Twitter User Profiling Model Based on Temporal Analysis of Hashtags and Social Interactions

机译：基于标签和社交互动的时间分析的Twitter用户分析模型

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Social content generated by users' interaction in social networks is a knowledge source that may enhance users' profiles modeling, by providing information on their activities and interests over time. The aim of this article is to propose several original strategies for modeling profiles of social networks' users, taking into account social information and its temporal evolution. We illustrate our approach on the Twitter network. We distinguish interactive and thematic temporal profiles and we study profiles' similarities by applying various clustering algorithms, by giving a special attention to overlapping clusters. We compare the different types of profiles obtained and show how they can be relevant for the recommendation of hashtags and users to follow.

机译：用户在社交网络中的交互所生成的社交内容是一种知识源，可以通过提供有关其活动和兴趣的信息来增强用户的个人资料建模。本文的目的是在考虑社交信息及其时间演变的情况下，提出一些用于对社交网络用户的个人资料进行建模的原始策略。我们在Twitter网络上说明了我们的方法。我们区分交互式和主题时间轮廓，并通过应用各种聚类算法来研究轮廓的相似性，并特别注意重叠的聚类。我们比较了获得的不同类型的配置文件，并显示了它们如何与推荐的标签和用户密切相关。

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《International conference on applications of natural language to information systems》|2017年|124-130|共7页
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Abir Gorrab; Ferihane Kboubi; Ali Jaffal; Benedicte Le Grand; Henda Ben Ghezala;
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User profile; Temporality; Social interactions; Hashtags; Similarity;

机译：用户资料;临时性;社交互动;标签;相似;

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Twitter User Profiling Model Based on Temporal Analysis of Hashtags and Social Interactions

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