Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data

The study examined a decision tree analysis using social big data to conduct the prediction model on types of risk factors related to cyberbullying in Korea. The study conducted an analysis of 103,212 buzzes that had noted causes of cyberbullying and data were collected from 227 online channels, such as news websites, blogs, online groups, social network services, and online bulletin boards. Using opinion-mining method and decision tree analysis, the types of cyberbullying were sorted using SPSS 25.0. The results indicated that the total rate of types of cyberbullying in Korea was 44%, which consisted of 32.3% victims, 6.4% perpetrators, and 5.3% bystanders. According to the results, the impulse factor was also the greatest influence on the prediction of the risk factors and the propensity for dominance factor was the second greatest factor predicting the types of risk factors. In particular, the impulse factor had the most significant effect on bystanders, and the propensity for dominance factor was also significant in influencing online perpetrators. It is necessary to develop a program to diminish the impulses that were initiated by bystanders as well as victims and perpetrators because many of those bystanders have tended to aggravate impulsive cyberbullying behaviors.     

Virus infection control in online social networks based on probabilistic communities

With the rapid progress of the Internet, more and more people socialize and make new friends through online social network sites and applications, such as Facebook, Twitter, and MSN Messenger. The number of users of these online social network sites and applications has increased significantly within a short period. Unfortunately, online social networks are often the platforms that propagate malicious software, such as viruses. The malicious software is spread by infected users who automatically send fake requests to other users. After accepting the fake requests, the users are infected. In a realistic environment, everyone can decide whether to accept or decline a request, and thus, we study how to construct and use community structures to efficiently control virus propagation in online social networks whose users each have a probability, namely, inclination, of accepting a request. In this paper, a community detection algorithm is proposed to detect communities in online social networks. In addition, a number of users are selected from the communities for patching and controlling virus propagation. Simulation results show that our proposed method provides good performance in terms of the number of distributed patches.Copyright © 2013 John Wiley & Sons, Ltd.     

多元舆情智能检测系统设计

随着近年诸如网络自媒体、个人公众号的迅猛发展,其安全形势日益严峻。用户在浏览各种网络信息时,许多扰乱公众视线的信息也层出不穷。这些信息既可能包含恶意篡改图像内容,也可能包含负面文字信息,它们在某种程度上会影响公众的判断,造成不必要的社会恐慌。为此,本系统将深度学习技术以及经典算法检测用于负面评论和恶意篡改图像信息的检测,对负面的文本、图像信息做到及时发现、准确检测,以解决用户在浏览网页时无法判断信息内容是否含有虚假图像以及负面评论的问题。     

Predicting continuity of online conversations on Reddit

Online conversation has been facilitating human society, which has encouraged people not only to share news, products, or daily events, but also to develop ideas. A popular type of online conversation is threaded conversation, where a person initiates a conversation with a new topic by uploading a post, then others reply to the post or the comments made by other participants in a recursive manner. Even though the growing importance of understanding and predicting the development process of threaded conversations has attracted research community to investigate continuity of online threaded conversations from a perspective of individual posts or comments, little work has focused on predicting the continuity or persistence of online conversations. In this paper, we propose a deep learning model to predict continuity of threaded conversations – whether there will be a newly-arrived comment or not. Cooperating with popular pre-trained text embedding models and graph neural network models, the proposed model captures text, structural, and temporal characteristics of the threaded conversation by an observable time for the final prediction. We evaluate the proposed model with two different types of the threaded conversations, self-introductory Q&A and discussion, which demonstrates that the proposed model can accurately predict the continuity of the conversations regardless of types of threaded conversations. We believe that the proposed methodology and the results can provide the potential insights to advertisers, opinion leaders, or platform designers who want to understand and predict the evolutionary process of threaded conversations.     

