measures. We also present some applications of centrality measures in biology, research, security, traffic, transportation, drug, class room. At last, our future research work on centrality measure is given.

Big Data: Deep Learning for financial sentiment analysis

Abstract: Deep Learning and Big Data analytics are two focal points of data science. Deep Learning models have achieved remarkable results in speech recognition and computer vision in recent years. Big Data is important for organizations that need to collect a huge amount of data like a social network and one of the greatest assets to use Deep Learning is analyzing a massive amount of data (Big Data). This advantage makes Deep Learning as a valuable tool for Big Data. Deep Learning can be used to extract incredible information that buried in a Big Data. The modern stock market is an example of these social networks. They are a popular place to increase wealth and generate income, but the fundamental problem of when to buy or sell shares, or which stocks to buy has not been solved. It is very common among investors to have professional financial advisors, but what is the best resource to support the decisions these people make? Investment banks such as Goldman Sachs, Lehman Brothers, and Salomon Brothers dominated the world of financial advice for more than a decade. However, via the popularity of the Internet and financial social networks such as StockTwits and SeekingAlpha, investors around the world have new opportunity to gather and share their experiences. Individual experts can predict the movement of the stock market in financial social networks with the reasonable accuracy, but what is the sentiment of a mass group of these expert authors towards various stocks? In this paper, we seek to determine if Deep Learning models can be adapted to improve the performance of sentiment analysis for StockTwits. We applied several neural network models such as long short-term memory, doc2vec, and convolutional neural networks, to stock market opinions posted in StockTwits. Our results show that Deep Learning model can be used effectively for financial sentiment analysis and a convolutional neural network is the best model to predict sentiment of authors in StockTwits dataset.

Depression detection from social network data using machine learning techniques.

Abstract: Social networks have been developed as a great point for its users to communicate with their interested friends and share their opinions, photos, and videos reflecting their moods, feelings and sentiments. This creates an opportunity to analyze social network data for user’s feelings and sentiments to investigate their moods and attitudes when they are communicating via these online tools. Although diagnosis of depression using social networks data has picked an established position globally, there are several dimensions that are yet to be detected. In this study, we aim to perform depression analysis on Facebook data collected from an online public source. To investigate the effect of depression detection, we propose machine learning technique as an efficient and scalable method. We report an implementation of the proposed method. We have evaluated the efficiency of our proposed method using a set of various psycholinguistic features. We show that our proposed method can significantly improve the accuracy and classification error rate. In addition, the result shows that in different experiments Decision Tree (DT) gives the highest accuracy than other ML approaches to find the depression. Machine learning techniques identify high quality solutions of mental health problems among Facebook users.

Social Network Sites and Well-Being: The Role of Social Connection:

Abstract: In the early days of the Internet, both conventional wisdom and scholarship deemed online communication a threat to well-being. Later research has complicated this picture, offering mixed evidence ...

Can Network Theory-based Targeting Increase Technology Adoption?

Abstract: In order to induce farmers to adopt a productive new agricultural technology, we apply simple and complex contagion diffusion models on rich social network data from 200 villages in Malawi to identify seed farmers to target and train on the new technology. A randomized controlled trial compares these theory-driven network targeting approaches to simpler strategies that either rely on a government extension worker or an easily measurable proxy for the social network (geographic distance between households) to identify seed farmers. Our results indicate that technology diffusion is characterized by a complex contagion learning environment in which most farmers need to learn from multiple people before they adopt themselves. Network theory based targeting can out-perform traditional approaches to extension, and we identify methods to realize these gains at low cost to policymakers. Keywords: Social Learning, Agricultural Technology Adoption, Complex Contagion, Malawi JEL Classification Codes: O16, O13

A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part II

Abstract: Recent years have witnessed a significant trend towards filling the gap between Social Network Analysis (SNA) and control theory. This trend was enabled by the introduction of new mathematical models describing dynamics of social groups, the development of algorithms and software for data analysis and the tremendous progress in understanding complex networks and multi-agent systems (MAS) dynamics. The aim of this tutorial is to highlight a novel chapter of control theory, dealing with dynamic models of social networks and processes over them, to the attention of the broad research community. In its first part [1], we have considered the most classical models of social dynamics, which have anticipated and to a great extent inspired the recent extensive studies on MAS and complex networks. This paper is the second part of the tutorial, and it is focused on more recent models of social processes that have been developed concurrently with MAS theory. Future perspectives of control in social and techno-social systems are also discussed.

