The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Social sensing for epidemiological behavior change
Open accessAbstract
An important question in behavioral epidemiology and public health is to understand how individual behavior is affected by illness and stress. Although changes in individual behavior are intertwined with contagion, epidemiologists today do not have sensing or modeling tools to quantitatively measure its effects in real-world conditions. In this paper, we propose a novel application of ubiquitous computing. We use mobile phone based co-location and communication sensing to measure characteristic behavior changes in symptomatic individuals, reflected in their total communication, interactions with respect to time of day (e.g., late night, early morning), diversity and entropy of face-to-face interactions and movement. Using these extracted mobile features, it is possible to predict the health status of an individual, without having actual health measurements from the subject. Finally, we estimate the temporal information flux and implied causality between physical symptoms, behavior and mental health.
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
“Individual mobility behaviour changes in fever, influenza or mental stress situations [116].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [116]
In the citing paper
“Finally the health status of an individual is predicted with a Bayesian network classifier, without having actual health measurements from the subjects.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [116]
In the citing paper
“Behavioural changes are reflected in total communication, interactions with respect to time of day (e.g. late night, early morning), diversity and entropy of face-to-face interactions and movement.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [116]
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
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,186
The Spread of Obesity in a Large Social Network over 32 Years
Christakis, N. A., & Fowler, J. H. (2007) · New England Journal of Medicine · doi:10.1056/nejmsa066082 · Cited by 5,007
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
Self-citationThe collective dynamics of smoking in a large social network
Christakis, N. A., & Fowler, J. H. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa0706154 · Cited by 2,390
Fowler, J. H., & Christakis, N. A. (2008) · BMJ · doi:10.1136/bmj.a2338 · Cited by 1,990
Inferring friendship network structure by using mobile phone data
Eagle, N., Pentland, A. (., & Lazer, D. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0900282106 · Cited by 1,856
Self-citationClinical Epidemiology: The Essentials.
(1991) · Annals of Internal Medicine · doi:10.7326/0003-4819-114-9-825_10 · Cited by 1,838
Domingos, P. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312220 · Cited by 1,325
Stress and infectious disease in humans.
Cohen, S., & Williamson, G. M. (1991) · Psychological Bulletin · doi:10.1037/0033-2909.109.1.5 · Cited by 756
Research works printed but not linked: 5
- }} Fletcher et. al. Clinical Epidimiology : The Essentials . Williams and Wilkins , 2005 . }}Fletcher et. al. Clinical Epidimiology: The Essentials. Williams and Wilkins, 2005.
- }} Miettinen . Theoretical Epidimiology: principles of occurence research in medicine . Wiley New York , 1985 . }}Miettinen. Theoretical Epidimiology: principles of occurence research in medicine. Wiley New York, 1985.
- }}S. Milgram . The Familiar Stranger : An Aspect of Urban Anonymity. The Individual in a Social World: Essays and Experiments , Longman , 1977 . }}S. Milgram. The Familiar Stranger: An Aspect of Urban Anonymity. The Individual in a Social World: Essays and Experiments, Longman, 1977.
- }} Olguin D. , Gloor P. , Pentland A. Capturing Individual and Group Behavior Using Wearable Sensors. In AAAI Spring Symposium, Human Behavior Modeling , Palo Alto, CA , 2009 . }}Olguin D., Gloor P., Pentland A. Capturing Individual and Group Behavior Using Wearable Sensors. In AAAI Spring Symposium, Human Behavior Modeling, Palo Alto, CA, 2009.
- }}Rang, H.P., Dale, M.M., Ritter, J.M., Moore, P.K. Pharmacology 5 th Edition. Churchill Livingstone , Edinburg , 2003 . }}Rang, H.P., Dale, M.M., Ritter, J.M., Moore, P.K. Pharmacology 5th Edition. Churchill Livingstone, Edinburg, 2003.
