Information In Online Social Networks Research Articles

Efficiently discovering users connectivity with local information in online social networks

People’s activities in Online Social Networks (OSNs) have generated a massive volume of data to which tremendous attention has been paid in academia and industry. With such data, researchers and third-parties can analyze human beings’ behaviors in social communities and develop more user-friendly services and applications to meet people’s needs. However, often times, they face a big challenge of acquiring the data, as the access to such data is restricted by their collectors (e.g., Facebook and Twitter), due to various reasons, such as their user’s privacy. In this paper, we intend to shed light on leveraging limited local social network topological properties to effectively and efficiently conduct search in OSNs. The problem we focus on is to discover the connectivity of a group of target users in an OSN, particularly from the perspective of a third-party analyst who does not have full access to the network. For the analyst, even discovering a user’s local connections requires issuing a query through OSN APIs (e.g., Facebook Friendlist API or Twitter Followerlist API). We develop searching techniques which demand only a few number of queries for the connectivity discovery.After conducting an intensive set of experiments on both real-world and synthetic data sets, we found that our proposed techniques perform as well as the centralized detection algorithm, which assumes the availability of the entire data set, in terms of the size of the discovered subgraph connecting all target users as well as the number of queries made in the search. The experiment results demonstrate the effectiveness of incorporating topological properties of social networks into searching in the OSNs.

When Wireless Crowd Charging Meets Online Social Networks: A Vision for Socially Motivated Energy Sharing

Individual users of a social crowd are bound by finite resource capacity. Other users (such as online friends) may have surplus capacity or resources that, if shared, could be used to meet fluctuating demand. A particularly important resource in modern societies is the energy supplies of mobile portable devices like smartphones; quick energy depletion is an everyday problem in the lives of billions of smartphone users worldwide. Peer-to-peer wireless crowd charging has recently emerged as an alternative energy replenishment option. Although there have been some recent advances on this type of wireless energy sharing, to the best of our knowledge, none of the related works has considered using online social network information as input. In this paper, for the first time in the state of the art, we introduce the vision of socially motivated wireless energy sharing and we provide a holistic framework for socially aware wireless crowd charging. We present a taxonomy of the existing representative use cases, and we define a model which takes into account not only the wireless power transfer specifics, but also the online social information. After having selected a target use case (balanced crowd energy efficiency), we design two socially aware wireless energy sharing protocols. The design of each protocol is inspired by the social topology and structural characteristics of diverse datasets of online social relationships among real users. In fact, aligning to studies on psychology which conclude that users typically favor friends over strangers in allocating resources (especially when the sharing task is costly), our protocols favor energy exchanges among friends or friendly groups. For the purposes of the protocol evaluation, we simulate the energy sharing interactions based on the user encounters, as reported in the datasets. In order to demonstrate the effects of the social component, we compare the performance of our protocols to the performance of another state of the art protocol that does not use online social information. Interestingly enough, we demonstrate that online social network information can indeed influence the selected use case, in terms of energy losses, as well as level of and time elapsed until a balanced energy distribution in the crowd. We conclude the paper by providing some interesting open research directions.

Selectivity in posting on social networks: the role of privacy concerns, social capital, and technical literacy

People's posting behaviors in social networks was perceived as ambiguous, with concerns misaligned with people's public postings. To address this gap, we suggest a model that offers new insights into the relationship between perceptions and actual behaviors. We define a quantitative marker for agility, the frequency in which people update their audience selection when posting information in online social networks, and evaluate the factors that contribute to the variability of agility between different users. We analyzed the posting behavior of Facebook 181 participants, as well as their answers to open and close questions. We find that frequent changes in privacy settings are correlated with high social privacy and with institutional privacy concerns, whereas social concerns were found to be more prominent. Agility was negatively correlated with low public sharing. Our findings show that users use privacy settings to effectively mitigate privacy concerns and desires for creating and strengthening social connections. We discuss how agility can be used to design and to evaluate new user interfaces for managing privacy in social settings.

