The Intersection of Privacy by Design and Behavioral Economics: Nudging Users towards Privacy-Friendly Choices

This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can leverage “nudges” to encourage users towards privacy-friendly choices. We analyze the limitations of rational choice in the context of privacy decision-making and identify key opportunities for integrating behavioral economics into PbD. We propose a user-centered design framework for integrating behavioral economics into PbD, which includes strategies for simplifying complex choices, making privacy visible, providing feedback and control, and testing and iterating. Our analysis highlights the need for a more nuanced understanding of user behavior and decision-making in the context of privacy, and demonstrates the potential of behavioral economics to inform the design of more effective PbD solutions.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics.The framework models the user behavior as sequences of events representing the user activities at such a network.The represented sequences are thenfitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users.Thus,the model can recognize frequencies of regular behavior to profile the user manner in the network.The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular behavior.The importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the network.Typically detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including users.Therefore,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reflect a normal network workflow.In contrast,the irregular patterns can trigger an alert for a potential cyber-attack.The framework has been fully described where the evaluation metrics have also been introduced.The experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM 1.The paper has been concluded with pro-viding the potential directions for future improvements.

The Research on E-mail Users' Behavior of Participating in Subjects Based on Social Network Analysis

The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.

Evaluation of Microblog Users’ Influence Based on PageRank and Users Behavior Analysis

This paper explores the uses’ influences on microblog. At first, according to the social network theory, we present an analysis of information transmitting network structure based on the relationship of following and followed phenomenon of microblog users. Informed by the microblog user behavior analysis, the paper also addresses a model for calculating weights of users’ influence. It proposes a U-R model, using which we can evaluate users’ influence based on PageRank algorithms and analyzes user behaviors. In the U-R model, the effect of user behaviors is explored and PageRank is applied to evaluate the importance and the influence of every user in a microblog network by repeatedly iterating their own U-R value. The users’ influences in a microblog network can be ranked by the U-R value. Finally, the validity of U-R model is proved with a real-life numerical example.

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

Exploring users' within-site navigation behavior:A case study based on clickstream data

Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a Web forensic framework proposed in the literature and defined the metrics of footprint,track and movement.Data were obtained from user clickstreams provided by a real estate site’s administrators.There were two phases of data analysis with the first phase on navigation behavior based on user footprints and tracks,and the second phase on navigational transition patterns based on user movements.Findings:Preliminary results suggest that the apartment pages were heavily-trafficked while the agent pages and related information pages were underused to a great extent.Navigation within the same category of pages was prevalent,especially when users navigated among the regional apartment listings.However,navigation of these pages was found to be inefficient.Research limitations:The suggestions for navigation design optimization provided in the paper are specific to this website,and their applicability to other online environments needs to be verified.Preference predications or personal recommendations are not made during the current stage of research.Practical implications:Our clickstream data analysis results offer a base for future research.Meanwhile,website administrators and managers can make better use of the readily available clickstream data to evaluate the effectiveness and efficiency of their site navigation design.Originality/value:Our empirical study is valuable to those seeking analysis metrics for evaluating and improving user navigation experience on informational websites based on clickstream data.Our attempts to analyze the log file in terms of footprint,track and movement will enrich the utilization of such trace data to engender a deeper understanding of users’within-site navigation behavior.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

Research on the Influence of Anchor Attributes on Consumers’Online Behaviors in Social E-Commerce Platforms:The Moderating Effect of Platform Contextual Factors

As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.

Web users' language utilization behaviors in China

The paper focuses on the habits of China Web users' language utilization behaviors in accessing the Web. It also seeks to make a general study on the basic nature of language phenomenon with regard to digital accessing. A questionnaire survey was formulated and distributed online for these research purposes. There were 1,267 responses collected. The data were analyzed with descriptive statistics, Chi-square testing and contingency table analyses. Results revealed the following findings. Tagging has already played an important role in Web2.0 communication for China's Web users. China users rely greatly on all kinds of taxonomies in browsing and have also an awareness of them in effective searching. These imply that the classified languages in digital environment may aid Chinese Web users in a more satisfying manner. Highly subject-specific words, especially those from authorized tools, yielded better results in searching. Chinese users have high recognition for related terms. As to the demographic aspect, there is little difference between different genders in the utilization of information retrieval languages. Age may constitute a variable element to a certain degree. Educational background has a complex effect on language utilizations in searching. These research findings characterize China Web users' behaviors in digital information accessing. They also can be potentially valuable for the modeling and further refinement of digital accessing services.