基于信任节点辅助的安全协同频谱感知策略

在认知无线电网络中,多个用户相互协作进行频谱感知能有效地提高系统感知性能。然而这种协同方式也带来了新的安全隐患:当恶意用户出现时,现有协同感知方法无法确保感知结果的鲁棒性。本文针对这一问题,提出了一种基于信任节点辅助的安全协同感知策略。该策略通过借助网络中信任节点的感知结果,在用户域和时间域两个维度上消除恶意用户的影响,确保了算法在较多恶意用户环境中的稳定性。仿真结果表明,新算法的性能优于Kaligineedi所提算法,在恶意用户数目为网络用户总数一半时,仍能有效地进行协同感知,具有良好的鲁棒性。      

基于语义倾向性的视频安全监测研究

随着网络技术和应用迅猛发展,大量的不良视频图像在网上传播,极大地危害了社会的稳定和人们的身心健康,如何有效监测有害视频成为关注的重点。针对这个问题,提出了一种改进的文本安全检测方法。该检测方法首先采用基于语义倾向性分析对字幕文本进行分类,然后将分类结果与用户需求库比较并通过阈值判断是否报警,从而达到监测有害视频的目的。其中文本检测方法是通过分析上下文计算出极性词的修饰极性,并考虑到名词的影响,最后得到整篇文本的倾向性。实验结果表明,该方法能够有效的监测到有害视频。     

TAEffect: Quantifying interaction risks in trust-enabled communication systems

The interactive risks of different devices, serving as clients or servers, have increasingly attracted huge attention in various communication systems, such as wireless sensor networks, wireless communication networks, and mobile crowd-sensing. At present, lots of countermeasures had been proposed and deployed accordingly. Nevertheless, the investigation on the interaction risks between different devices is still very limited to date. In this paper, we propose a novel adverse effect inference mechanism TAEffect for malicious behaviors of devices emerged in various decentralized and open communication systems/networks through network-percolation theory. At first, four typical malicious interactional behaviors are mapped into four topologies, then, upon which a network influence-inspired approach is employed to quantify the adverse effect. Finally, multifacet experiments using five real-world datasets and a synthetic testbed are performed to validate the efficiency and effectiveness. The experimental results show our proposed approach is significant and rational to quantitatively calculate and qualitatively mirror the four kinds of malicious interactional behaviors in diverse misbehavior-emerged communication systems.     

基于卷积神经网络的旅游推荐模型设计

旅游业和网络时代高速发展,导致旅游信息过载问题日益严重,旅游推荐方法对解决信息过载问题十分重要。传统推荐算法只针对用户和项目之间的评分和基本属性计算相似度进行推荐,但行为需求及具有游客情感因素的评论却被忽视。本文利用卷积神经网络(CNN)对文本评论特征提取进行情感分类,用皮尔逊相似度公式计算相似的用户群体,用平均绝对误差(MAE)对结果误差进行评价。与传统的协同过滤方法进行对比,本文提出的模型能有效降低预测误差。     

Temporal Topic-Based Multi-Dimensional Social Influence Evaluation in Online Social Networks

Analytical applications in online social networks can be generalized as the influence evaluation problem, which targets at finding most influential users. Nowadays social influence evaluation is still an open and challenging issue. Most influence evaluation models focus on the single dimensional evaluation factor but fail to research on the multi-dimensional factors. In this paper, we propose a novel influence evaluation model: the temporal topic influence (TTI) evaluation model, which is a time-aware, content-aware and structure-aware evaluation model. For the aim of multi-dimensional evaluation, we incorporate multi-dimensional measure factors into our model, including the time factor, the topological information and the topic distribution information, etc. We propose a novel concept of user gravitational ability which is inspired by Newton’s law of universal gravitation. It can integrate multi-dimensional factors in an appropriate way. Our experiments are conducted on the Sina Weibo data set. Through the experimental analysis, we prove TTI model can calculate users’ influences effectively and efficiently. The TTI model can distinguish the value of users’ influences. And the TTI model identifies the top-k influential users with higher quality. We also validate the effect of time and topic measure factors in the influence evaluation process.     

Cooperative spectrum sensing in the presence of primary user emulation attack in cognitive radio network: multi-level hypotheses test approach

The inherent nature of cognitive radio (CR) networks has brought new threats to wireless communications. Primary user emulation attack (PUEA) has been widely studied as a serious threat to cooperative spectrum sensing (CSS) in CR networks. In PUEA, a malicious user can obstruct CR users from accessing idle frequency bands by imitating licensed primary user (PU) signal characteristics. The present study introduces a new CSS scheme in the presence of a malicious PUEA based on multi-level hypothesis testing (MLHT). In the proposed method, generalizing from binary hypothesis testing to MLHT, we partition the decision space to four decision options and apply minimum Bayes cost criteria to determine the channel status. We also discuss practical limitation issues that need to be considered when applying the MLHT approach. Simulation results are provided to indicate the performance improvement of the proposed MLHT method against PUEA, compared with the conventional method.     