Dynamic SEIZ in Online Social Networks: Epidemiological Modeling of Untrue Information

The epidemic propagation of untrue information in online social networks leads to potential damage to society. This phenomenon has attracted attention to researchers on a faster spread of false information. Epidemic models such as SI, SIS, SIR, developed to study the infection spread on social media. This paper uses SEIZ, an enhanced epidemic model classifies the overall population in four classes (i.e. Susceptible, Exposed, Infected, Skeptic). It uses probabilities of transition from one state to another state to characterize misinformation from actual information. It suffers from two limitations i.e. the rate of change of population and state transition probabilities considered constant for the entire period of observation. In this paper, a dynamic SEIZ computes the rate of change of population at fixed intervals and the predictions based on the new rates periodically. Research findings on Twitter data have indicated that this model gives more accuracy by early indications of being untrue information.

Intervention algorithm for malicious information in online social networks based on trusted regulator

Intervention algorithm for malicious information in online social networks based on trusted regulator

Reverse Intervention for Dealing with Malicious Information in Online Social Networks

Malicious information is often hidden in the massive data flow of online social networks. In “We Media'' era, if the system is closed without intervention, malicious information may spread to the entire network quickly, which would cause severe economic and political losses. This paper adopts a reverse intervention strategy from the perspective of topology control, so that the spread of malicious information could be suppressed at a minimum cost. Noting that as the information spreads, social networks often present a community structure and multiple malicious information promoters may appear. Therefore, this paper adopts a divide and conquer strategy and proposes an intervention algorithm based on subgraph partitioning, in which we search for some influential nodes to block or release clarification. The main algorithm consists of two main phases. Firstly, a subgraph partitioning method based on community structure is given to quickly extract the community structure of the information dissemination network. Secondly, a node blocking and clarification publishing algorithm based on the Jordan Center is proposed in the obtained subgraphs. Experiments show that the proposed algorithm can effectively suppress the spread of malicious information with a low time complexity compared with the benchmark algorithms.

Evolutionary Game Model of Public Opinion Information Propagation in Online Social Networks

The rapid development of communication technology has greatly changed the way of information propagation. While making use of positive public opinion information in online social networks (OSN) to create value, it is necessary to manage and control the propagation of public opinion. Considering the existence of both positive and negative public opinion, we proposed a tripartite evolutionary game model through identifying the relevant stakeholders involved in the public opinion spreading process, discussed the equilibrium conditions of stakeholders’ behavior strategies emphatically and carried out simulation experiments. Then, based on the experimental results, the management strategy and the key intervention points of public opinion spreading were proposed. The result shows that the key to management and control public opinion is realizing the interest balance of all stakeholders. That is, the government should increase the benefits of netizens and media spreading (reporting) positive public opinion, and at the same time strengthen the punishment of them spreading (reporting) negative public opinion. This paper further expands the research of public opinion propagation in OSN, and provides theoretical support and decision-making basis for the management and control of public opinion.

Intervention algorithm for malicious information in online social networks based on trusted regulator

A lot of malicious information such as rumours are hidden in the massive data flow in social online networks. Once this type of malicious information spreads, it could affect social stability in severe cases. This paper introduces the concept of trusted regulator to select key nodes, and we propose a method to hinder the rapid spread of malicious information by blocking accounts and publishing clarification, so that the external disturbances from the chosen nodes and the malicious information could fight against each other. Local control strategy is applied to the propagation of malicious information. Specifically, we first introduce the SIMRT model and an importance indicator based on edge weight, then we propose the reverse intervention algorithm based on the importance indicator. Experiment results on different data sets show that the proposed algorithm can effectively suppress the spread of malicious information.

