The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
MoodScope: building a mood sensor from smartphone usage patterns
Abstract
We report a first-of-its-kind smartphone software system, MoodScope, which infers the mood of its user based on how the smartphone is used. Compared to smartphone sensors that measure acceleration, light, and other physical properties, MoodScope is a "sensor" that measures the mental state of the user and provides mood as an important input to context-aware computing. We run a formative statistical mood study with smartphone-logged data collected from 32 participants over two months. Through the study, we find that by analyzing communication history and application usage patterns, we can statistically infer a user's daily mood average with an initial accuracy of 66%, which gradu-ally improves to an accuracy of 93% after a two-month personal-ized training period. Motivated by these results, we build a service, MoodScope, which analyzes usage history to act as a sensor of the user's mood. We provide a MoodScope API for developers to use our system to create mood-enabled applications. We further create and deploy a mood-sharing social application.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Higher degrees of depression are linked to reduced contact with one’s social network, shorter text messages, reduced duration of calls, and less variability in geospatial activity [6-9].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Development and validation of brief measures of positive and negative affect: The PANAS scales.
Watson, D., Clark, L. A., & Tellegen, A. (1988) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.54.6.1063 · Cited by 32,433
(1996) · Cited by 20,179
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334
Journal of personality and social psychology
Armstrong, J. (1988) · International Journal of Forecasting · doi:10.1016/0169-2070(88)90126-4 · Cited by 10,544
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Review of personality and social psychology
Davies, M. (1983) · Personality and Individual Differences · doi:10.1016/0191-8869(83)90072-7 · Cited by 3,394
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
Crawford, J. R., & Henry, J. D. (2004) · British Journal of Clinical Psychology · doi:10.1348/0144665031752934 · Cited by 2,957
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Universals and cultural differences in facial expressions of emotion.
Ekman, P. (1972) · Medical Entomology and Zoology · Cited by 2,273
Feature selection: evaluation, application, and small sample performance
Jain, A. K., & Zongker, D. E. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.574797 · Cited by 2,186
Affect Grid: A single-item scale of pleasure and arousal.
Russell, J. A., Weiss, A., & Mendelsohn, G. A. (1989) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.57.3.493 · Cited by 1,951
Can people feel happy and sad at the same time?
Larsen, J. T., McGraw, A. P., & Cacioppo, J. T. (2001) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.81.4.684 · Cited by 1,083
Toward detecting emotions in spoken dialogs
Lee, C. M., & Narayanan, S. S. (2005) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2004.838534 · Cited by 988
Social signal processing: Survey of an emerging domain
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.11.007 · Cited by 842
Mood and the mundane: Relations between daily life events and self-reported mood.
Clark, L. A., & Watson, D. (1988) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.54.2.296 · Cited by 784 · cited 1×
“Literature suggests that mood is strongly correlated with the social interactions [28] and daily activities [29] of an individual.”
Scherer, K. R., Schuller, B., & Elkins, A. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316676202 · Cited by 768
Distinctions between emotion and mood
Beedie, C., Terry, P., & Lane, A. (2005) · Cognition and Emotion · doi:10.1080/02699930541000057 · Cited by 593
Tao, J., & Tan, T. (2005) · Lecture notes in computer science · doi:10.1007/11573548_125 · Cited by 531
Automatic mood detection and tracking of music audio signals
Lu, L., Liu, D., & Zhang, H.-J. (2005) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tsa.2005.860344 · Cited by 518
Rachuri, K. K., Musolesi, M., Cecilia Mascolo, C., Rentfrow, P. J., Longworth, C., & Auçinas, A. (2010) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/1864349.1864393 · Cited by 509
Myths and fallacies of personally identifiable information
Narayanan, A., & Shmatikov, V. (2010) · Communications of the ACM · doi:10.1145/1743546.1743558 · Cited by 346
The influence of mood on perceptions of social interactions
Forgas, J. P., Bower, G. H., & Krantz, S. E. (1984) · Journal of Experimental Social Psychology · doi:10.1016/0022-1031(84)90040-4 · Cited by 335
Shepard, C. W., Rahmati, A., Tossell, C. C., Zhong, L., & Kortum, P. (2011) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1925019.1925023 · Cited by 295
Self-citationDifferentiating affect, mood, and emotion: Toward functionally based conceptual distinctions.
