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.

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.

A User-Recommendation Method Based on Social Media

User-analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user rec- ommendation algorithm that takes into account microblog length, relativity between microblog and users, and familiarity between users. The experimental results show that this multidi- mensional algorithm is more accurate than a traditional recom- mendation algorithm.

Identification of Influential Users in Online Social Network: A Brief Overview

Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.

Influencing factors of answer adoption in social Q&A communities from users' perspective: Taking Zhihu as an example

Purpose: Taking Zhihu as the object for a case study, we intend to analyze the key factors that have affected users on adopting answers in social Q&A(SQA) websites.Design/methodology/approach: With information adoption model(IAM) as the theoretical foundation and widely accepted evaluation criteria for answer quality in SQA sites as variables, we constructed a factor model that has influenced SQA community users to adopt offered answers. With the partial least squares(PLS) technique, our model was then empirically tested through a sample of 311 Zhihu users.Findings: Our results showed that answer usefulness is the most effective variable, and answer interactivity and answer entertainment both have positive and significant impacts on users’ attitude to adopt answers in an SQA community. Except for novelty, other three components of answer quality, i.e. knowledge, reliability, and solution to the problem have all significant effect on answer usefulness.Research limitations: First, due to the limited sample size, it is still questionable if our research results based on Zhihu could be applied to other SQA communities. Second, our questionnaires were mainly designed to investigate how users felt about the answers in an SQA site, but did not differentiate the content of the answer itself.Practical implications: As a three-year-old SQA platform, Zhihu has developed very quickly with its high-quality answers and public intellectual users, and has been regarded as one of the representatives of fast emerging Chinese SQA communities in recent years. Our studycould help shed light on users’ information sharing and knowledge adoption behaviors in a Chinese SQA site, such as Zhihu. Originality/value: Compared with previous studies on answer quality assessments in SQA sites and on information adoption model, to the best of our knowledge, this is one of the pioneer studies which combined answer qualities with users’ intention of adopting SQA answers. Our study on user answer adoption in Zhihu community could further develop the theory of IAM. This study showed that answer usefulness is the most important motivation of Zhihu users in the process of adopting answers.

Factors influencing knowledge contribution:An empirical investigation of social networking website users

Purpose: In the Web 2.0 era,leveraging the collective power of user knowledge contributions has become an important part of the study of collective intelligence. This research aims to investigate the factors which influence knowledge contribution behavior of social networking sites(SNS) users.Design/methodology/approach: The data were obtained from an online survey of 251 social networking sites users. Structural equation modeling analysis was used to validate the proposed model.Findings: Our survey shows that the individuals' motivation for knowledge contribution,their capability of contributing knowledge,interpersonal trust and their own habits positively influence their knowledge contribution behavior,but reward does not significantly influence knowledge contribution in the online virtual community.Research limitations: Respondents of our online survey are mainly undergraduate and graduate students. A limited sample group cannot represent all of the population. A larger survey involving more SNS users may be useful.Practical implications: The results have provided some theoretical basis for promoting knowledge contribution and user viscosity.Originality/value: Few studies have investigated the impact of social influence and user habits on knowledge contribution behavior of SNS users. This study can make a theoretical contribution by examining how the social influence processes and habits affect one's knowledge contribution behavior using online communities.

Connotea中Social Tagging机制研究

选取Connotea为研究对象,统计分析标签、被标引资源、标引者两两关联关系,发现标签覆盖资源范围较广,标签共现现象突出,部分用户标引活跃,资源平均标签数较低,用户对内容关注度规律性转移,科研领域的用户比较多地使用规范词。提出增加标引词结构关联和细粒内容定位标引。

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.

Geo-Social Profile Matching Algorithm for Dynamic Interests in Ad-Hoc Social Network

Among mobile users, ad-hoc social network (ASN) is becoming a popular platform to connect and share their interests anytime anywhere. Many researchers and computer scientists investigated ASN architecture, implementation, user experience, and different profile matching algorithms to provide better user experience in ad-hoc social network. We emphasize that strength of an ad-hoc social network depends on a good profile-matching algorithm that provides meaningful friend suggestions in proximity. Keeping browsing history is a good way to determine user’s interest, however, interests change with location. This paper presents a novel profile-matching algorithm for automatically building a user profile based on dynamic GPS (Global Positing System) location and browsing history of users. Building user profile based on GPS location of a user provides benefits to ASN users as this profile represents user’s dynamic interests that keep changing with location e.g. office, home, or some other location. Proposed profile-matching algorithm maintains multiple local profiles based on location of mobile device.