Minimizing the influence of rumors during breaking news events in online social networks

The malicious rumors have tremendously attracted a more substantial number of researchers to join the fight against the propagation of these types of information in online social networks (OSNs). The spread of rumors has a severe impact on society, which can creates political conflicts, shape public opinion and weakens their trust in governments; therefore, it must be stopped as soon as it is detected. This paper investigates the problem of minimizing the influence of malicious rumors that emerge during breaking news, which are characterized by the dissemination of a large number of malicious information over a short period. Therefore, we introduce the problem of multi-rumors influence minimization (MRIM) in OSNs and propose a solution to it. To this end, we design a multi-rumor propagation model named the HISBMmodel that captures the propagation process of multi-rumors in OSNs. Moreover, we present a new formulation of an individual’s opinion toward a rumor based on a Markov chain representation, which adds a layer of realism to the proposed model. Subsequently, we propose a dynamic blocking period (DBP) approach as a solution for the MRIM problem. The main objective is to minimize both the spread and the influence of these rumors in OSNs. The proposed method selects and blocks nodes that most likely to spread a large number of rumors and support them. Different from existing methods, the proposed solution does not block nodes for an unlimited period, but this period is estimated according to the high activity of a node in an OSN. The survival theory has been exploited in this work to provide a solution formulated from the perspective of probabilistic inference of networks. Consequently, an algorithm has been proposed based on a likelihood principle to select the target nodes, which guarantees a (1−1∕e)-approximation of the optimal solution. The experimental results show that the HISBMmodel could capture the propagation of multi-rumor propagation more accurately than classical models and provides metrics to assess the impact of rumors efficiently. Moreover, the results show the outstanding performance of the proposed approach compared to the other solution in the literature. The experimental results show that in the worst-case, the DBP achieves on an average 37.66% reduction on the impact of rumors, compared to 18.46% obtained by the second-best method. However, in the best-case the performance of the proposed method reached 93.38% where second-best method achieved only 57.65% on an average. Besides, even though when the number of rumors is high, the DBP could achieve on an average 68.01% reduction on the impact of rumors.

Information Propagation and Public Opinion Evolution Model Based on Artificial Neural Network in Online Social Network

Abstract This paper proposes a new information dissemination and opinion evolution IPNN (Information Propagation Neural Network) model based on artificial neural network. The feedforward network, feedback network and dynamic evolution algorithms are designed and implemented. Firstly, according to the ‘six degrees separation’ theory of information dissemination, a seven-layer neural network underlying framework with input layer, propagation layer and termination layer is constructed; secondly, the information sharing and information interaction evolution process between nodes are described by using the event information forward propagation algorithm, opinion difference reverse propagation algorithm; finally, the external factors of online social network information dissemination is considered, the impact of external behavior patterns is measured by media public opinion guidance and network structure dynamic update operations. Simulation results show that the proposed new mathematical model reveals the relationship between the state of micro-network nodes and the evolution of macro-network public opinion. It accurately depicts the internal information interaction mechanism and diffusion mechanism in online social network. Furthermore, it reveals the process of network public opinion formation and the nature of public opinion explosion in online social network. It provides a new scientific method and research approach for the study of social network public opinion evolution.

Detecting Stress Based on Social Networking Interactions

Stress is a kind of demand to respond to any in your body's manner. It can be based on experiences that are both good and bad. Psychological stress threatens the health of individuals. People are used to exchanging their schedule and daily operations with colleagues on social media platforms with the reputation of a social media network, creating it possible to hold online social network information for stress detection. For a variety of applications data mining methods are used. Data mining plays a significant role in the detection of stress in sector. We proposed a new model in this article to detect stress. Initially, in this model, discover a correlation between stress states of user and effective public interactions. This describes a set of textual, visual and social characteristics related to stress from different elements and proposes a new hybrid model coupled with Convolutional Neural Network (CNN) to efficiently hold tweet content and data on social interaction to detect stress. The suggested model can enhance the detection efficiency by 97.8 percent, which is quicker than the current scheme, from the experimental outcomes.