Batson, C. D., Shaw, L. L., & Oleson, K. C. (1992) · Cited by 263
Machine Analysis of Facial Expressions
Pantic, M., & Stewart, M. (2007) · Face Recognition · doi:10.5772/4847 · Cited by 248
Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones
Chittaranjan, G. T., Blom, J., & Gática-Pérez, D. (2011) · International Symposium on Wearable Computers · doi:10.1109/iswc.2011.29 · Cited by 223
Lane, N. D., Xu, Y., Lu, H., Hu, S., Choudhury, T., Campbell, A. T., & Zhao, F. (2011) · Proceedings of the 13th international conference on Ubiquitous computing · doi:10.1145/2030112.2030160 · Cited by 171
Self-citationSpeakerSense: Energy Efficient Unobtrusive Speaker Identification on Mobile Phones
Lu, H., Brush, A. J. B., Priyantha, N. B., Karlson, A. K., & Liu, J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21726-5_12 · Cited by 155
Towards unobtrusive emotion recognition for affective social communication
Lee, H., Choi, Y. S., Lee, S., & Park, I. P. (2012) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2012.6181098 · Cited by 136
Foundations of human computing
Cohn, J. F. (2006) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1180995.1181043 · Cited by 106
Hernandez, J., Hoque, M. E., Drevo, W., & Picard, R. W. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370264 · Cited by 99
Foundations of Human Computing: Facial Expression and Emotion
Cohn, J. F. (2007) · Springer eBooks · doi:10.1007/978-3-540-72348-6_1 · Cited by 77
A study of mobile mood awareness and communication through MobiMood
Church, K., Hoggan, E. E., & Oliver, N. (2010) · Nordic Conference on Human-Computer Interaction · doi:10.1145/1868914.1868933 · Cited by 69
Meta-Classifiers in Acoustic and Linguistic Feature Fusion-Based Affect Recognition
Schuller, B. W., Villar, R. J. R., Rigoll, G., & Lang, M. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2005.1415116 · Cited by 61
Towards Mood Based Mobile Services and Applications
Gluhak, A., Presser, M. A., Zhu, L., Esfandiyari, S., & Kupschick, S. (2007) · Springer eBooks · doi:10.1007/978-3-540-75696-5_10 · Cited by 32
Studying Affect Among the Chinese: The Circular Way
Yik, M. (2009) · Journal of Personality Assessment · doi:10.1080/00223890903087596 · Cited by 31
Flagged first, then by citations
1 of the 39 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- S. S. Tomkins. Affect Imagery Consciousness. Springer 1962. S. S. Tomkins. Affect Imagery Consciousness. Springer 1962.
- 10.5555/1775377.1775390 · DOI
- 10.5555/1775377.1775390 · DOI
- 10.5555/3001460.3001507 · DOI
- R. LiKamWa , Y. Liu , N. D. Lane and L. Zhong . " Can your smartphone infer your mood? ", In Proc. PhoneSense Work-shop , 2011 . R. LiKamWa, Y. Liu, N. D. Lane and L. Zhong. "Can your smartphone infer your mood?", In Proc. PhoneSense Work-shop, 2011.
Cited by
Sorted by citations
Psychological targeting as an effective approach to digital mass persuasion
Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710966114 · Cited by 892
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802
Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616650285 · Cited by 515
Self-citationCanzian, L., & Musolesi, M. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2805845 · Cited by 477
Emotion recognition using wireless signals
Zhao, M., Adib, F., & Katabi, D. (2016) · Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking · doi:10.1145/2973750.2973762 · Cited by 461
Metadata
- Authors
- LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L.
- Affiliations
- Rice University (US)
- Microsoft Research Asia (China) (CN)
- Venue
- Proceeding of the 11th annual international conference on Mobile systems, applications, and services
- Publisher
- ACM
- Published
- 2013-06-25
- Pages
- 389-402
- OpenAlex
- W2152423878
- Cited by
- 444
- References
- 39
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 15 · 2023: 12 · 2022: 34 · 2021: 29 · 2020: 53 · 2019: 65 · 2018: 37 · 2017: 59 · 2016: 51 · 2015: 41 · 2014: 23 · 2013: 6
- FWCI
- 25.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Mobile Systems, Applications, and Services (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- smartphone usage patterns, context-aware computing, individualized training, smartphone sensors
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