角色演变视角下辟谣信息对社交媒体意见领袖形成的影响——基于舆论领导法则

[目的/意义]探究辟谣信息对社交媒体用户的影响,特别是对意见领袖形成现象机制的挖掘。构建社交媒体用户角色演变行为模型,研究人员可以更好地了解意见领袖的形成原因,刻画辟谣信息对社交媒体用户的作用机制,有助于对参与辟谣的“准意见领袖”进行合理引导,以提高社交媒体舆情管理的效率和准确性。[方法/过程]首先,对微博社交媒体平台中的特定主题事件进行跟踪,识别并筛选出“新生意见领袖”群体,并利用质性访谈对此群体进行深度访谈。然后,基于程序化扎根理论方法,利用NVivo 12软件进行扎根编码构建模型,并通过理论饱和度检验验证模型的完整性。最后,对社交媒体用户角色演变的原因进行深入分析和探讨。[结果/结论]在辟谣信息作用下,个体主观因素、社会环境因素和辟谣信息因素对一般用户向新生意见领袖的演变行为产生重要影响,其中个体主观因素在辟谣信息的作用下对意见领袖形成的影响更加显著,自我认知清晰、高情绪需求且情绪稳定的个体更容易成为影响力持久的意见领袖;社会公众认可、实质性奖励和政策法律支持共同构成社会环境因素,是意见领袖形成的重要保障;辟谣信息因素中的辟谣信息热度、可信度和发布质量等方面都会直接影响“潜在意见领袖”用户群体对辟谣事件的把握,进而干预其参与辟谣的决定。

Studying Design and Use of Healthcare Technologies in Interaction: The Social Learning Perspective in a Dutch Quality Improvement Collaborative Program

Designing technologies is a process that relies on multiple interactions between design and use contexts. These interactions are essential to the development and establishment of technologies. This article seeks to understand the attempts of healthcare organisations to integrate use contexts into the design of healthcare technologies following insights of the theoretical approaches of social learning and user representations. We present a multiple case study of three healthcare technologies involved in improving elderly care practice. These cases were part of a Dutch quality improvement collaborative program, which urged that development of these technologies was not “just” development, but should occur in close collaboration with other parts of the collaborative program, which were more focused on implementation. These cases illustrate different ways to develop technologies in interaction with use contexts and users. Despite the infrastructure of the collaborative program, interactions were not without problems. We conclude by arguing that interactions between design and use are not naturally occurring phenomena, but must be actively organised in order to create effects.

UGC-Driven Social Influence Study in Online Micro- Blogging Sites

In Web 2.0 era,the content on a web page is increasingly generated by end users,rather than limited number of administrators.Hence,large number of User Generated Content(UGC) has driven the explosion of content in the web.Thanks to UGC,the pattern of web usage has evolved from download dominated way to a hybrid one with both information download and upload.Large number of UGC has unveiled great capacity of information that is unavailable for researchers before,such as individual preferences,social connections,and etc.In this paper,we propose a novel model which studies the UGC in micro-blogging web sites,the largest and fastest information diffusion media online,and evaluate the social influence for an arbitrary individual.Experimental results show that our model outperforms state-of-the-art techniques in social influence evaluation in both the running time and accuracy.

基于角色理论的档案社交媒体用户角色建构研究

Web2.0时代下,档案社交媒体的广泛应用改变了档案用户的活动空间和行为模式,使其面临角色加持和角色转变的挑战。角色理论为研究档案社交媒体用户的行为和态度提供了新的范式和视角。基于此,论文从角色理论视角探讨档案社交媒体用户角色建构的内涵,并在此基础上分析现阶段我国档案社交媒体用户由于档案意识薄弱、主体互动缺乏、档案专业技能欠缺造成的角色不清、角色偏差和角色中断等角色扮演困境,结合实际进一步指出角色定位明晰化、角色实践深入化、角色互动社群化、角色类型多元化的未来愿景,为此需要采取外部引导、内部调适为一体的角色建构策略,包括发挥档案主体能动作用、提升档案信息素养、开拓档案社交媒体用户活动空间、加强档案社交媒体用户角色调适。

支持或反对:社交媒体用户观点的形成机理

探究社交媒体用户观点形成机理,揭示影响网民观点形成的重要因素,可深入理解网络用户的行为模式,洞察网络舆论本质。文章以“双减”话题微博数据为数据源,基于主题模型和机器学习的情感分析方法挖掘用户观点;从意见氛围、社交关系、前期行为特征和用户属性等维度构造特征变量,基于LightGBM建立不同主题下的情感预测模型,采用SHapley Additive exPlanations(SHAP)对特征重要性进行排序,发现影响网民观点形成的重要因素。研究发现:前期行为特征对用户观点形成的影响最大,意见氛围次之,社交关系即好友关系影响较弱;个体在情感倾向上更易跟随规模较大的传播主体,而不是崇拜权威。

On-Line Privacy Behavior: Using User Interfaces for Salient Factors

The problem of privacy in social networks is well documented within literature;users have pri- vacy concerns however, they consistently disclose their sensitive information and leave it open to unintended third parties. While numerous causes of poor behaviour have been suggested by re- search the role of the User Interface (UI) and the system itself is underexplored. The field of Per- suasive Technology would suggest that Social Network Systems persuade users to deviate from their normal or habitual behaviour. This paper makes the case that the UI can be used as the basis for user empowerment by informing them of their privacy at the point of interaction and remind- ing them of their privacy needs. The Theory of Planned Behaviour is introduced as a potential theoretical foundation for exploring the psychology behind privacy behaviour as it describes the salient factors that influence intention and action. Based on these factors of personal attitude, subjective norms and perceived control, a series of UIs are presented and implemented in con- trolled experiments examining their effect on personal information disclosure. This is combined with observations and interviews with the participants. Results from this initial, pilot experiment suggest groups with privacy salient information embedded exhibit less disclosure than the control group. This work reviews this approach as a method for exploring privacy behaviour and propos- es further work required.

