Happiness Recognition from Mobile Phone Data
Open accessAbstract
In this paper we provide the first evidence that daily happiness of individuals can be automatically recognized using an extensive set of indicators obtained from the mobile phone usage data (call log, sms and Bluetooth proximity data) and ``background noise'' indicators coming from the weather factor and personality traits. Our final machine learning model, based on the Random Forest classifier, obtains an accuracy score of 80.81% for a 3-class daily happiness recognition problem. Moreover, we identify and discuss the indicators, which have strong predictive power in the source and the feature spaces, discuss different approaches, machine learning models and provide an insight for future research.
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Mobile phones have been used to monitor stress and mental health.
“Mobile phones have been used to monitor stress and mental health [4-10].”
The above three studies were all performed on the "Friends and Family" data set, including 8 weeks of mobile phone data from 117 participants living in a major US university's married graduate student residency.
“The above three studies were all performed on the “Friends and Family” data set, including 8 weeks of mobile phone data from 117 participants living in a major US university’s married graduate student residency.”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [17]
Bogomolov et al established machine learning models to recognize happiness and stress with features of Bluetooth records, calls, and text messages, which obtained accuracy rates of 80.81% and 72.28%, respectively.
“Bogomolov et al established machine learning models to recognize happiness and stress with features of Bluetooth records, calls, and text messages, which obtained accuracy rates of 80.81% and 72.28%, respectively [17,18].”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [17]
Previous studies [15-18] have been performed on relatively small (approximately 100 participants) homogeneous (eg, university students) cohorts of participants over relatively short periods (8-10 weeks), which may limit their generalizability.
“Previous studies [15-18] have been performed on relatively small (approximately 100 participants) homogeneous (eg, university students) cohorts of participants over relatively short periods (8-10 weeks), which may limit their generalizability.”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [17]
Bluetooth features used in previous studies [15-18] have been limited to basic statistical features (eg, sum, mean, and standard deviation), which are unable to characterize some nonlinear aspects (such as complexity, regularity, and periodicity) of the Bluetooth data.
“Besides, Bluetooth features used in these studies [15-18] have been limited to basic statistical features (eg, sum, mean, and standard deviation), which are unable to characterize some nonlinear aspects (such as complexity, regularity, and periodicity) of the Bluetooth data.”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [17]
Our study was performed on a larger (N=316) multicenter data set with a longer follow-up (median 4 months) than previous Bluetooth-related studies.
“Compared with previous Bluetooth-related studies [15-18], our study was performed on a larger (N=316) multicenter data set with a longer follow-up (median 4 months).”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [17]
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- Authors
- Bogomolov, A., Lepri, B., & Pianesi, F.
- Affiliations
- University of Trento (IT)
- Fondazione Bruno Kessler (IT)
- ORCID
- Lepri, Bruno
- Venue
- 2013 International Conference on Social Computing
- Publisher
- IEEE
- Published
- 2013-09
- Pages
- 790-795
- OpenAlex
- W2065340646
- Cited by
- 67
- References
- 34
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 8 · 2020: 6 · 2019: 10 · 2018: 4 · 2017: 7 · 2016: 7 · 2015: 8 · 2014: 7
- FWCI
- 10.09
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- International Conference on Social Computing (publishedVersion)
- Iris (University of Trento) (submittedVersion, other-oa)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- happiness recognition, mobile phone data, call logs, SMS, background noise, weather, personality traits, automatic emotion recognition, feature selection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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