Showing papers in "IEEE Transactions on Computational Social Systems in 2020"

Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo

Abstract: During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.

A Survey of Sentiment Analysis from Social Media Data

Abstract: In the current era of automation, machines are constantly being channelized to provide accurate interpretations of what people express on social media. The human race nowadays is submerged in the idea of what and how people think and the decisions taken thereafter are mostly based on the drift of the masses on social platforms. This article provides a multifaceted insight into the evolution of sentiment analysis into the limelight through the sudden explosion of plethora of data on the internet. This article also addresses the process of capturing data from social media over the years along with the similarity detection based on similar choices of the users in social networks. The techniques of communalizing user data have also been surveyed in this article. Data, in its different forms, have also been analyzed and presented as a part of survey in this article. Other than this, the methods of evaluating sentiments have been studied, categorized, and compared, and the limitations exposed in the hope that this shall provide scope for better research in the future.

Security and Trust in Blockchains: Architecture, Key Technologies, and Open Issues

Abstract: As a new promising distributed technology, blockchains have been widely applied since its inception. Its decentralization feature reduces the reliance on the trusted authorities and third parties. It can well solve the problem of data being tampered and increase data sharing. However, a blockchain system faces various security and trust issues, such as attacks against consensus mechanisms and propagation processes, which may make it store malicious information or delay data propagation. The work discusses the basic architecture of blockchains as well as its potential security and trust issues at data, network, consensus, smart contract, and application layers. Then, the related literature work is analyzed in terms of the issues at these layers. Some open issues are presented and discussed.

Defensive Modeling of Fake News Through Online Social Networks

Abstract: Online social networks (OSNs) have become an integral mode of communication among people and even nonhuman scenarios can also be integrated into OSNs. The ever-growing rise in the popularity of OSNs can be attributed to the rapid growth of Internet technology. OSN becomes the easiest way to broadcast media (news/content) over the Internet. In the wake of emerging technologies, there is dire need to develop methodologies, which can minimize the spread of fake messages or rumors that can harm society in any manner. In this article, a model is proposed to investigate the propagation of such messages currently coined as fake news. The proposed model describes how misinformation gets disseminated among groups with the influence of different misinformation refuting measures. With the onset of the novel coronavirus-19 pandemic, dubbed COVID-19, the propagation of fake news related to the pandemic is higher than ever. In this article, we aim to develop a model that will be able to detect and eliminate fake news from OSNs and help ease some OSN users stress regarding the pandemic. A system of differential equations is used to formulate the model. Its stability and equilibrium are also thoroughly analyzed. The basic reproduction number ( $R_{0}$ ) is obtained which is a significant parameter for the analysis of message spreading in the OSNs. If the value of $R_{0}$ is less than one ( $R_{0} ), then fake message spreading in the online network will not be prominent, otherwise if $R_{0}> 1$ the rumor will persist in the OSN. Real-world trends of misinformation spreading in OSNs are discussed. In addition, the model discusses the controlling mechanism for untrusted message propagation. The proposed model has also been validated through extensive simulation and experimentation.

Movie Recommendation System Using Sentiment Analysis From Microblogging Data

Abstract: Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media. Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these approaches that have certain limitations, such as the necessity of prior user history and habits for performing the task of recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies that leverage the best of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging sites. The purpose to use movie tweets is to understand the current trends, public sentiment, and user response of the movie. Experiments conducted on the public database have yielded promising results.

Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods

Abstract: Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This article summarizes the recently proposed methods to solve an aspect-based sentiment analysis problem. At present, there are three mainstream methods: lexicon-based, traditional machine learning, and deep learning methods. In this survey article, we provide a comparative review of state-of-the-art deep learning methods. Several commonly used benchmark data sets, evaluation metrics, and the performance of the existing deep learning methods are introduced. Finally, existing problems and some future research directions are presented and discussed.

A Blockchain-Based Reward Mechanism for Mobile Crowdsensing

Abstract: Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator’s utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS.