A Semantic Model for Socially Aware Objects

The Internet of Things (IoT) assumes that things interact and exchange information thus defining the future of pervasive computing environments. The integration between people and interconnected objects realizes a new physical and social space and opens new frontiers in context awareness and objects adaptation. In this paper we investigate the possibility of creating socially aware objects able to interact not only among themselves but also with human beings sharing the same environment. The main contribution of this work is to provide a knowledge model for social context-awareness and reasoning using an ontology-based context modeling, a user model and exploiting of social networks. This model is part of a larger framework called So Smart that aims at empowering networks of interconnected objects with social context awareness in order to improve their social interaction with people.

基于改进HK模型的社交用户观点演化预测研究

[目的/意义]准确预测舆情观点的演化态势,对化解舆情危机具有重要意义。[方法/过程]针对HK模型用于用户观点演化预测存在的不足,进行如下改进:首先,从和当前事件相似度高于阈值的历史博文中获取用户的初始观点值;其次,利用用户间交互行为以及关注关系计算亲密度,修订用户信任阈值;最后,融合用户全局影响力、用户间交互度以及观点接近度计算用户间的综合信任度,突出用户间观点影响权重的差异性。最终利用改进的HK模型实现对社交用户观点演化预测。[结果/结论]实验结果表明,改进的HK模型用于观点演化预测具有较低的MSE值和MAE值,可为舆情发展提供有效预测。

Topological and Historical Considerations for Infectious Disease Transmission among Injecting Drug Users in Bushwick, Brooklyn (USA)

Recent interest by physicists in social networks and disease transmission factors has prompted debate over the topology of degree distributions in sexual networks. Social network researchers have been critical of “scale-free” Barabasi-Albert approaches, and largely rejected the preferential attachment, “rich-get-richer” assumptions that underlie that model. Instead, research on sexual networks has pointed to the importance of homophily and local sexual norms in dictating degree distributions, and thus disease transmission thresholds. Injecting Drug User (IDU) network topologies may differ from the emerging models of sexual networks, however. Degree distribution analysis of a Brooklyn, NY, IDU network indicates a different topology than the spanning tree configurations discussed for sexual networks, instead featuring comparatively short cycles and high concurrency. Our findings suggest that IDU networks do in some ways conform to a “scale-free” topology, and thus may represent “reservoirs” of potential infection despite seemingly low transmission thresholds.

利用邻近度与内容特征的用户识别方法

社交网络对用户拓扑结构进行了获取限制,使得利用结构特征进行识别的方法准确率大大下降。利用邻近度与内容特征的用户识别方法构建了一个融合属性特征、结构特征与内容特征的基于XGboost的半监督网络模型,将跨社交网络用户识别问题转换为二分类问题。针对无法获得完整用户拓扑结构与种子用户不足的问题,提出显式好友与隐式好友的提取方法,根据待匹配用户对好友网络中的显式匹配用户对、隐式匹配用户对与其他好友将好友网络融合,结合用户重要度改进LINE算法二阶邻近度的经验概率,获取待匹配用户对的结构特征;将用户发文时间序列特征、生成内容关键词重叠度特征与关注用户标签特征作为生成内容特征;最后将属性特征、结构特征与内容特征进行融合完成用户识别。在真实数据集上的实验证明了本方法的有效性。

基于异质图注意力网络与多特征融合的跨社交媒体用户识别研究

跨社交媒体用户识别对于网络舆情的协同治理以及用户偏好的全方位识别与预测具有重要的指导意义。针对现有方法存在数据表达能力弱、忽略用户信息的动态性和关联性的问题,本文提出一种融合异质特征嵌入与实体动态关联的跨社交媒体用户识别模型。首先,整合用户的基本属性、生成内容和社交结构信息,构建各个社交媒体的异质信息网络;其次,通过设计新的元路径识别策略构造邻接矩阵,使用异质图注意力网络模型汇聚用户节点信息,增强节点特征的表示能力;再其次,引入了3种连续时间衰减函数,对跨社交媒体的实体相似矩阵进行加权,增强实体之间的动态关联;最后,融合单社交网络和跨社交网络中的以上特征,利用多层感知机实现跨社交媒体用户的识别和预测。在微博-知乎真实数据集中的研究结果显示,本文模型的整体性能优于其他基准模型,特别是线性衰减函数,其展现了最佳效果,且本文提出的元路径识别策略对提升用户识别效果具有重要作用。