Hiding individuals and communities in a social network

Abstract: The Internet and social media have fuelled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections, with particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question ‘Can individuals or groups actively manage their connections to evade social network analysis tools?’ By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence and security agencies may better understand how terrorists escape detection. We first study how an individual can evade ‘node centrality’ analysis while minimizing the negative impact that this may have on his or her influence. We prove that an optimal solution to this problem is difficult to compute. Despite this hardness, we demonstrate how even a simple heuristic, whereby attention is restricted to the individual’s immediate neighbourhood, can be surprisingly effective in practice; for example, it could easily disguise Mohamed Atta’s leading position within the World Trade Center terrorist network. We also study how a community can increase the likelihood of being overlooked by community-detection algorithms. We propose a measure of concealment—expressing how well a community is hidden—and use it to demonstrate the effectiveness of a simple heuristic, whereby members of the community either ‘unfriend’ certain other members or ‘befriend’ some non-members in a coordinated effort to camouflage their community. Waniek and colleagues show that individuals and communities can disguise themselves from detection online by standard social network analysis tools through simple changes to their social network connections.

Rumor Detection with Hierarchical Social Attention Network

Abstract: Microblogs have become one of the most popular platforms for news sharing. However, due to its openness and lack of supervision, rumors could also be easily posted and propagated on social networks, which could cause huge panic and threat during its propagation. In this paper, we detect rumors by leveraging hierarchical representations at different levels and the social contexts. Specifically, we propose a novel hierarchical neural network combined with social information (HSA-BLSTM). We first build a hierarchical bidirectional long short-term memory model for representation learning. Then, the social contexts are incorporated into the network via attention mechanism, such that important semantic information is introduced to the framework for more robust rumor detection. Experimental results on two real world datasets demonstrate that the proposed method outperforms several state-of-the-arts in both rumor detection and early detection scenarios.

Egocentric Network Analysis: Foundations, Methods, and Models

Abstract: Egocentric network analysis is used widely across the social sciences, especially in anthropology, political science, economics, and sociology, and is increasingly being employed in communications, informatics, and business and marketing studies. Egocentric network analysis requires a unique set of data collection and analysis skills that overlap only minimally with other network methodologies. However, until now there has been no single reference for conceptualizing, collecting, and analyzing egocentric social network data. This comprehensive guide to study design, data collection, and analysis brings together the state of knowledge with the most effective research tools to guide newcomers to this field. It is illustrated with many engaging examples and graphics and assumes no prior knowledge. Covering the entire research process in a logical sequence, from conceptualizing research questions to interpreting findings, this volume provides a solid foundation for researchers at any stage of their career to learn and apply ego network methods.

Interventions to address social connectedness and loneliness for older adults: a scoping review.