Walrasian Equilibrium-Based Multiobjective Optimization for Task Allocation in Mobile Crowdsourcing

Abstract: With the rapid development of Industry 5.0 and mobile devices, the research of mobile crowdsensing networks has become an important research focus. Task allocation is an important research content that can inspire crowd workers to participate in crowd tasks and provide truthful sensed data in mobile crowdsourcing systems. However, how to inspire crowd workers to participate in crowd tasks and provide truthful sensed data still has many challenges. In this article, based on the Markov model and collaborative filtering model, the similarities, trajectory prediction, dwell time, and trust degree are considered to propose the Markov and Collaborative filtering-based Task Recommendation (MCTR) model. Then, based on the Walrasian equilibrium, the optimum solution is researched to maximize the social welfare of mobile crowdsourcing systems. Finally, the comparison experiments are carried out to evaluate the performance of the proposed multiobjective optimization and the Markov-based task allocation with other methods. Through comparison experiments, the efficiency and adaptation of mobile crowdsourcing systems could be improved by the proposed task allocation.

Toward Artificial Emotional Intelligence for Cooperative Social Human–Machine Interaction

Abstract: The aptitude to identify the emotional states of others and response to exposed emotions is an important aspect of human social intelligence. Robots are expected to be prevalent in society to assist humans in various tasks. Human–robot interaction (HRI) is of critical importance in the assistive robotics sector. Smart digital assistants and assistive robots fail quite often when a request is not well defined verbally. When the assistant fails to provide services as desired, the person may exhibit an emotional response such as anger or frustration through expressions in their face and voice. It is critical that robots understand not only the language, but also human psychology. A novel affection-based perception architecture for cooperative HRIs is studied in this paper, where the agent is expected to recognize human emotional states, thus encourages a natural bonding between the human and the robotic artifact. We propose a method to close the loop using measured emotions to grade HRIs. This metric will be used as a reward mechanism to adjust the assistant’s behavior adaptively. Emotion levels from users are detected through vision and speech inputs processed by deep neural networks (NNs). Negative emotions exhibit a change in performance until the user is satisfied.

Deep Representation Learning With Full Center Loss for Credit Card Fraud Detection

Abstract: Credit card fraud detection is an important study in the current era of mobile payment. Improving the performance of a fraud detection model and keeping its stability are very challenging because users’ payment behaviors and criminals’ fraud behaviors are often changing. In this article, we focus on obtaining deep feature representations of legal and fraud transactions from the aspect of the loss function of a deep neural network. Our purpose is to obtain better separability and discrimination of features so that it can improve the performance of our fraud detection model and keep its stability. We propose a new kind of loss function, full center loss (FCL), which considers both distances and angles among features and, thus, can comprehensively supervise the deep representation learning. We conduct lots of experiments on two big data sets of credit card transactions, one is private and another is public, to demonstrate the detection performance of our model by comparing FCL with other state-of-the-art loss functions. The results illustrate that FCL outperforms others. We also conduct experiments to show that FCL can ensure a more stable model than others.

Automatic Concept Extraction Based on Semantic Graphs From Big Data in Smart City

Abstract: With the rapid development of smart cities, various types of sensors can rapidly collect a large amount of data, and it becomes increasingly important to discover effective knowledge and process information from massive amounts of data. Currently, in the field of knowledge engineering, knowledge graphs, especially domain knowledge graphs, play important roles and become the infrastructure of Internet knowledge-driven intelligent applications. Domain concept extraction is critical to the construction of domain knowledge graphs. Although there have been some works that have extracted concepts, semantic information has not been fully used. However, the excellent concept extraction results can be obtained by making full use of semantic information. In this article, a novel concept extraction method, Semantic Graph-Based Concept Extraction (SGCCE), is proposed. First, the similarities between terms are calculated using the word co-occurrence, the LDA topic model and Word2Vec. Then, a semantic graph of terms is constructed based on the similarities between the terms. Finally, according to the semantic graph of the terms, community detection algorithms are used to divide the terms into different communities where each community acts as a concept. In the experiments, we compare the concept extraction results that are obtained by different community detection algorithms to analyze the different semantic graphs. The experimental results show the effectiveness of our proposed method. This method can effectively use semantic information, and the results of the concept extraction are better from domain big data in smart cities.

