Although social media platforms serve diverse purposes, from social and professional networking to photo sharing and blogging, people frequently use them to share the thoughts and opinions and most importantly, their interests (e.g., politics, economy, sports). Understanding the interests of social media users is key to many applications that need to characterize them to recommend some services and find other individuals with similar interests. In this paper, we propose two approaches to the automatic determination of the interests of social media users. The first, that we named Frisk, is an unsupervised multilingual approach that determines the interests of a user from the explicit meaning of the words that occur in the user’s posts. The second, that we termed Ascertain, is a supervised approach that resorts to the hidden dimensions of the words that several studies indicated to be capable of revealing some of the psychological processes and personality traits of a person. In our evaluation, that we performed on two datasets obtained from Twitter, we show that Frisk is capable of inferring the interests in a multilingual context with good accuracy and that the psychological dimensions used by Ascertain are also good predictors of a user’s interests.

中文翻译：

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确定社交媒体用户的兴趣：两种方法

尽管社交媒体平台有多种用途，从社交和专业网络到照片共享和博客，人们经常使用它们来分享思想和观点，最重要的是，分享他们的兴趣（例如，政治，经济，体育）。了解社交媒体用户的兴趣是许多应用程序的关键，这些应用程序需要表征他们的能力以推荐某些服务并寻找其他具有相似兴趣的个人。在本文中，我们提出了两种自动确定社交媒体用户利益的方法。第一种，我们称为Frisk，是一种无监督的多语言方法，可以根据用户帖子中出现的单词的明确含义来确定用户的兴趣。第二，我们称为确定是一种有监督的方法，该方法诉诸于一些研究表明能够揭示一个人的某些心理过程和人格特质的单词的隐藏维度。在我们的评估中，我们对从Twitter获得的两个数据集进行了研究，结果表明Frisk能够在多语言环境中准确地推断出兴趣，并且Ascertain所使用的心理维度也可以很好地预测用户的兴趣。