Abstract
Human activities embedded in crowdsourced data, such as social media trajectory, represent individual daily styles and patterns, which are valuable in many applications. However, the accurate identification of human activity types (HATs) from social media is challenging, possibly because interactions between posts and users at different time are overlooked. To fill this gap, we propose a novel model that introduces the interactions hidden in social media and synthesizes Graph Convolutional Network (GCN) for identifying HAT. The model first characterizes interactions among words, posts, dates, and users, and then derives a Time Gated Human Activity Graph Convolutional Network (TG-HAGCN) to predict the HATs of social media trajectory. To examine the proposed model performance, we built a new dataset including interactions between post content, post time, and users from the open Yelp dataset. Experimental results show that exploiting interactions hidden in social media to recognize HATs achieves state-of-the-art performance with high accuracy. The study indicates that interactions among social media promotes ability of machine learning on social media data mining and intelligent applications, and offers a reference solution for how to fuse multi-type heterogeneous data in social media.
Highlights
Urban spaces, where citizens live, move, and engage in different activities, are socialized, and dynamic [1]
This study proposes a model that introduces the interactions in social media into human activity types (HATs) identification
A graph neural network-based method for human activity recognition is described, which is derived from Graph Convolutional Network (GCN) [25], named Time Gated Human Activity Graph Convolutional Network (TG-HAGCN)
Summary
Urban spaces, where citizens live, move, and engage in different activities, are socialized, and dynamic [1]. Understanding the complexities underlying the emerging behaviors of human travel patterns on the city level is essential for making informed decision-making pertaining to urban transportation infrastructures [2]. Emerging crowdsourced data provides available data sources for capturing human behaviors and modeling urban systems. The human activity types (HATs) (e.g., sports, shopping, and travel) in human trajectory characterize the user’s daily behaviors, and present human lifestyles and patterns [3,4]. The trajectory with HATs contributes to understanding human society and urban systems, and to performing many tasks for different purposes, such as customer recommendation, traffic forecasting, travel demand modeling, urban planning, and so on. Identifying HATs from the trajectory on a large scale by experts is always labor-intensive and time-consuming. It is significant to develop an approach, which can automatically identify accurate HATs on large-scale
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