Comparative Analysis of Feature Selection Algorithms for Computational Personality Prediction From Social Media

Abstract: With the rapid growth of social media, users are getting involved in virtual socialism, generating a huge volume of textual and image contents. Considering the contents such as status updates/tweets and shared posts/retweets, liking other posts is reflecting the online behavior of the users. Predicting personality of a user from these digital footprints has become a computationally challenging problem. In a profile-based approach, utilizing the user-generated textual contents could be useful to reflect the personality in social media. Using huge number of features of different categories, such as traditional linguistic features (character-level, word-level, structural, and so on), psycholinguistic features (emotional affects, perceptions, social relationships, and so on) or social network features (network size, betweenness, and so on) could be useful to predict personality traits from social media. According to a widely popular personality model, namely, big-five-factor model (BFFM), the five factors are openness-to-experience, conscientiousness, extraversion, agreeableness, and neuroticism. Predicting personality is redefined as predicting each of these traits separately from the extracted features. Traditionally, it takes huge number of features to get better accuracy on any prediction task although applying feature selection algorithms may improve the performance of the model. In this article, we have compared the performance of five feature selection algorithms, namely the Pearson correlation coefficient (PCC), correlation-based feature subset (CFS), information gain (IG), symmetric uncertainly (SU) evaluator, and chi-squared (CHI) method. The performance is evaluated using the classic metrics, namely, precision, recall, f-measure, and accuracy as evaluation matrices.

Improved TrAdaBoost and its Application to Transaction Fraud Detection

Abstract: AdaBoost is a boosting-based machine learning method under the assumption that the data in training and testing sets have the same distribution and input feature space. It increases the weights of those instances that are wrongly classified in a training process. However, the assumption does not hold in many real-world data sets. Therefore, AdaBoost is extended to transfer AdaBoost (TrAdaBoost) that can effectively transfer knowledge from one domain to another. TrAdaBoost decreases the weights of those instances that belong to the source domain but are wrongly classified in a training process. It is more suitable for the case that data are of different distribution. Can it be improved for some special transfer scenarios, e.g., the data distribution changes slightly over time? We find that the distribution of credit card transaction data can change with the changes in the transaction behaviors of users, but the changes are slow most of the time. These changes are yet important for detecting transaction fraud since they result in a so-called concept drift problem. In order to make TrAdaBoost more suitable for the abovementioned case, we, thus, propose an improved TrAdaBoost (ITrAdaBoost) in this article. It updates (i.e., increases or decreases) the weight of a wrongly classified instance in a source domain according to the distribution distance from the instance to a target domain, and the calculation of distance is based on the theory of reproducing kernel Hilbert space. We do a series of experiments over five data sets, and the results illustrate the advantage of ITrAdaBoost.

A Spatial Mobile Crowdsourcing Framework for Event Reporting

Abstract: The widespread use of advanced mobile devices has led to the emergence of a new class of mobile crowdsourcing called spatial mobile crowdsourcing (SMCS). The main feature of SMCS is the presence of spatial tasks that require workers to be physically present at a particular location for task fulfillment. These tasks usually take advantage of the built-in sensors in mobile devices by requesting environment sensing services. Because cameras are becoming the most common way for visual logging techniques and sensing in our daily lives, we propose, in this article, a photo-based SMCS framework for event reporting. The proposed framework allows event report requesters to solicit photos of ongoing events and keep track of any updates. We propose a full architecture in which we solve the SMCS recruitment problem using different fairness strategies in the presence of multiple events and reporters. Then, once submissions are received and before forwarding final responses to event requesters, we proceed with a data processing phase for data quality monitoring. In short, our event reporting platform helps requesters recruit ideal reporters, select highly relevant data from an evolving picture stream, and receive accurate responses. This solution mainly incorporates: 1) a strategic and generic recruitment algorithm for recruiting and scheduling suitable reporters to events; 2) a deep learning model that eliminates false submissions and ensures photo’s credibility; and 3) an A-tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. Experiment results investigate the performances of the proposed recruitment approach and show that our algorithm outperforms two other benchmarking approaches. Also, we conduct simulations to evaluate the strategies of the proposed recruitment algorithm, given different fairness levels among events. Data quality simulation results show effectiveness in reducing false submissions and delivering high-quality responses. Finally, framework implementation for real-world applications is provided.