一类BBS网络统计特性实证分析

通过实证,分析了某高校BBS论坛不同时期的网络特征。BBS帖子网络是由两级节点树组成的森林,用户网络是一般形态的复杂网络,其中用户网络度分布服从广延指数分布。静寂期和活跃期用户网络的广延指数值会发生变化。实证结论表明BBS网络度分布不是严格的幂律分布,不同的BBS网络度分布特征存在差异;而且证实同一网络在不同时期的网络度分布特征也会发生变化。

基于群体智能算法的BBS空间集体观点形成模型研究

基于群体智能算法,将在BBS中参与某一话题讨论的网民视为依据一定规则,在一定空间中交互的主体。为此,在定义一些概念的基础上,依照群体智能算法的要求给出BBS空间集体观点的演化规则,并进行实例仿真。实验结果表明,该模型符合实际演化过程,能够用于研究BBS中基于某一话题的集体观点的形成。

基于用户行为的高校BBS热帖预测模型

校园BBS是高校网络舆论的主要载体,反应了大学生的舆论倾向以及生活的各个方面,高校BBS的实证研究具有重要的意义。如何高效地对帖子的热度进行预测是发现突发网络舆情的基础,对网络舆情的研究具有重要的意义。以一高校BBS实际的数据为研究对象,对帖子和用户进行深入分析,提出一种基于用户行为的高校BBS热帖预测模型,通过实验分析,该方法可以对论坛中的热帖进行较好的预测。

Survey of HIV Infection among Injection Drug Users in Guangdong, China

Objective： To understand the prevalence and behavioral risk factors of HIV infection among injection drug users in the Pearl River Delta Region （PRDR） of Guangdong province, and to provide evidence for establishing effective intervention strategies. Methods： Face to face interviews were conducted and serum samples from injection drug users from detoxification centers and the community were collected for HIV screening. Results： 655 drug users were recruited and interviewed. The HIV seropositive rate was 29.0%. 99.5 % of subjects were injection drug users （IDUs）, of whom,75.4% reported sharing injection equipment. Conclusion： HIV prevalence among injection drug users is high in the PRDR of Guangdong. Injection drug use is the principal behavioral risk factor for HIV transmission. Pragmatic harm reduction programs should be implemented to prevent the spread of HIV infection.

高校BBS活跃用户信息行为分析

高校BBS中的活跃用户发帖量大,而且都积极回复他人的帖子。根据所发主题帖数和获得回帖数的不同,可将活跃用户分为领袖型、实力型、热情型和回应型4类。活跃用户的点入度和点出度呈显著正相关关系,活跃用户的回帖数和获回复数显著正相关,活跃用户所发主题帖数与他们回复的主题帖数也显著正相关。

基于布拉德利曲线与BBS干预的煤矿工人不安全行为分析

为了减少煤矿事故发生,针对人的不安全行为问题,用BBS行为安全观察法控制煤矿工人的不安全行为。研究分析了BBS行为安全观察、安全行为学理论、不安全行为及其影响因素等,利用现场调查、行为分析、查找文献资料等方法,基于杜邦布拉德利曲线得到适合煤矿的布拉德利曲线模型,并以此实施BBS行为安全观察法,然后在观察过程中和观察之后分别提出相对应的行为纠正与改善措施以此来控制不安全行为。结果表明以布拉德利曲线为基点来实施BBS行为安全观察法并将其充分应用在煤矿不安全行为控制中,可在遏制煤矿事故发生方面起到至关重要的作用,为煤矿安全管理提供一个切实有效的方案。

青少年网络亲社会行为研究——以上海某综合中学BBS为例

网络亲社会行为是指在网络中发生的符合社会期望的,对他人、群体或者社会有益的,自愿实施的行为。本文通过追踪调查上海某中学的BBS,发现无偿提供信息是青少年主要的亲社会行为。青少年网络亲社会行为呈现出行为主体符号化、相对不确定性与"零"损失;互动形态多样性;行为主题多为无偿提供信息和精神支持,行为结果具有强扩散性等特征。为促进青少年的网络亲社会行为,需要培养青少年的网络素养,规范虚拟社区秩序,培育虚拟社区文化。

高校BBS中学生的网络行为及对策研究——以我院“水木年华”BBS为例

从高校BBS中学生的网络行为特征入手,分析了网络行为的成因,结合我院“水木年华”BBS的实例,探讨了利用BBS开展网络思想政治工作所应采取的工作方式和方法。

加入用户行为因素的BBS热点分析算法仿真

BBS热点挖掘是研究网络舆情传播影响力的重要手段。传统的BBS热点挖掘采用统计特征分析法,不能实现对BBS信息资源的全面覆盖,分析参量单一,热点特征挖掘不准确。提出一种对BBS用户转发评论和提及行为综合分析的BBS热点挖掘算法,建立了BBS热点影响力度量及用户行为综合分析数学模型,进行热点评价及影响力数学度量,实现BBS用户行为综合分析。实验表明采用该算法进行BBS热点跟踪挖掘,提高了BBS网页的召回率,避免出现冗余信息和中止检索,热点挖掘性能和准确度较传统方法提高显著,在网络舆情分析和BBS系统管理等领域具有很好的应用价值。

高校BBS与微博的用户社交行为特征分析

为了扩大信息宣传渠道传播校园正能量,高校会使用官方微博发布BBS的一些关键信息。通过对一个高校BBS微博的研究,实现了一种将微博社交信息反馈至BBS的信息抓取与编辑系统。在此基础上,分析微博用户社交行为特征,提出紧密度和亲密度的概念,很好地实现了用户间的好友关系及关注度。特殊标点符号对微博话题的提取能提供很大的帮助,通过比较基于词典与表情符号和基于不同词典的评论情感分析,得出综合网络词典和表情符号的方法能取得更好效果。