Abstract: Older adults are at risk for loneliness, and interventions to promote social connectedness are needed to directly address this problem. The nature of interventions aimed to affect the distinct, subjective concepts of loneliness/social connectedness has not been clearly described. The purpose of this review was to map the literature on interventions and strategies to affect loneliness/social connectedness for older adults. A comprehensive scoping review was conducted. Six electronic databases were searched from inception in July 2015, resulting in 5530 unique records. Standardized inclusion/exclusion criteria were applied, resulting in a set of 44 studies (reported in 54 articles) for further analysis. Data were extracted to describe the interventions and strategies, and the context of the included studies. Analytic techniques included calculating frequencies, manifest content analysis and meta-summary. Interventions were described or evaluated in 39 studies, and five studies described strategies to affect loneliness/social connectedness of older adults or their caregivers in a qualitative descriptive study. The studies were often conducted in the United States (38.6%) among community dwelling (54.5%), cognitively intact (31.8%), and female-majority (86.4%) samples. Few focused on non-white participants (4.5%). Strategies described most often were engaging in purposeful activity and maintaining contact with one’s social network. Of nine intervention types identified, the most frequently described were One-to-One Personal Contact and Group Activity. Authors held divergent views of why the same type of intervention might impact social connectedness, but social contact was the most frequently conceptualized influencing factor targeted, both within and across intervention types. Research to test the divergent theories of why interventions work is needed to advance understanding of intervention mechanisms. Innovative conceptualizations of intervention targets are needed, such as purposeful activity, that move beyond the current focus on the objective social network as a way to promote social connectedness for older adults.

Latent-Data Privacy Preserving With Customized Data Utility for Social Network Data

Abstract: Social network data can help with obtaining valuable insight into social behaviors and revealing the underlying benefits. New big data technologies are emerging to make it easier to discover meaningful social information from market analysis to counterterrorism. Unfortunately, both diverse social datasets and big data technologies raise stringent privacy concerns. Adversaries can launch inference attacks to predict sensitive latent information, which is unwilling to be published by social users. Therefore, there is a tradeoff between data benefits and privacy concerns. In this paper, we investigate how to optimize the tradeoff between latent-data privacy and customized data utility. We propose a data sanitization strategy that does not greatly reduce the benefits brought by social network data, while sensitive latent information can still be protected. Even considering powerful adversaries with optimal inference attacks, the proposed data sanitization strategy can still preserve both data benefits and social structure, while guaranteeing optimal latent-data privacy. To the best of our knowledge, this is the first work that preserves both data benefits and social structure simultaneously and combats against powerful adversaries.

A global approach to the analysis of user behavior in mobile payment systems in the new electronic environment

Abstract: Mobile devices and social media have led to a profound revolution of modern society, obliging many companies to reorient their sales systems towards more successful commercial formats (mobile commerce and social commerce). The mobile payment, for instance, as an emerging and supplementary service to these new commercial formats, is now undergoing the adoption process. Mobile payment has long been discussed, but it has not yet reached the usage levels expected by the different mass market players (financial institutions, telephone operators, etc.) in Western societies. The purpose of this paper is to analyze users’ acceptance of mobile payment systems on social networks. In order to explain acceptance, we have integrated trust and perceived risk into the traditional TAM model. To complete this study, we have established the decisive factors of this payment system by analyzing user’s gender, age and experience level. The study was conducted through an online survey among a national panel composed by 2.012 social network users. The results of this research support previous studies and provide alternatives for companies to consolidate this new business model by means of the new technical developments.

News in Social Media

Abstract: Social network sites are becoming essential to how people experience news. The social media feed is made up of a mixture of private and public postings, and news is intertwined with all sorts of ac...

Mobility Dataset Generation for Vehicular Social Networks Based on Floating Car Data

Abstract: Vehicular social networks (VSNs) have attracted the research community due to its diverse applications ranging from safety to entertainment. Social vehicles standing for private cars and floating cars standing for taxis are two important components of VSN. However, the lack of social vehicles data causes some factors are neglected including social aspects and macroscopic features, which blocks researching social attributes of vehicles in VSN. Generating a realistic mobility dataset for VSN validation has been a great challenge. In this paper, we present the detailed procedure to generate social vehicular mobility dataset from the view of floating car data, which has the advantage of wide universality. First, through the deep analysis and modeling of the dataset of floating cars and combining with the official data, we predict the origin–destination (OD) matrix of social vehicles with the gravity model, and then calibrate the OD matrix with the average growth factor method. Second, we construct network description after editing the road network. Third, we use simulation of urban mobility to reproduce the scenario in view of microsimulation by generating the mobility dataset of social vehicles based on floating car data and urban functional areas. At last, we prove the effectiveness of our method by comparing with real traffic situation in Beijing. The generated mobility model may not accurately represent the mobility of social vehicles in few spots, such as train station or airport, however, exploiting figures and facts of transportation in the city have been considered in the study to calibrate the model up to maximum possible realization.

