# Identifying Human Daily Activity Types with Time-Aware Interactions
## 2 Related Work  
One goal of activity recognition research is to identify physical activities [7; 8; 9; 10; 11]. They can be used for human trajectory tracking, recommended system, regions-of-interest detection [12; 13], and so on. Researchers use popular machine learning methods, such as support vector machine (SVM), convolutional neural network (CNN), and long short-term memory network (LSTM) to identify human activities and propose a fixed approach that treats activities as sequential text or multi-dimensional images, and uses context-sensitive grammar to identify physical activities. Actually, this research heavily depends on video cameras, wearable sensor, or monitors, and human behavior in urban scope is difficult to capture because data collection is time consuming and maybe conflicts with peoples' privacy. Different from physical activities and events, human daily activities are high-level logical activities that can be obtained from many types of crowdsourced data. These data are easily obtained and closely connected with human behavior and urban systems. In this paper, we aim to identify human daily activities (e.g., sports, shopping, and travel) from social media trajectory. User's daily logical activities can be inferred from social media through several methods. These works can be classified into two groups, with and without Point of Interest (POI). In works with POI, the trajectory data under study contain or correlate directly with POI data. Given the close correlation between POIs and human activities, information from POIs can be leveraged to infer HATs. Some studies used activities and POIs interchangeably [14] because activities performed at a location usually define its meaning to some degree [15] (e.g., cafe is a place where people drink coffee). A common practice is to use semantic location labels as a proxy to high-level activities (e.g., office \(\rightarrow\)working) [16; 17]. Dominant functional places, such as home or work can be detected from people's daily routines [18]. Thus, such an approach is an understandable simplification. Nevertheless, a considerable number of locations can be a scene of more than one activity at the same moment (e.g., at a shopping mall), and one activity can be a characteristic of various locations (e.g., \"taking picture\"). [PERSON] [19] attempted to discover the relationship categories between POIs and activities. However, most trajectory data in social media do not contain location or POIs because of limitations in privacy, devices, and so on. Works without POIs mostly regard HATs as a text classification task in machine learning. [PERSON] integrated trajectory data and social media to reveal individual activities in daily life [20]. However, extracting knowledge from Twitter and matching trajectories to knowledge base. [PERSON] tried to establish a set of activities that will occur at a future time such as tonight, tomorrow, and next week, by using a future time-window and keywords related to activities [21]. They only extracted unexpected activities, but ignored frequently recurring activities. [PERSON] proposed a novel collaborative boosting framework comprising a text-to-activity classifier for each user to recognize user activities [22]. [PERSON] built a multi-label classifier using tweets manually annotated with activities to predict up to three activities [23]. The classifier considers the tweet text, the tweet posting time, the POI type from Foursquare, and the POI name from Foursquare. Their approach relies on crowdsourced annotations with the limited scalability of human labor involved in the process. [PERSON] interpreted the concept of activities as verb-noun phrase pairs (e.g., drinking coffee), which they extracted using a pattern-based paradigm [24]. Those methods applying various supervised machine learning or natural language processing (NLP) techniques can capture semantic information in local consecutive word sequences well.