Cited by
Sorted by citations
Items 101–200 of 218
Inferring social activities with mobile sensor networks
Tri, T. M., Kalimeri, K., Lepri, B., Pianesi, F., & Gática-Pérez, D. (2013) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2522848.2522894 · Cited by 23
Komlósi, É., Sebestyén, T., Tóth-Pajor, Á., & Bedő, Z. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121349 · Cited by 23
Human Behavior Understanding for Inducing Behavioral Change: Application Perspectives
Salah, A. A., Lepri, B., Pianesi, F., & Pentland, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25446-8_1 · Cited by 22
Self-citationInformation flow and cognition affect each other: Evidence from digital learning
Sharma, K., Mangaroska, K., van Berkel, N., Giannakos, M. N., & Kostakos, V. (2020) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102549 · Cited by 27
He, Q., & Agu, E. (2014) · doi:10.1145/2611264.2611268 · Cited by 22
Introduction to Multimedia Big Data Computing for IoT
Sharmila, A., Kumar, D., Kumar, P., & Ashok, A. (2019) · Intelligent systems reference library · doi:10.1007/978-981-13-8759-3_1 · Cited by 21
Fusing location data for depression prediction
Yue, C., Ware, S., Morillo, R., Lü, J., Shang, C., Bi, J., Kamath, J., Russell, A. C., Bamis, A., & Wang, B. (2017) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/uic-atc.2017.8397515 · Cited by 22
Decomposing Activities of Daily Living to Discover Routine Clusters
Yürüten, O., Zhang, J., & Pu, P. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8902 · Cited by 20
Personalised models for speech detection from body movements using transductive parameter transfer
Gedik, E., & Hung, H. (2017) · Personal and Ubiquitous Computing · doi:10.1007/s00779-017-1006-4 · Cited by 17
The Impact of Spatial Resolution and Representation on Human Mobility Predictability
Qian, W., Stanley, K. G., & Osgood, N. D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37087-8_3 · Cited by 18
Remote Sensing of Atmospheric Optical Depth Using a Smartphone Sun Photometer
Cao, T., & Thompson, J. E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0084119 · Cited by 18
Dong, W., Guan, T., Lepri, B., & Qiao, C. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3328912 · Cited by 20
Behavior-Aware Computing: Applications and Challenges
Favela, J. (2013) · IEEE Pervasive Computing · doi:10.1109/mprv.2013.44 · Cited by 17
The Ubiquitous Role of Smartphones in Mobile Health
Appelboom, G. (2014) · Biometrics & Biostatistics International Journal · doi:10.15406/bbij.2014.01.00004 · Cited by 17
Beyond magic bullets: true innovation in health care
Narayan, V. A., Mohwinckel, M., Pisano, G. P., Yang, M. J., & Manji, H. K. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd3944 · Cited by 16
Connecting people through physical proximity and physical resources at a conference
Chin, A., Xu, B., Wang, H., Chang, L., Wang, H., & Zhu, L. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2483669.2483683 · Cited by 16
Toward Integrated Urban Observatories: Synthesizing Remote and Social Sensing in Urban Science
Yu, D. (2025) · Remote Sensing · doi:10.3390/rs17122041 · Cited by 15
A System to Analyze Group Socializing Behaviors in Social Parties
Jang, H., Choe, S. P., Gunkel, S. N. B., Kang, S., & Song, J. (2016) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2016.2634918 · Cited by 15
Consolvo, S., Bentley, F. R., Hekler, E. B., & Phatak, S. S. (2017) · Synthesis lectures on mobile and pervasive computing · doi:10.2200/s00763ed1v01y201703mpc012 · Cited by 14
Modeling and inferring mobile phone users’ negative emotion spreading in social networks