Cyberbullying perpetration and victimization among children and adolescents: A systematic review of longitudinal studies

In this systematic review of exclusively longitudinal studies on cyberbullying perpetration and victimization among adolescents, we identified 76 original longitudinal studies published between 2007 and 2017. The majority of them approached middle school students in two waves at 6 or 12 months apart. Prevalence rates for cyberbullying perpetration varied between 5.3 and 66.2 percent, and for cyberbullying victimization between 1.9 and 84.0 percent. Person-related factors (e.g., traditional bullying, internalizing problems) were among the most studied concepts, primarily examined as significant risk factors. Evidence on the causal relationships with media-related factors (e.g., (problematic) Internet use), and environmental factors (e.g., parent and peer relations) was scarce. This review identified gaps for future longitudinal research on cyberbullying perpetration and victimization in childhood and adolescence.     

Cyberbullying: Effect of emergency perception on the helping tendencies of bystanders

The effect of the emergency perception of bystanders of cyberbullying victims on helping behaviors is often neglected in research on cyberbullying. In this study, we explored the influence of this cognitive factor on cyber-bystanders’ helping tendencies as well as elucidated possible underlying processes. The results of two studies were reported. In Study 1, 150 undergraduates read a true case of a girl experiencing cyberbullying. The results indicated that when the participants perceived the victim’s situation to be more critical (i.e., higher emergency perception), their helping tendencies were stronger, partly through increased state empathy followed by feelings of responsibility to help. In Study 2, we randomly assigned 300 undergraduates to two groups. The low emergency group read the same cyberbullying case as Study 1, whereas the cyberbullying case read by the high emergency group contained additional emergency information of the victim. The results indicated that the high emergency group expressed stronger helping tendencies than did the low emergency group. This effect was caused by a stronger perception that the victim was in an emergency situation, which not only strengthened the participants’ helping tendencies directly but also indirectly through increasing their state empathy and feelings of responsibility to help.     

Distributed cooperative spectrum sensing based on reinforcement learning in cognitive radio networks

Spectrum sensing is an initial task for the successful operation of cognitive radio networks (CRN). During cooperative spectrum sensing, malicious secondary user (SU) may report false sensing data which would degrade the final aggregated sensing outcome. In this paper, we propose a distributed cooperative spectrum sensing (CSS) method based on reinforcement learning (RL) to remove data fusion between users with different reputations in CRN. This method regards each SU as an agent, which is selected from the adjacent nodes of CRN participating in CSS. The reputation value is used as reward to ensure that the agent tends to merge with high reputation nodes. The conformance fusion is adopted to promote consensus of the whole network, while it’s also compared with the decision threshold to complete CSS. Simulation results show that the proposed method can identify malicious users effectively. As a result, the whole CRN based on RL is more intelligent and stable.     

Applying trust enhancements to reactive routing protocols in mobile ad hoc networks

Due to the characteristics of mobile ad hoc networks, such networks are more susceptible to the destruction of malicious attacks or denial of cooperation. It would be easy for an adversary or a malicious node to launch attacks on routing function, especially attacks on packet routing. In order to mitigate these hazards, we incorporate the concept of ‘trust’ into MANETs, and abstract a decentralized trust inference model. The core of this model is trust computation, which is divided into two parts: historical trust assessment and trust prediction. We can quantify a node’s historical trust based on its historical behaviors via introducing multiple trust attributes. The fuzzy AHP method based on entropy weights is used to calculate the weight of trust attributes. By making use of the obtained historical trust data sequence, we propose an improved dynamic grey-Markov chain prediction measure to effectively estimate node’s trust prediction. In order to verify the validity of our trust model, we propose a trust-enhanced unicast routing protocol and a trust-enhanced multicast routing protocol, respectively. Both of the two new protocols can provide a feasible approach to kick out the untrustworthy nodes and choose the optimal trusted routing path. Moreover, the new proposed data-driven route maintenance mechanisms can reduce the routing overhead. The persuasive experiments have been conducted to evaluate the effectiveness of the new proposed trust-enhanced routing protocols in the aspects of packets delivery ratio, end-to-end latency, malicious node detection and attack resistance.     

基于终端行为的可信网络连接控制方案

在可信网络连接(TNC)框架下,结合完整性度量方式,通过对终端活动进程的行为属性实时分析并计算终端的"健康度",进而提出实施网络连接控制的方案.与已有的基于终端静态特征的控制方法相比,该方案在识别和隔离潜在安全威胁方面更有效.实验结果表明利用该方案能够实时地将感染恶意代码的终端阻断在网络之外.     