Use of Social Network Sites for Communication Among Health Professionals: Systematic Review

Abstract: Background: Although much research has been done investigating the roles of social network sites (SNSs) in linking patients and health professionals, there is a lack of information about their uses, benefits, and limitations in connecting health professions only for professional communication. Objective: This review aimed to examine the utilization of SNSs for communication among health professionals in (1) frontline clinical practice, (2) professional networks, and (3) education and training to identify areas for future health communication research. Methods: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic search of the literature published in the last 10 years (January 1, 2007, to March 1, 2017) was performed in March 2017, using the following electronic databases: MEDLINE via OvidSP, EMBASE, CINAHL Complete, and InfoSci-Journals. The searches were conducted using the following defined search terms: “social media” OR “social network” OR “social network site” OR “Facebook” OR “Twitter” OR “Linkedin” OR “Instagram” OR “Weibo” OR “Whatsapp” OR “Telegram” OR “WeChat” AND “health” OR “health profession.” Results: Of the 6977 papers retrieved, a total of 33 studies were included in this review. They were exploratory in nature, and the majority used surveys (n=25) and interviews (n=6). All retrieved studies stated that SNSs enhanced effective communication and information sharing. SNSs were used for supporting delivering of clinical services, making referrals, and sharing information. They were beneficial to network building and professional collaboration. SNSs were novel tools to enhance educational interactions among peers, students, instructors, and preceptors. The application of SNSs came with restraints in technical knowledge, concerns on data protection, privacy and liability, issues in professionalism, and data protection. Conclusions: SNSs provide platforms facilitating efficient communication, interactions, and connections among health professionals in frontline clinical practice, professional networks, education, and training with limitations identified as technical knowledge, professionalism, and risks of data protection. The evolving use of SNSs necessitates robust research to explore the full potential and the relative effectiveness of SNSs in professional communication.

Network Structure Inference, A Survey: Motivations, Methods, and Applications

Abstract: Networks represent relationships between entities in many complex systems, spanning from online social interactions to biological cell development and brain connectivity. In many cases, relationships between entities are unambiguously known: are two users “friends” in a social network? Do two researchers collaborate on a published article? Do two road segments in a transportation system intersect? These are directly observable in the system in question. In most cases, relationships between nodes are not directly observable and must be inferred: Does one gene regulate the expression of another? Do two animals who physically co-locate have a social bond? Who infected whom in a disease outbreak in a population? Existing approaches for inferring networks from data are found across many application domains and use specialized knowledge to infer and measure the quality of inferred network for a specific task or hypothesis. However, current research lacks a rigorous methodology that employs standard statistical validation on inferred models. In this survey, we examine (1) how network representations are constructed from underlying data, (2) the variety of questions and tasks on these representations over several domains, and (3) validation strategies for measuring the inferred network’s capability of answering questions on the system of interest.

Sense‐making in social media during extreme events

Abstract: During an extreme event, individuals use social media to communicate, self-organize, manage, and mitigate risks (crisis-related communications) but also to make sense of the event (commentary-related communications). This study focuses on commentary-based social media communication practices of Twitter users to understand the processes and patterns of inter-subjective sense-making during an extreme event. We analyse Twitter communication generated during three events: The Sydney Lindt Cafe Siege (2014), the Germanwings plane crash (2015), and the Brussels Terror Attacks (2016). We focus on the (i) communication structure, (ii) emotionality of the content via sentiment analyses, and (iii) influence of Twitter users on communications via social network analyses. We identified differences in the communication structures between the three events, which suggests a research agenda focussed on inter-subjective sense-making through the use of social media platforms, would make a significant contribution to knowledge about social media adoption and use in extreme events.