Hybrid Neural Network for Sina Weibo Sentiment Analysis

Abstract: Sina Weibo sentiment analysis technology provides the methods to survey public emotion about the related events or products in China. Most of the current works in sentiment analysis are to apply neural networks, such as convolution neural network (CNN), long short-term memory (LSTM), or C-LSTM. In this article, a novel structure of a hybrid neural network model is proposed to deal with the polysemy phenomena of words and topic confusion with Sina Weibo. First, the embeddings from language models (ELMo) and some statistical methods based on the corpus and sentiment lexicon are employed to extract the features. This method uses latent semantic relationships in different linguistic contexts and cooccurrence statistical features between words in Weibo. Second, for the classification model, unlike traditional C-LSTM which feeds CNN’s output into LSTM, we employ several filters with variable window sizes to extract a sequence of high-level word representation in different granularity distributions of text data in multichannel CNN. At the same time, obtain the sentence representation in Bi-LSTM. Then, concatenate the outputs of multichannel CNN and Bi-LSTM. In conclusion, the results indicate that the proposed model performs better on the precision, recall, and F1-score for Weibo sentiment analysis.

Docschain: Blockchain-Based IoT Solution for Verification of Degree Documents

Abstract: Degree verification is the process of verifying the academic credentials of successfully graduated students. It is a time-consuming and costly process as universities annually spend millions of dollars on handling the degree verification requests. Hence, there is a dire need to improve the degree verification process, and the Massachusetts Institute of Technology, Cambridge, MA, USA, has introduced the blockcerts, a blockchain-based solution for freely handling the degree verification requests. Although blockcerts eliminates the cost of the degree verification process, it also alters the existing workflow of degree issuance. This is because blockcerts are primarily focused on facilitating the students, and there is room for improvement from the perspective of educational institutes. In this article, we have introduced the docschain to tackle the three mentioned limitations of the blockcerts. Docschain seamlessly incorporates within the existing workflow of degree issuance by operating over the hard copies of the degree documents. This is achieved through optical character recognition (OCR), and the record of each degree document is stored along with the details of the corresponding OCR template to understand the semantics of the data stored at different sections of the degree document. In contrast to blockcerts, docschain also supports the bulk submission of degree details for both the previously and newly graduated students.

Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network

Abstract: Malicious social bots generate fake tweets and automate their social relationships either by pretending like a follower or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweet in order to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, a learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes’ theorem, and the indirect trust is derived from the Dempster–Shafer theory (DST) to determine the trustworthiness of each participant accurately. Experimentation has been performed on two Twitter data sets, and the results illustrate that the proposed algorithm achieves improvement in precision, recall, F-measure, and accuracy compared with existing approaches for MSBD.

Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation

Abstract: In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time The results can be used in social cognition, person reidentification, and human–machine interactions

C-Blondel: An Efficient Louvain-Based Dynamic Community Detection Algorithm

Abstract: One of the most interesting topics in the scope of social network analysis is dynamic community detection, keeping track of communities’ evolutions in a dynamic network. This article introduces a new Louvain-based dynamic community detection algorithm relied on the derived knowledge of the previous steps of the network evolution. The algorithm builds a compressed graph, where its supernodes represent the detected communities of the previous step and its superedges show the edges among the supernodes. The algorithm not only constructs the compressed graph with low computational complexity but also detects the communities through the integration of the Louvain algorithm into the graph. The efficiency of the proposed algorithms is widely investigated in this article. By doing so, several evaluations have been performed over three standard real-world data sets, namely Enron Email, Cit-HepTh, and Facebook data sets. The obtained results indicate the superiority of the proposed algorithm with respect to the execution time as an efficiency metric. Likewise, the results show the modularity of the proposed algorithm as another effectiveness metric compared with the other well-known related algorithms.

Parallel Societies: A Computing Perspective of Social Digital Twins and Virtual–Real Interactions

Abstract: Welcome to the first issue of the IEEE Transactions on Computational Social Systems (TCSS) of 2020, and Happy New Year to You! We would like to take this opportunity to express our sincere thanks to our editors, reviewers, authors, and readers for your great support and effort devoted to the TCSS, along with our best wish and hope that everyone has a happy, healthy, and fruitful 2020.