Multi-level word features based on CNN for fake news detection in cultural communication

In recent years, due to the booming development of online social networks, fake news has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. However, fake news detection remains to be a challenge, primarily because the content is crafted to resemble the truth in order to deceive readers, and without fact-checking or additional information, it is often hard to determine veracity by text analysis alone. In this paper, we first proposed multi-level convolutional neural network (MCNN), which introduced the local convolutional features as well as the global semantics features, to effectively capture semantic information from article texts which can be used to classify the news as fake or not. We then employed a method of calculating the weight of sensitive words (TFW), which has shown their stronger importance with their fake or true labels. Finally, we develop MCNN-TFW, a multiple-level convolutional neural network-based fake news detection system, which is combined to perform fake news detection in that MCNN extracts article representation and WS calculates the weight of sensitive words for each news. Extensive experiments have been done on fake news detection in cultural communication to compare MCNN-TFW with several state-of-the-art models, and the experimental results have demonstrated the effectiveness of the proposed model.

Understanding the mechanism of social tie in the propagation process of social network with communication channel

The propagation of information in online social networks plays a critical role in modern life, and thus has been studied broadly. Researchers have proposed a series of propagation models, generally, which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his neighbors. However, the research on the mechanism how social ties between users play roles in propagation process is still limited. Specifically, comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works, so that the existing models failed to clearly describe the process a user be activated by a neighbor. To this end, in this paper, we analyze the close correspondence between social tie in propagation process and communication channel, thus we propose to exploit the communication channel to describe the information propagation process between users, and design a social tie channel (STC) model. The model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference, and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social tie. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.

Enhancing the privacy risk awareness of teenagers in online social networks through soft-paternalism mechanisms

Abstract Privacy Risk in Online Social Networks (OSNs) is one of the main concerns that has increased in the last few years. Even though social network applications provide mechanisms to control risk, teenagers are not often aware of the privacy risks of disclosing information in online social networks. The privacy decision-making process is complex and users often do not have full knowledge and enough time to evaluate all potential scenarios. They do not consider the audience that will have access to disclosed information or the risk if the information continues to spread and reaches an unexpected audience. To deal with these issues, we propose two soft-paternalism mechanisms that provide information to the user about the privacy risk of publishing information on a social network. That privacy risk is based on a complex privacy metric. To evaluate the mechanisms, we performed an experiment with 42 teenagers. The proposed mechanisms were included in a social network called Pesedia . The results show that there are significant differences in teenagers’ behaviors towards better privacy practices when the mechanisms are included in the network.

Detecting misinformation in online social networks: A think-aloud study on user strategies

Although online social networks (OSN) facilitate the distribution of misinformation, one way of reducing the spread of false information in OSN is for users to detect it. Building on the framework of how audiences act to authenticate information, this study provides a user perspective on which strategies people use in evaluating information in OSN. In 15 qualitative interviews, participants were asked to think aloud while evaluating whether the content of posts from their own newsfeeds and of interviewer-supplied posts was true or false. Their answers were analyzed to determine which evaluation strategies they used. Analyzing participants’ thoughts as they evaluate information is more reliable than directly asking participants which strategies they think they use. Results show that users’ strategies in information evaluation are searching for more information, knowledge of account or content carries the most weight, and every detail needs to fit. A comparison of strategy usage for posts from befriended versus unknown personal accounts as well as for posts from followed news outlets versus not followed news outlets shows that for posts from followed news outlets, knowledge of the account was the most-used strategy followed by knowledge of the content. For other types of posts, strategy usage varied more widely and depended on each post. This highlights the importance and possible higher ecological validity of research on posts from news outlets that users actually follow, as users’ experiences with previous posts seem to play a major role in how they go about evaluating information in new posts.

Metrics for Privacy Assessment When Sharing Information in Online Social Networks

Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts' scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures.