Social comparison as a double-edged sword on social media: The role of envy type and online social identity

Several studies have explored the psychological consequences of social comparison in SNS usage. This paper aims to explore the behavioral outcomes of social comparison through the underlying mechanisms of benign and malicious envy on Facebook. The paper also examines the role of online social identity in predicting benign and malicious envy. Based on multi-wave data collected from 469 Facebook users in Pakistan, we found that Facebook user's social comparison provoked benign and malicious envy; benign envy, in turn, triggered self-improvement intention, and malicious envy triggered negative gossiping. Moreover, user's online social identity moderated the social comparison-envy relationship such that the positive relationship of social comparison and benign envy was stronger, and the positive relationship of social comparison and malicious envy was weaker when the user's online social identity was high. The study contributes to social media literature by examining the behavioral outcomes of social comparison on social media and discusses empirical implications for policymakers, advertisers, SNS providers, SNS designers, educators, and users.     

A message keyword extraction approach by accurate identification of field boundaries

With the recent exponential increase in internet speeds, the traditional network environment is evolving into a high-capacity network environment. Network traffic usage is also increasing exponentially, as are new malicious behaviors and related applications. Most of these applications and malicious behaviors use unknown protocols for which the structure is inaccessible; hence, protocol reverse engineering is receiving increasing attention in the field of network management. Various approaches have been proposed, but they still suffer from misidentification of field boundaries. To understand message structures properly, it is important to identify accurately the boundaries of the fields constituting the protocol message; accurate keyword extraction based on this approach leads to the correct inference of message types, semantics, and state machine. In this study, we propose a message keyword extraction method using accurate identification of field boundaries from delimiter inference and statistical analysis. Through the identification of field boundaries, messages can be subdivided into fields. We evaluate the efficacy of the proposed method by applying it to several textual and binary protocols. The proposed method showed better results than did other previous studies for both textual and binary protocols.     

Modeling and Verification of a Sentiment Analysis System Using Aspect-Oriented Petri Nets

An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets (AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.     

Factors influencing users’ post replying behavior in a senior online community: An empirical investigation

Senior online communities (SOCs) have become an important venue for older people to seek support and exchange information. While online community engagement has been well studied in the existing literature, few studies have explored how older adults behave in online communities. Therefore, drawing upon signaling theory, this study aims to investigate how different content-related and social-related signals influence users’ post replying behavior (i.e., reply to another user’s post) in SOCs. We collected 7486 health-related posts and 71,859 comments from one of the most popular Chinese SOCs, Keai (https://www.keai99.com). Information signals in the posts were operationalized using different techniques such as text mining and social network analysis. Results from negative binomial regression indicated that content-related signals (posts’ topic and length) and social-related signals (authors’ position and centrality) were related to replying behavior. In addition, we revealed some differences between the effects of these signals on informational replies and emotional replies. More specifically, compared to posts mentioning traditional Chinese medicine, posts mentioning western medicine received more informational replies, but less emotional replies. Original posts triggered more informational replies, whereas shared posts attracted more emotional replies. Average reply length was positively related to informational replies, but negatively related to emotional replies. Considering the important role of SOCs in satisfying older adults’ social and informational needs, future research is needed to promote user social engagement in SOCs, thereby maintaining their sustainability.     

A hierarchical approach for resource allocation in hybrid cloud environments

Cloud computing is a key technology for online service providers. However, current online service systems experience performance degradation due to the heterogeneous and time-variant incoming of user requests. To address this kind of diversity, we propose a hierarchical approach for resource management in hybrid clouds, where local private clouds handle routine requests and a powerful third-party public cloud is responsible for the burst of sudden incoming requests. Our goal is to answer (1) from the online service provider’s perspective, how to decide the local private cloud resource allocation, and how to distribute the incoming requests to private and/or public clouds; and (2) from the public cloud provider’s perspective, how to decide the optimal prices for these public cloud resources so as to maximize its profit. We use a Stackelberg game model to capture the complex interactions between users, online service providers and public cloud providers, based on which we analyze the resource allocation in private clouds and pricing strategy in public cloud. Furthermore, we design efficient online algorithms to determine the public cloud provider’s and the online service provider’s optimal decisions. Simulation results validate the effectiveness and efficiency of our proposed approach.