Factors Impacting the Quality of User Answers on Smartphones
This work addresses the challenge of improving behavior prediction models for mobile applications by enhancing data quality, though it is incremental as it builds on existing sensor-based approaches.
The paper tackled the problem of predicting human behavior by identifying factors that affect the quality of user-provided context data on smartphones, finding that reaction time and completion time correlate with both external and internal causes.
So far, most research investigating the predictability of human behavior, such as mobility and social interactions, has focused mainly on the exploitation of sensor data. However, sensor data can be difficult to capture the subjective motivations behind the individuals' behavior. Understanding personal context (e.g., where one is and what they are doing) can greatly increase predictability. The main limitation is that human input is often missing or inaccurate. The goal of this paper is to identify factors that influence the quality of responses when users are asked about their current context. We find that two key factors influence the quality of responses: user reaction time and completion time. These factors correlate with various exogenous causes (e.g., situational context, time of day) and endogenous causes (e.g., procrastination attitude, mood). In turn, we study how these two factors impact the quality of responses.