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User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity
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作者 Yuting Tan Vijay K. Madisetti 《Journal of Software Engineering and Applications》 2024年第6期463-473,共11页
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 ... 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. 展开更多
关键词 Social Media User Behavior Analysis Sentiment Analysis Data Mining Machine Learning User profiling CYBERSECURITY Behavioral Insights personality Prediction
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大数据视角下数字社区用户群体人格画像
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作者 符虔 赵海腾 +1 位作者 赵小青 帅懿芯 《贵州大学学报(自然科学版)》 2023年第6期62-68,共7页
人格特征是人类行为的关键驱动因素,时刻影响人们的日常生活。尤其在突发公共事件情境下,这种影响机制可能更具有个体差异性。数字社区的出现使得基于用户信息行为大数据自动有效地进行用户群体人格画像成为可能,但相关研究还相对较少。... 人格特征是人类行为的关键驱动因素,时刻影响人们的日常生活。尤其在突发公共事件情境下,这种影响机制可能更具有个体差异性。数字社区的出现使得基于用户信息行为大数据自动有效地进行用户群体人格画像成为可能,但相关研究还相对较少。以Twitter用户在COVID-19疫情期间发布的相关信息和其相关信息行为记录为样本,进行用户群体人格画像。首先,邀请专业心理咨询师基于自恋人格的定义和量表设定了数据标注规则并对数据集进行标注;其次,设计了13个潜在的用户行为指标,构建了Logit回归模型,并评估了模型的分类性能(分类准确率达到70.34%);再次,确定了一组与用户群体自恋人格特征密切相关的信息行为指标。这组指标共有5项,具体包括:用户近三年发表的推文总数、负面情感倾向推文所占比例、推文中动词平均数、推文中话题标签平均数、推文中感叹号平均数。从而,提出了一种针对特定情境(突发公共事件)基于用户信息行为大数据分析的群体人格画像的方法,为维护民众心理健康和数字社区清朗空间提供了新的思路。 展开更多
关键词 数字社区 群体人格 自恋人格 人格画像 LOGIT回归
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