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.

User Behavior Path Analysis Based on Sales Data

With the rapid development of science and technology and the increasing popularity of the Internet,the number of network users is gradually expanding,and the behavior of network users is becoming more and more complex.Users’actual demand for resources on the network application platform is closely related to their historical behavior records.Therefore,it is very important to analyze the user behavior path conversion rate.Therefore,this paper analyses and studies user behavior path based on sales data.Through analyzing the user quality of the website as well as the user’s repurchase rate,repurchase rate and retention rate in the website,we can get some user habits and use the data to guide the website optimization.

Task analysis approach based on perceptual control theory

Based on perceptual control theory,a task analysis approach is proposed to describe more accurately user tasks in dynamic environments,which is of more powerful and flexible descriptive ability. Theoretically,a task meta model is established to describe the interactive process in an individual,dynamic,and flexible way.Methodologically,an implementation framework is illustrated to map the user-oriented description into implementation-oriented models,which will be as a technical tool to transform from a task model to a user interface prototype.

用户行为逆向分析技术研究

通过收集并分析用户在计算机上的各种遗留信息,根据Windows操作系统提供的用户对各种文件的操作行为,设计了一种逆向综合分析算法,可分析出一定时间内用户对计算机的各种行为和操作,从而为发现各种非法和异常行为并加固操作系统提供可靠的支撑信息.

计算机软件项目管理中的需求分析

计算机软件项目管理中的需求分析是提高软件质量的基础也是决定一个软件项目成败的关键。本文介绍了在需求分析研究中探索出的一些有效措施。

Ranking Potential Reply-Providers in Community Question Answering System

Community Question Answering （CQA） websites have greatly facilitated users＇ lives, with an increasing number of people seeking help and exchanging ideas on the Internet. This newlymerged community features two characteristics： social relations and an ask-reply mechanism. As users＇ behaviours and social statuses play a more important role in CQA services than traditional answer retrieving websites, researchers＇ concerns have shifted from the need to passively find existing answers to actively seeking potential reply providers that may give answers in the near future. We analyse datasets derived from an online CQA system named ＂Quora＂, and observed that compared with traditional question answering services, users tend to contribute replies rather than questions for help in the CQA system. Inspired by the findings, we seek ways to evaluate the users＇ ability to offer prompt and reliable help, taking into account activity, authority and social reputation char- acteristics. We propose a hybrid method that is based on a Question-User network and social network using optimised PageRank algorithm. Experimental results show the efficiency of the proposed method for ranking potential answer-providers.

Building a click model： From idea to practice

Click-through information is considered as a valuable source of users＇ implicit relevance feedback. As user behavior is usually influenced by a number of factors such as position, presentation style and site reputation, researchers have proposed a variety of assumptions to generate a reasonable estimation of result relevance. Therefore, many click models have been proposed to describe how user click action happens and to predict click probability （and search result relevance）. This work builds upon many years of existing efforts from THUIR labs, summarizes the most recent advances and provides a series of practical click models. In this paper, we give an introduction of how to build an effective click model. We use two click models as specific examples to introduce the general procedures of building a click model. We also introduce common evaluation metrics for the comparison of different click models. Some useful datasets and tools are also introduced to help readers better understand and implement existing click models. The goal of this survey is to bring together current efforts in the area, summarize the research performed so far and give a view on building click models for web search.

Context-Aware Recommendation System using Graph-based Behaviours Analysis

Recommendation systems have been extensively studied over the last decade in various domains. It has been considered a powerful tool for assisting business owners in promoting sales and helping users with decision-making when given numerous choices. In this paper, we propose a novel Graph-based Context-Aware Recommendation Systems with Knowledge Graph to analyse and predict users’ behaviours, i.e., making recommendations based on historical events and their implicit associations. The model incorporates contextual information extracted from both users’ historical behaviours and events relations, where the contexts have been modelled as knowledge graphs. By leveraging the advantages offered from the knowledge graph, events dependencies and their subtle relations can be established and have been introduced in the recommendation process. Experimental results indicate that the proposed approach can outperform the state-of-the-art algorithms and achieve more accurate recommendations.

Using log mining to analyze user behavior on search engine

Users＇ behavior analysis has become one of the most important research topics, especially in terms of performance optimization, architecture analysis, and system maintenance, due to the rapid growth of search engine users. By adequately performing analysis on log data, researchers and Internet companies can get guidance to better search engines. In this paper, we perform our analysis based on approximately 750million entries of search requests obtained from log of a real commercial search engine. Several aspects of users＇ behavior are studied, including query length, ratio of query refining, recommendation access, and so on. Different information needs may lead to different behaviors, and we address this discussion in this paper. We firmly believe that these analyses would be helpful with respect of improving both effectiveness and efficiency of search engines.

Manipify:An Automated Framework for Detecting Manipulators in Twitter Trends

The rapid adoption of online social media platforms has transformed the way of communication and interaction.On these platforms,discussions in the form of trending topics provide a glimpse of events happening around the world in real-time.Also,these trends are used for political campaigns,public awareness,and brand promotions.Consequently,these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience.In this article,we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan.We propose“Manipify”-a framework for automatic detection and analysis of malicious users in Twitter trends.Our framework consists of three distinct modules:(1)user classifier,(2)hashtag classifier,and(3)trend analyzer.The user classifier module introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features.Also,the module classifies human and bot users.Next,the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories.Finally,the trend analyzer module examines users,hashtags,and tweets for hashtag reach,linguistic features,and user behaviour.Our user classifier module achieves 0.92 and 0.98 accuracy in classifying manipulators and bots,respectively.We further test Manipify on the dataset comprising 652 trending hashtags with 5.4 million tweets and 1.9 million users.The analysis of trends reveals that the trending panel is mostly dominated by political hashtags.In addition,our results show a higher contribution of human accounts in trend manipulation as compared to bots.