A Personalized Learning System for Parallel Intelligent Education

Abstract: Technological advancement has given education a new definition— parallel intelligent education —resulting in fundamentally new ways of teaching and learning. This article exemplifies an important component of parallel intelligent education—artificial education system in a narrative game environment to offer personalized learning. The system collects data on the player’s actions while they play, assessing their concept knowledge via k-nearest-neighbor (kNN) classification, and provides tailored feedback to that student as they play the game. Based on an empirical evaluation, the kNN-based game system is shown to accurately provide players with differentiated instructions to guide them through the learning process based on the estimation of their knowledge levels.

Link Prediction Adversarial Attack Via Iterative Gradient Attack

Abstract: Increasing deep neural networks are applied in solving graph evolved tasks, such as node classification and link prediction. However, the vulnerability of deep models can be revealed using carefully crafted adversarial examples generated by various adversarial attack methods. To explore this security problem, we define the link prediction adversarial attack problem and put forward a novel iterative gradient attack (IGA) strategy using the gradient information in the trained graph autoencoder (GAE) model. Not surprisingly, GAE can be fooled by an adversarial graph with a few links perturbed on the clean one. The results on comprehensive experiments of different real-world graphs indicate that most deep models and even the state-of-the-art link prediction algorithms cannot escape the adversarial attack, such as GAE. We can benefit the attack as an efficient privacy protection tool from the link prediction of unknown violations. On the other hand, the adversarial attack is a robust evaluation metric for current link prediction algorithms of their defensibility.

Computational Social Simulation With E-CARGO: Comparison Between Collectivism and Individualism

Abstract: Computational social simulation is a long-term, cutting-edge topic in the interdisciplinary field where information technology, computer science, social science, and sociology overlap. In this article, we establish the fundamental requirements for social simulation and demonstrate that the Environments—Classes, Agents, Roles, Groups, and Objects (E-CARGO) model for role-based collaboration (RBC) and the subsequent group role assignment (GRA) optimization model are highly qualified to meet these requirements. Based on E-CARGO and GRA, we propose a new approach to social simulation and conduct a case study to verify this approach. This case study involves a comparison between collectivism and individualism. The contribution of this work is a novel approach to social simulation using E-CARGO and GRA. This approach reveals the exciting results that explain social phenomena, e.g., collectivism is better than individualism if the team manager is perfect in the evaluation process, and individualism can beat collectivism without much difficulty if the team manager is not perfect.

Automatic Bug Triage in Software Systems Using Graph Neighborhood Relations for Feature Augmentation

Abstract: Bug triaging is the process of prioritizing bugs based on their severity, frequency, and risk in order to be assigned to appropriate developers for validation and resolution. This article introduces a graph-based feature augmentation approach for enhancing bug triaging systems using machine learning. A new feature augmentation approach that utilizes graph partitioning based on neighborhood overlap is proposed. Neighborhood overlap is a quite effective approach for discovering relationships in social graphs. Terms of bug summaries are represented as nodes in a graph, which is then partitioned into clusters of terms. Terms in strong clusters are augmented to the original feature vectors of bug summaries based on the similarity between the terms in each cluster and a bug summary. We employed other techniques such as term frequency, term correlation, and topic modeling to identify latent terms and augment them to the original feature vectors of bug summaries. Consequently, we utilized frequency, correlation, and neighborhood overlap techniques to create another feature augmentation approach that enriches the feature vectors of bug summaries to use them for bug triaging. The new modified vectors are used to classify bug reports into different priorities. Bug Triage in this context is to correctly recognize the priority of new bugs. Several classification algorithms are tested using the proposed methods. Experimental results on a data set with Eclipse bug reports extracted from the Bugzilla tracking system have shown that our approach outperformed the existing bug triaging systems including modern techniques that utilize deep learning.

