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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.
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“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].”
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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
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.
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Langener, A. M., Bringmann, L. F., Kas, M. J., & Stulp, G. (2024) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-023-01328-0 · Cited by 12
Semi-automatic remote medicine monitoring system of mobile users
Du, Z.-W., & Yang, Y.-J. (2015) · China Communications · doi:10.1109/cc.2015.7366236 · Cited by 13
Estimating Individualized Daily Self-Reported Affect with Wearable Sensors
Yan, S., Hosseinmardi, H., Kao, H.-T., Narayanan, S. S., Lerman, K., & Ferrara, E. (2019) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2019.8904691 · Cited by 12
Steinberger, F., Schroeter, R., & Babiac, D. (2017) · Human-computer interaction series · doi:10.1007/978-3-319-49448-7_2 · Cited by 13
Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing
Vaid, S. S., & Harari, G. M. (2019) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-31620-4_5 · Cited by 12
Understanding Heavy Drinking at Night through Smartphone Sensing and Active Human Engagement
Phan, T., Labhart, F., Muralidhar, S., & Gática-Pérez, D. (2020) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3421937.3421992 · Cited by 13
Personality psychology is getting personal: Advancing the field through personalization
Modersitzki, N., Phan, L. V., McDonald, S., Wright, A. G. C., & Renner, K. (2025) · European Journal of Personality · doi:10.1177/08902070251316915 · Cited by 13
Timing rather than user traits mediates mood sampling on smartphones
Noë, B., Turner, L. D., Linden, D. E. J., Allen, S. M., Maio, G. R., & Whitaker, R. M. (2017) · BMC Research Notes · doi:10.1186/s13104-017-2808-1 · Cited by 12
Towards pervasive geospatial affect perception
Raja, M., Exler, A., Hemminki, S., Konomi, S., Sigg, S., & Inoue, S. (2017) · GeoInformatica · doi:10.1007/s10707-017-0294-1 · Cited by 12
Wellness assessment through environmental sensors and smartphones
McCarthy, M., & Spachos, P. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7997416 · Cited by 12
Predicting Personality Traits Using Smartphone Sensor Data and App Usage Data
Kambham, N. K., Stanley, K. G., & Bell, S. (2018) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon.2018.8614854 · Cited by 12
Real-Time Monitoring: A Key Element in Personalized Health and Precision Health
Zulueta, J., Leow, A., & Ajilore, O. (2020) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.20190042 · Cited by 11
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Li, B., & Dong, W. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00038 · Cited by 12
iSelf: Towards cold-start emotion labeling using transfer learning with smartphones
Sun, B., Ma, Q., Zhang, S., Liu, K., & Liu, Y. (2015) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2015.7218495 · Cited by 12
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Maxhuni, A., Hernandez-Leal, P., Morales, E., Sucar, L. E., Osmani, V., Muñoz-Meléndez, A., & Mayora, O. (2017) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-49622-1_16 · Cited by 10
Socialni odnosi, animizem, antropomorfizem in interakcija z UI
Babić, N. Č. (2022) · doi:10.18690/um.ff.11.2022.12 · Cited by 10
Barata, F., Kowatsch, T., Tinschert, P., & Filler, A. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2972713 · Cited by 11
Memento: An Emotion Driven Lifelogging System with Wearables
Jiang, S., Zhou, P., Li, Z., & Li, M. (2017) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2017.8038411 · Cited by 11
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Beyond location check-ins: Exploring physical and soft sensing to augment social check-in apps
Rachuri, K. K., Hossmann, T., Cecilia Mascolo, C., & Holden, S. B. (2015) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2015.7146518 · Cited by 11
Mood self-assessment on smartphones
