NationalMood: Large-scale Estimation of People's Mood from Web Search Query and Mobile Sensor Data

TL;DR

This paper introduces a large-scale method to estimate people's moods using web search queries and mobile sensor data, revealing mood patterns and their relation to external events like COVID-19.

Contribution

It presents a novel approach combining web search and sensor data for mood estimation and demonstrates its effectiveness at a large scale.

Findings

Certain advertisements are more effective when tailored to mood.

National Mood Score reflects pandemic-related mood fluctuations.

Weekly mood rhythms are observable in large-scale data.

Abstract

The ability to estimate current affective statuses of web users has considerable potential towards the realization of user-centric opportune services. However, determining the type of data to be used for such estimation as well as collecting the ground truth of such affective statuses are difficult in the real world situation. We propose a novel way of such estimation based on a combinational use of user's web search queries and mobile sensor data. Our large-scale data analysis with about 11,000,000 users and 100 recent advertisement log revealed (1) the existence of certain class of advertisement to which mood-status-based delivery would be significantly effective, (2) that our "National Mood Score" shows the ups and downs of people's moods in COVID-19 pandemic that inversely correlated to the number of patients, as well as the weekly mood rhythm of people.