Du, Z., Yang, Y., Cai, Q., Zhang, C., & Bai, Y. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.04.015 · Cited by 15
Pejović, V., Mehrotra, A., & Musolesi, M. (2016) · Springer eBooks · doi:10.1007/978-3-319-45142-8_15 · Cited by 23
Liu, S., Hachen, D. S., Ližardo, O., Poellabauer, C., Striegel, A., & Milenković, T. (2018) · Applied Network Science · doi:10.1007/s41109-018-0103-2 · Cited by 14
A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams
Yan, R., Liu, X., Dutcher, J. M., Tumminia, M. J., Villalba, D. K., Cohen, S., Creswell, D. J., Creswell, K. G., Mankoff, J. C., Dey, A. K., et al. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3510029 · Cited by 14
Kumar, R. B., Longhurst, C., Wall, D. P., & Stark, D. E. (2016) · Applied Clinical Informatics · doi:10.4338/aci-2015-12-le-0176 · Cited by 13
Analytic Challenges in Social Sensing
Abdelzaher, T., & Wang, D. (2013) · Signals and communication technology · doi:10.1007/978-3-642-40066-7_16 · Cited by 12
Zhang, D., Qiang, M., Jiang, H., Wen, Q., An, N., & Xia, B. (2018) · Water Policy · doi:10.2166/wp.2018.141 · Cited by 12
Unnikrishnan, V., Shah, Y., Schleicher, M., Strandzheva, M., Dimitrov, P., Velikova, D., Pryss, R. C., Schobel, J., Schlee, W., & Spiliopoulou, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61527-7_43 · Cited by 11
Socio-Technical Network Analysis from Wearable Interactions
Farrahi, K., Emonet, R., & Ferscha, A. (2012) · International Symposium on Wearable Computers · doi:10.1109/iswc.2012.19 · Cited by 11
Trade-Offs in Social and Behavioral Modeling in Mobile Networks
Altshuler, Y., Fire, M., Aharony, N., Volkovich, Z., Elovici, Y., & Pentland, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37210-0_45 · Cited by 11
Self-citationLinking people through physical proximity in a conference
Chin, A., Xu, B., Hao, W., & Wang, X. (2012) · International Workshop on Modeling Social Media (MSM) · doi:10.1145/2310057.2310061 · Cited by 10
Passive Health Monitoring Using Large Scale Mobility Data
Zhang, Y., Xu, F., Li, T., Kostakos, V., Hui, P., & Li, Y. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448078 · Cited by 10
Change in BMI Accurately Predicted by Social Exposure to Acquaintances
Oloritun, R. O., Ouarda, T. B. M. J., Moturu, S. T., Madan, A., Pentland, A., & Khayal, I. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079238 · Cited by 10
Self-citationA probabilistic approach to socio-geographic reality mining
Farrahi, K. (2011) · ACM SIGMultimedia Records · doi:10.1145/2069203.2069206 · Cited by 9
Leveraging H1N1 infection transmission modeling with proximity sensor microdata
Hashemian, M. S., Stanley, K. G., & Osgood, N. D. (2012) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-12-35 · Cited by 9
Detecting pathogen exposure during the non-symptomatic incubation period using physiological dataPreprint
Milechin, L., Davis, S. K., Patel, T., Hernandez, M. A., Ciccarelli, G., Schwartz, S., Samsi, S., Hensley, L. E., Goff, A. J., Trefry, J. C., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/218818 · Cited by 10
Li, Y., & Du, H. (2021) · Journal of Asian Architecture and Building Engineering · doi:10.1080/13467581.2021.1972812 · Cited by 9
Social networks and social capital: New directions for a household panel survey
Tach, L., & Cornwell, B. (2015) · Journal of Economic and Social Measurement · doi:10.3233/jem-150413 · Cited by 5
Stealing Reality: When Criminals Become Data Scientists (or Vice Versa)
Altshuler, Y., Aharony, N., Elovici, Y., Pentland, A., & Cebrián, M. (2012) · Springer eBooks · doi:10.1007/978-1-4614-4139-7_7 · Cited by 8
Self-citationPredicting a Community's Flu Dynamics with Mobile Phone Data