Khue, L. M., Ouh, E. L., & Jarzabek, S. (2015) · Wireless Health · doi:10.1145/2811780.2811921 · Cited by 11
The case for human-centric personal analytics
Lee, Y., & Balan, R. K. (2014) · doi:10.1145/2611264.2611267 · Cited by 11
Monitoring and Prediction of Mood in Elderly People during Daily Life Activities
Bautista‐Salinas, D., González, J. R., Méndez, I., & Mozos, Ó. M. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857847 · Cited by 15
Correlation of significant places with self-reported state of bipolar disorder patients
Sabatelli, M., Osmani, V., Mayora, O., Gruenerbl, A., & Lukowicz, P. (2014) · International Conference on Wireless Mobile Communication and Healthcare · doi:10.1109/mobihealth.2014.7015923 · Cited by 11
Differences in smartphone usage: Validating, evaluating, and predicting mobile user intimacy
Gustarini, M., Scipioni, M. P., Fanourakis, M., & Wac, K. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.06.003 · Cited by 12
Changing health behaviors through social and physical context awareness
Chen, G., Ding, X., Huang, K. W., Xu, Y., & Zhang, C. (2015) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2015.7069424 · Cited by 10
Life-logs Aggregation for Quality of Life Monitoring
Zini, F., Reinstadler, M., & Ricci⋆, F. (2015) · International Conference on Digital Health · doi:10.1145/2750511.2750531 · Cited by 10
Zhang, H., Gashi, S., Kimm, H., Hancı, E., & Matthews, O. (2018) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3267305.3274760 · Cited by 10
Smartphone usage contexts and sensable patterns as predictors of future sedentary behaviors
He, Q., & Agu, E. (2016) · IEEE Healthcare Innovations and Point of Care Technologies Conference (HI-POCT) · doi:10.1109/hic.2016.7797695 · Cited by 10
Geller, T. (2014) · Communications of the ACM · doi:10.1145/2555809 · Cited by 10
Exploiting usage statistics for energy-efficient logical status inference on mobile phones
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Xuan, W., Chowdhury, M. S. R., Ding, Y., & Zhao, Y. (2025) · IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft) · doi:10.1109/mobilesoft66462.2025.00014 · Cited by 9
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Flutura, S., Wagner, J., Lingenfelser, F., Seiderer, A., & André, E. (2016) · International Conference on Multimodal Interaction · doi:10.1145/2993148.2993164 · Cited by 12
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Wang, H., Li, R., Shokouhi, M., Li, H., & Chang, Y. (2017) · ACM Transactions on Information Systems · doi:10.1145/3086665 · Cited by 9
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Sasaki, W., Nakazawa, J., & Okoshi, T. (2018) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3267305.3267699 · Cited by 9
Choe, E. K., Klasnja, P., & Pratt, W. (2021) · Springer eBooks · doi:10.1007/978-3-030-58721-5_19 · Cited by 12
Krishna, A., Mallick, M., & Mitra, B. (2016) · doi:10.1145/2933566.2933570 · Cited by 9
Towards designing an intelligent experience sampling method for emotion detection
Ghosh, S., Ganguly, N., Mitra, B., & De, P. (2017) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2017.7983143 · Cited by 8
Survey of mood detection through various input modes
Lietz, R., Harraghy, M., Calderon, D., Brady, J., Becker, E., & Makedon, F. S. (2019) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3316782.3321543 · Cited by 8
Oh, B., Hwang, J., Seo, S., Chun, S., & Lee, K.-H. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114912 · Cited by 9
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Shi, D.-X., Chen, X., Wei, J., & Yang, R. (2015) · International Conference on Smart Cities · doi:10.1109/smartcity.2015.116 · Cited by 7
Rich context information for just-in-time adaptive intervention promoting physical activity
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Estrada, M. L. B., Cabada, R. Z., & Aispuro-Gallegos, C. G. (2018) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2018.00104 · Cited by 8
Mano, L. Y., Faiçal, B. S., Gonçalves, V. P., Pessin, G., Gomes, P. H., de Carvalho, A. C. P. L. F., & Ueyama, J. (2019) · Soft Computing · doi:10.1007/s00500-019-04411-7 · Cited by 8