ActiveIter: Meta Diagram based Active Learning in Social Networks Alignment

Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several reasons, i.e., lack of training data, network heterogeneity and one-to-one constraint. Existing network alignment works usually require a large number of training data, but such a demand can hardly be met in applications, as manual anchor link labeling is extremely expensive. Significantly different from other homogeneous network alignment works, information in online social networks is usually of heterogeneous categories, the incorporation of which in model building is not an easy task. Furthermore, the one-to-one cardinality constraint on anchor links renders their inference process intertwistingly correlated. To resolve these three challenges, a novel network alignment model, namely ActiveIter, is introduced in this paper. ActiveIter defines a set of inter-network meta diagrams for anchor link feature extraction, adopts active learning for effective label query and uses greedy link selection for anchor link cardinality filtering. Extensive experiments are conducted on real-world aligned networks datasets, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.

A neural computing approach to the construction of information credibility assessments for online social networks

Information credibility assessment has traditionally been understood in the online information processing context. This study extends this literature by exploring information credibility assessment mechanisms in the online social network context. In doing so, it responds to the emerging call for an understanding of the online social network context under the operational mechanism of information credibility assessment. Specifically, this study proposes a neural computing approach based on the stimulate–organism–response framework to determine the process, strategy and clue of online social network information assessment. Drawing on the theory of the Technology Acceptance Model and the theory of reasoned action, and based on a survey of 399 returned online questionnaires, we find that the involvement of online social network information, information sources and information transfer channels has a significant positive correlation with information usefulness and credibility, while information scenarios have no significant impact on usefulness and credibility, and the credibility of information is significant to the user’s information adoption behavior. Different from the predictions made in the literature, the information scenario does not affect information credibility and information usefulness. Conclusions and academic and practical implications are discussed.

Inferring Mobility Relationship via Graph Embedding

Inferring social relationships from user location data has become increasingly important for real-world applications, such as recommendation, advertisement targeting, and transportation scheduling. Most existing mobility relationship measures are based on pairwise meeting frequency, that it, the more frequently two users meet (i.e., co-locate at the same time), the more likely that they are friends. However, such frequency-based methods suffer greatly from data sparsity challenge. Due to data collection limitation and bias in the real world (e.g., check-in data), the observed meeting events between two users might be very few. On the other hand, existing methods focus too much on the interactions between two users, but fail to incorporate the whole social network structure. For example, the relationship propagation is not well utilized in existing methods. In this paper, we propose to construct a user graph based on their spatial-temporal interactions and employ graph embedding technique to learn user representations from such a graph. The similarity measure of such representations can well describe mobility relationship and it is particularly useful to describe the similarity for user pairs with low or even zero meeting frequency. Furthermore, we introduce semantic information on meeting events by using point-of-interest (POI) categorical information. Additionally, when part of the social graph is available as friendship ground truth, we can easily encode such online social network information through a joint graph embedding. Experiments on two real-world datasets demonstrate the effectiveness of our proposed method.

SEIM: Search economics for influence maximization in online social networks

The influence of online social networks (OSN), which can be regarded as part of our life, is evident today. As expected, a great deal of useful information about the humans is hidden in the data, such as interpersonal relationship and personal preference. The influence maximization problem (IMP) is one of the well-known problems in this research domain that has attracted the attention of researchers from different disciplines in recent years. One of the reasons is that it can speed up the propagation of information in OSN if we can find out users that have maximum influence on other users. However, traditional rule-based and heuristic algorithms may not be able to find useful information out of these data because the data are generally large and complex. Although many recent studies attempted to use metaheuristic algorithms to solve the IMP, there is still plenty of room for improvement. The proposed algorithm, called search economics for influence maximization (SEIM), is motivated by the concept of return on investment to design its search strategies. As far as the proposed algorithm is concerned, the search strategy of SEIM is like making a good plan to determine how to invest the high potential investment subjects (i.e., regions) in the market (i.e., search space). The experimental results show that the proposed algorithm is significantly better than the other state-of-the-art influence maximization problem algorithms compared in this paper in terms of the quality of the end result and the number of objective function evaluations.