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Abstract：
Profiling social media users is an analytical approach to generate an extensive blueprint of user\'s personal characteristics, which can be useful for a diverse range of applications, such as targeted marketing and personalized recommendations. Although social user profiling has gained substantial attention in recent years, effectively constructing a collaborative model that could describe long and short-term profiles is still challenging. In this paper, we will discuss the profiling problem from two perspectives; how to mathematically model and track user\'s behavior over short and long periods and how to enhance the classification of user\'s activities. Using mathematical equations, our model can define periods in which the user\'s interests abruptly changed. A dataset consisting of 30,000 tweets was built and manually annotated into 10 topic categories. Bi-LSTM and GRU models are applied to classify the user\'s activities representing his interests, which then are utilized to create and model the dynamic profile. In addition, the effect of word embedding techniques and pre-trained classification models on the accuracy of the classification process is explored in this research.
摘要：
分析社交媒体用户是一种分析方法，用于生成用户个人特征的广泛蓝图，这对各种应用都很有用，例如有针对性的营销和个性化的推荐。尽管近年来社交用户概况受到了广泛关注，有效地构建一个可以描述长期和短期概况的协作模型仍然具有挑战性。在本文中,我们将从两个角度讨论分析问题；如何在短期和长期内对用户的行为进行数学建模和跟踪，以及如何增强用户活动的分类。使用数学方程式，我们的模型可以定义用户兴趣突然改变的时期。构建了一个由30,000条推文组成的数据集，并将其手动注释为10个主题类别。应用Bi-LSTM和GRU模型对用户代表其兴趣的活动进行分类，然后利用它来创建和建模动态配置文件。此外,本研究探讨了词嵌入技术和预训练分类模型对分类过程准确性的影响。