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Chan, J., Goel, M., Gollakota, S., & Nandakumar, R. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00330-5 · Cited by 8
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Farzand, H., Abraham, M., Brewster, S. A., Khamis, M., & Marky, K. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2361519 · Cited by 7
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Elmalaki, S., Shoukry, Y., & Srivastava, M. B. (2018) · doi:10.1145/3277893.3277901 · Cited by 7
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Bhardwaj, S., Dixit, A., Yadav, K., & Rajan, G., V (2021) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite51222.2021.9404755 · Cited by 7
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Li, B., & Dong, W. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2021.3129367 · Cited by 7
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S, P. M., Raj, A., Sen, S., Saha, S., & Ghosh, S. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3596269 · Cited by 7
Lee, T., Lee, H.-J., Lee, J.-B., & Kim, J.-D. (2023) · Italian National Conference on Sensors · doi:10.3390/s23208544 · Cited by 8
Alamoudi, D., Nabney, I. T., & Crawley, E. M. (2024) · Sensors · doi:10.3390/s24030722 · Cited by 7
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Sarma, S., Venkatasubramanian, N., & Dutt, N. D. (2014) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2593069.2596688 · Cited by 7
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Du, S., Shu, E., Tong, F., Ge, Y., Lu, L., Qiu, J., Guillotel, P., Fleureau, J., Danieau, F., & Muller, D. (2016) · Linköping electronic conference proceedings · doi:10.3384/ecp10302 · Cited by 7
Roshanaei, M., Han, R. Y., & Mishra, S. (2017) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3110025.3110127 · Cited by 7
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Curtis, A., Pai, A., Cao, J. R., Moukaddam, N. J., & Sabharwal, A. (2019) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3300061.3345433 · Cited by 10
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Zard, L. (2025) · Journal of Advertising · doi:10.1080/00913367.2025.2466082 · Cited by 7
Vision-based happiness inference
Sarsenbayeva, Z., Ferreira, D., van Berkel, N., Luo, C., Vaisanen, M., Kostakos, V., & Gonçalves, J. (2017) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3123024.3124438 · Cited by 6
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Torkamaan, H. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3580864 · Cited by 6
Stress Modelling Using Transfer Learning in Presence of Scarce Data
Hernandez-Leal, P., Maxhuni, A., Sucar, L. E., Osmani, V., Morales, E., & Mayora, O. (2015) · International Conference on Ambient Intelligence for Health · doi:10.1007/978-3-319-26508-7_22 · Cited by 11
iGuard: A real-time anti-theft system for smartphones
Jin, M., He, Y., Fang, D., Chen, X., Meng, X., & Xing, T. (2017) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2017.8057021 · Cited by 6
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Feng, X., Lu, X. S., Li, Z., Zhang, M., Li, J., & Zhang, D. (2020) · The Open Psychology Journal · doi:10.2174/1874350102013010169 · Cited by 7
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Bagadia, Y., Tambe, P., Khalil, T., & Shaikh, L. J. S. N. (2016) · Cited by 6
Wayfinding Behavior Detection by Smartphone
Narimoto, R., Kajita, S., Yamaguchi, H., & Teruo Higashino, Y. (2018) · International Conference on Advanced Information Networking and Applications (AINA) · doi:10.1109/aina.2018.00078 · Cited by 6
Costa, P. M., Vasalou, A., Pitt, J. V., Galvão, T., & Cunha, J. F. E. (2013) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/2541831.2541843 · Cited by 6
Zaman, A., Zhang, B., Silenzio, V., Hoque, M. E., & Kautz, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00613 · Cited by 8
A Review on Mood Assessment Using Smartphones
Sarsenbayeva, Z., Fleming, C., Tag, B., Withana, A. I., van Berkel, N., & McEwan, A. L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42283-6_22 · Cited by 5
Relapse prediction: A meteorology-inspired mobile model
Bishop, F. M. (2016) · Health Psychology Open · doi:10.1177/2055102916665934 · Cited by 5