Multiobjective Particle Swarm Optimization Based on Network Embedding for Complex Network Community Detection

Abstract: Community detection in complex networks is significant to social network analysis. Most of the algorithms take advantage of single-objective optimization methods, which may not be effective for complex networks. Compared with single-objective algorithms, multiobjective evolutionary algorithms can avoid local optimization. However, multiobjective evolutionary algorithms often encounter problems of excessive search space and low efficiency. To solve these issues, this study introduces network embedding into the multiobjective particle swarm algorithm and maps nodes into a low-latitude space, thereby effectively reducing the search space while increasing search efficiency via a consensus propagation strategy. Experimental results demonstrate that a novel effective algorithm based on multiobjective particle swarm optimization (NE-PSO) performs effectively and has competitive performance in comparison with state-of-the-art approaches on synthetic and real-world networks, especially the large-scale ones.

Specification Patterns of Service-Based Applications Using Blockchain Technology

Abstract: With the fast development of information technologies, traditional value-added chain business models are shifting to service-based applications (SBAs) to cope with ever-changing user behaviors and the modern social system. An SBA allows an organization to utilize external and distributed resources to achieve its business goals, especially when the Internet of Thing (IoT) will soon become mainstream. However, the development of SBAs is at its early stage with a number of unsolved issues, such as the availability of effective methods for services selection and composition, semantic representation of specifications, and security assurances. This article aims to address two main issues in SBAs: 1) the standardization of formulation and 2) the security assurance. A systematic method is proposed to formulate the specifications of services, the prevalent blockchain technology (BCT) is adopted to enable SBAs, and the proposed specification patterns and BCT are integrated as a BCT-based quality-of-service (QoS) framework to support service selections and workflow compositions.

Activity Minimization of Misinformation Influence in Online Social Networks

Abstract: In recent years, online social media has flourished, and a large amount of information has spread through social platforms, changing the way in which people access information. The authenticity of information content is weakened, and all kinds of misinformation rely on social media to spread rapidly. Network space governance and providing a trusted network environment are of critical significance. In this article, we study a novel problem called activity minimization of misinformation influence (AMMI) problem that blocks a node set from the network such that the total amount of misinformation interaction between nodes (TAMIN) is minimized. That is to say, the AMMI problem is to select $K$ nodes from a given social network $G$ to block so that the TAMIN is the smallest. We prove that the objective function is neither submodular nor supermodular and propose a heuristic greedy algorithm (HGA) to select top $K$ nodes for removal. Furthermore, in order to evaluate our proposed method, extensive experiments have been carried out on three real-world networks. The experimental results demonstrate that our proposed method outperforms comparison approaches.

Two-Path Deep Semisupervised Learning for Timely Fake News Detection

Abstract: News in social media, such as Twitter, has been generated in high volume and speed. However, very few of them are labeled (as fake or true news) by professionals in near real time. In order to achieve timely detection of fake news in social media, a novel framework of two-path deep semisupervised learning (SSL) is proposed where one path is for supervised learning and the other is for unsupervised learning. The supervised learning path learns on the limited amount of labeled data, while the unsupervised learning path is able to learn on a huge amount of unlabeled data. Furthermore, these two paths implemented with convolutional neural networks (CNNs) are jointly optimized to complete SSL. In addition, we build a shared CNN to extract the low-level features on both labeled data and unlabeled data to feed them into these two paths. To verify this framework, we implement a Word CNN-based SSL model and test it on two data sets: LIAR and PHEME. Experimental results demonstrate that the model built on the proposed framework can recognize fake news effectively with very few labeled data.

On Structural Features, User Social Behavior, and Kinship Discrimination in Communication Social Networks

Abstract: In the research of social networks, their structural characteristics, user social behaviors, and user relationships are elements that are important to understand social networks, to predict user behaviors, and to manage social networks. In this article, we took as the research object of social networks the mobile communication network, which is closely connected with people’s real lives. We studied the structural characteristics using the complex network analysis methods and derived the laws that exist in the structure of communication social networks. We analyzed users’ social behaviors or social patterns from the perspective of age, gender, social scope, age differences, and time. In addition, we extracted various salient features of user’s calling behavior, and used the XGBoost and logistic regression (LR) fusion method to establish the Kinship-XL model, which is able to improve the performance and speed. Through the experimental verification, the Kinship-XL model can determine whether there is a kinship between users or not.

MODEL: Motif-Based Deep Feature Learning for Link Prediction

Abstract: Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%).