RedDust: a Large Reusable Dataset of Reddit User Traits

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 6118–6126 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 6118 RedDust: a Large Reusable Dataset of Reddit User Traits Anna Tigunova, Andrew Yates, Paramita Mirza, Gerhard Weikum Max Planck Institute for Informatics Saarland Informatics Campus, Saarbr¨ucken, Germany {tigunova, ayates, paramita, weikum}@mpi-inf.mpg.de Abstract Social media is a rich source of assertions about personal traits, such as I am a doctor or my hobby is playing tennis. Precisely identifying explicit assertions is difficult, though, because of the users’ highly varied vocabulary and language expressions. Identifying personal traits from implicit assertions like I’ve been at work treating patients all day is even more challenging. This paper presents RedDust, a large-scale annotated resource for user profiling for over 300k Reddit users across five attributes: profession, hobby, family status, age, and gender. We construct RedDust using a diverse set of high-precision patterns and demonstrate its use as a resource for developing learning models to deal with implicit assertions. RedDust consists of users’ personal traits, which are (attribute, value) pairs, along with users’ post ids, which may be used to retrieve the posts from a publicly available crawl or from the Reddit API. We discuss the construction of the resource and show interesting statistics and insights into the data. We also compare different classifiers, which can be learned from RedDust. To the best of our knowledge, RedDust is the first annotated language resource about Reddit users at large scale. We envision further use cases of RedDust for providing background knowledge about user traits, to enhance personalized search and recommendation as well as conversational agents. Keywords: personal knowledge, user profiling, conversational text, online forums 1 Introduction Reddit is a popular social media platform for discussing a wide range of topics. It is an important source of infor- mation for data analysis on social media as it provides rich structure, abundance of data and covers a broad range of topics. Reddit is used by approximately 330 million users1 with 2.8 million comments written each day2. Alexa.com ranks it as the 21st most popular website worldwide. Despite its popular and rich data, few have considered Red- dit as a source of data about users’ personal traits like their professions and hobbies. Prior work has focused on Red- dit as a source of demographic information, whereas we consider rich attributes like profession and hobbies in addi- tion to demographic ones (age, gender, family status). Such data has many applications, including personalizing health- care (Gyrard et al., 2018), recommendations, search, and conversational agents. We address this gap by creating a labeled dataset of Reddit users (including their posts and comments) that covers five user attributes: profession, hobby, family status, age, and gender. We leveraged three high-precision approaches to identify predicates and their object values in users’ posts: (1) natural language patterns matching assertions like I am a flight attendant, (2) bracket patterns matching structured assertions of users’ ages and genders (I [35m] just …

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