Farrahi, K., Emonet, R., & Cebrián, M. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675237 · Cited by 8
Passive detection of behavioral shifts for suicide attempt preventionPreprint
Moreno-Muñoz, P., Romero-Medrano, L., Moreno, Á., Herrera-López, J., Baca‐García, E., & Artés-Rodrı́guez, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09848 · Cited by 8
A Review of Air Quality Monitoring System Based on Crowdsensing
Huang, M., & Wu, X. (2019) · Smart innovation, systems and technologies · doi:10.1007/978-981-13-7542-2_28 · 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
Human Behavior Understanding for Inducing Behavioral Change: Social and Theoretical Aspects
Lepri, B., Salah, A. A., Pianesi, F., & Pentland, A. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-31479-7_44 · Cited by 7
Self-citationPejović, V., Mehrotra, A., & Musolesi, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.03722 · Cited by 7
Trust reality-mining: evidencing the role of friendship for trust diffusion
Farrahi, K., & Zia, K. (2017) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-016-0085-y · Cited by 6
Wlodarczak, P., Soar, J., & Ally, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19156-0_1 · Cited by 6
Whose posts to read: Finding social sensors for effective information acquisition
Yuan, K., Liu, G., & Wu, J. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.01.009 · Cited by 6
Lee, K., Sato, D., Asakawa, S., Asakawa, C., & Kacorri, H. (2021) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3441852.3471232 · Cited by 7
Mestry, M., Mehta, J., Mishra, A., & Gawande, K. (2015) · International Conference on Communication, Information & Computing Technology · doi:10.1109/iccict.2015.7045656 · Cited by 6
Davis, S. K., Milechin, L., Patel, T., Hernandez, M. A., Ciccarelli, G., Samsi, S., Hensley, L. E., Goff, A. J., Trefry, J. C., Johnston, S. C., et al. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.691074 · Cited by 6
Osores, M. E. M. (2015) · Dialnet (Universidad de la Rioja) · Cited by 6
On quantifying the quality of information in social sensing
Wang, D. (2012) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 5
Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening
Tlachac, M., Heinz, M. V., Reisch, M., & Ogden, S. S. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3643554 · Cited by 5
Parallel Continuous k-Nearest Neighbor Computing in Location Based Spatial Networks on GPUs
Liao, W., Zhiming, Z., Zhimin, Y., Wei, F., & Wu, X. (2013) · International Conference on Computational and Information Sciences · doi:10.1109/iccis.2013.79 · Cited by 5
Mobile monitoring parents’ behaviors for supporting self-management in children with disabilities
Macías, A., Hinojos-Ramos, J. E., Valdez, C., Garcia, I., Paez, G., Caro, K., & Castro, L. A. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01293-3 · Cited by 5
Shin, D. Y., Arisona, S. M., Georgakopoulou, S., Schmitt, G., & Kim, S. (2012) · eCAADe proceedings · doi:10.52842/conf.ecaade.2012.2.377 · Cited by 5
Modeling and Analysis of Spatiotemporal Social Networks
Brugere, I., Gunturi, V. M. V., & Shekhar, S. (2014) · Springer eBooks · doi:10.1007/978-1-4614-6170-8_320 · Cited by 5
Periñán-Pascual, C. (2023) · Applied Ontology · doi:10.3233/ao-230281 · Cited by 5
Jackson, K. M., Meisel, M. K., Sokolovsky, A. W., Chen, K., & Barnett, N. P. (2024) · JMIR Research Protocols · doi:10.2196/50650 · Cited by 5
Assessing contextual mood in public transport
Costa, P. M., Pitt, J. V., Galvão, T., & Cunha, J. F. E. (2013) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2493190.2494429 · Cited by 4