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Ghosh, S., Ganguly, N., Mitra, B., & De, P. (2018) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3241539.3267761 · Cited by 5
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Personal productivity monitoring through smartphones
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Neal, T. J., & Canavan, S. J. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00090 · Cited by 5
Happiness recognition from smartphone usage data considering users’ estimated personality traits
Sadeghian, A., & Kaedi, M. (2021) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2021.101389 · Cited by 5
Context-Aware Probabilistic Models for Predicting Future Sedentary Behaviors of Smartphone Users
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Towards smartphone-based sensing of social interaction for ambulatory assessment
Bachmann, A. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2800835.2801642 · Cited by 5
Wemotion: A System to Detect Emotion Using Wristbands and Smartphones
Le-Quang, B., Dao, M.-S., & Nazmudeen, M. (2018) · Communications in Computer and Information Science · doi:10.1007/978-981-13-5758-9_8 · Cited by 4
Koh, Y., Lee, C., Joung, E., Lee, H., & Lee, U. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3749485 · Cited by 5
Bosschaerts, K., Kashefi, A., Marez, L. D., Conradie, P. D., Hoecke, S. V., & Ongenae, F. (2025) · Digital Health · doi:10.1177/20552076251381747 · Cited by 5
HiNextApp: A context-aware and adaptive framework for app prediction in mobile systems
Liu, D., Xiang, C., Li, S., Ren, J., Liu, R., Liang, L., Guan, Y. L., & Chen, X. (2018) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2018.08.009 · Cited by 4
Impact of experience sampling methods on tap pattern based emotion recognition
Ghosh, S., Chauhan, V., Ganguly, N., Mitra, B., & De, P. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2800835.2804396 · Cited by 4
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Mehrotra, A., & Musolesi, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-409548-9.09673-1 · Cited by 4
HiNextApp: A Context-Aware and Adaptive Framework for App Prediction in Mobile Systems
Xiang, C., Liu, D., Li, S., Zhu, X., Li, Y., Ren, J., & Liang, L. (2017) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom/bigdatase/icess.2017.312 · Cited by 4
Doing and Feeling: Relationships Between Moods, Productivity and Task-Switching
Alibasa, M. J., Purwanto, R. W., Yacef, K., Glozier, N., & Calvo, R. A. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3029440 · Cited by 5
Rad, M. F., Shakeri, M., Roudposhti, K. K., & Shakerinia, I. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01034-3 · Cited by 4
Emotion-aware Human–Robot Interaction and Social Robots
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502407 · Cited by 4
From Outside In: Profiling, Persuasion and Political Opinion in the Age of Big Data
Dhawan, S., & Hegelich, S. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_10 · Cited by 6
Rethinking Wireless Network Management Through Sensor-driven Contextual Analysis
Irshad, S., Rozner, E., Bhartia, A., & Chen, B. (2020) · IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) · doi:10.1145/3376897.3377863 · Cited by 4
Beyond non-inferior: how telepsychiatry technologies can lead to superior care
Zulueta, J., & Ajilore, O. (2020) · International Review of Psychiatry · doi:10.1080/09540261.2020.1835840 · Cited by 4
Human Context Sensing in Smart Cities
Ranjan, J., Griffiths, E., & Whitehouse, K. (2017) · Smart Cities · doi:10.1002/9781119226444.ch11 · Cited by 4
Real World Longitudinal iOS App Usage Study at ScalePreprint
Kim, D.-H., Gluck, J., Hall, M., & Agarwal, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12526 · Cited by 5
Wu, C., Barczyk, A. N., Craddock, R. C., Harari, G. M., Thomaz, E., Shumake, J., Beevers, C. G., Gosling, S. D., & Schnyer, D. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09807 · Cited by 4
Computational PsychologyPreprint
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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
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