does incorporating user traits help the task?
only in the 806-class task predicting <= 25 clusters
The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future. In this paper, we explore the task of predicting human activities from user-generated content. We collect a dataset containing instances of social media users writing about a range of everyday activities. We then use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and perform an automatic clustering of these activities. We train a neural network model to make predictions about which clusters contain activities that were performed by a given user based on the text of their previous posts and self-description. Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.
What a person does says a lot about who they are. Information about the types of activities that a person engages in can provide insights about their interests BIBREF0 , personality BIBREF1 , physical health BIBREF2 , the activities that they are likely to do in the future BIBREF3 , and other psychological phenomena like personal values BIBREF4 . For example, it has been shown that university students who exhibit traits of interpersonal affect and self-esteem are more likely to attend parties BIBREF5 , and those that value stimulation are likely to watch movies that can be categorized as thrillers BIBREF6 . Several studies have applied computational approaches to the understanding and modeling of human behavior at scale BIBREF7 and in real time BIBREF8 . However, this previous work has mainly relied on specific devices or platforms that require structured definitions of behaviors to be measured. While this leads to an accurate understanding of the types of activities being done by the involved users, these methods capture a relatively narrow set of behaviors compared to the huge range of things that people do on a day-to-day basis. On the other hand, publicly available social media data provide us with information about an extremely rich and diverse set of human activities, but the data are rarely structured or categorized, and they mostly exist in the form of natural language. Recently, however, natural language processing research has provided several examples of methodologies for extracting and representing human activities from text BIBREF9 , BIBREF10 and even multimodal data BIBREF11 . In this paper, we explore the task of predicting human activities from user-generated text data, which will allow us to gain a deeper understanding of the kinds of everyday activities that people discuss online with one another. Throughout the paper, we use the word “activity” to refer to what an individual user does or has done in their daily life. Unlike the typical use of this term in the computer vision community BIBREF12 , BIBREF13 , in this paper we use it in a broad sense, to also encompass non-visual activities such as “make vacation plans" or “have a dream” We do not focus on fine-grained sequences actions such as “pick up a camera”, “hold a camera to one's face”, “press the shutter release button”, and others. Rather, we focus on the high-level activity as a person would report to others: “take a picture”. Additionally, we specifically focus on everyday human activities done by the users themselves, rather than larger-scale events BIBREF14 , which are typically characterized by the involvement or interest of many users, often at a specific time and location. Given that the space of possible phrases describing human activities is nearly limitless, we propose a set of human activity clusters that summarize a large set of several hundred-thousand self-reported activities. We then construct predictive models that are able to estimate the likelihood that a user has reported that they have performed an activity from any cluster. The paper makes the following main contributions. First, starting with a set of nearly 30,000 human activity patterns, we compile a very large dataset of more than 200,000 users undertaking one of the human activities matching these patterns, along with over 500 million total tweets from these users. Second, we use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and create a set of activity clusters of variable granularity. Third, we explore a neural model that can predict human activities based on natural language data, and in the process also investigate the relationships between everyday human activities and other social variables such as personal values.