Detection and Analysis of Interrupted Behaviors by Public Policy Interventions during COVID-19
Dong, G., Cai, L., Kumar, S., Datta, D., Barnes, L. E., & Boukhechba, M. (2021) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase52844.2021.00013 · Cited by 4
A Picture of Present Ubicomp Research Exploring Publications from Important Events in the Field
Silva, T. H., Celes, C. S. F. D. S., Mota, V. F. S., & Loureiro, A. A. F. (2012) · Journal of Applied Computing Research · doi:10.4013/jacr.2012.21.04 · Cited by 4
Modeling and Analysis of Spatiotemporal Social Networks
Gunturi, V. M. V., Brugere, I., & Shekhar, S. (2018) · Springer eBooks · doi:10.1007/978-1-4939-7131-2_320 · Cited by 4
Michalitsi-Psarrou, A., Παπαγεωργίου, Ι. Λ., Ntanos, C., & Psarras, J. E. (2021) · Applied Sciences · doi:10.3390/app11146530 · Cited by 3
Long-Term Behavioural Change Detection through Pervasive Sensing
Kemp, J., Gaura, E. I., Rednic, R., & Brusey, J. (2013) · ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) · doi:10.1109/snpd.2013.69 · Cited by 3
Прогнозирование психологических характеристик человека на основании его цифровых следов
Латынов, В. В., & Овсянникова, В. В. (2020) · Психология Журнал Высшей школы экономики · doi:10.17323/1813-8918-2020-1-166-180 · Cited by 3
Social fmri: measuring and designing social mechanisms using mobile phones
Pentland, A., & Aharony, N. (2012) · Cited by 3
Self-citationModeling and Analysis of Spatiotemporal Social Networks
Gunturi, V. M. V., Brugere, I., & Shekhar, S. (2016) · Springer eBooks · doi:10.1007/978-1-4614-7163-9_320-1 · Cited by 3
Towards a Reusable Infrastructure for Crowdsourcing
Hara, T., Springer, T., Muthmann, K., & Schill, A. (2014) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc.2014.98 · Cited by 2
The Effect of Social Networks on Active Living in Adolescents: Qualitative Focus Group Study
Hermsen, S., van Abswoude, F., & Steenbergen, B. (2023) · JMIR Formative Research · doi:10.2196/46350 · Cited by 7
Modeling User Context from Smartphone Data for Recognition of Health Status
Karanth, R., Guyer, M. S., Twilley, N. L., Crosier, M. B., Monroe, S. C., McQuain, A. J., Kha, L. T., Boukhechba, M., Gerber, M. S., & Barnes, L. E. (2019) · Systems and Information Engineering Design Symposium · doi:10.1109/sieds.2019.8735626 · Cited by 2
Thought and Life Logging: A Pilot Study
Hernandez-Cruz, N., Yavuz, G. R., Eşrefoğlu, R., Kepez, T., Özdemir, A., Demiray, B., Alan, H., Ersoy, C., Untersander, S., & Bert Arnrich, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26401-1_3 · Cited by 2
Chronic Knowledge Retrieval and Smart Health Services Based on Big Data
Liang, Y., Guo, N., Xing, C., Zhang, Y., & Guo, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29175-8_22 · Cited by 2
Activity, Behavior and Context: The ABC of Pervasive Healthcare Research
Favela, J. (2012) · Ambient intelligence and smart environments · doi:10.3233/978-1-61499-080-2-4 · Cited by 2
Social sensing trends and applications
Dong, W., Abdelzaher, T., & Kaplan, L. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-800867-6.00002-9 · Cited by 2
Fréjus, M., & Guibourdenche, J. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80091-8_127 · Cited by 2
Haddad, H., Moulin, B., & Thériault, M. (2014) · Wiley series in probability and statistics · doi:10.1002/9781118630013.ch19 · Cited by 2
Modeling Corporate EpidemiologyPreprint
Waber, B. N., Pollock, E., Cebrián, M., Crane, R., Danon, L., & Pentland, A. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1008.3402 · Cited by 2
Self-citationWangwiwattana, C., Aggarwal, S., & Larson, E. C. (2021) · arXiv (Cornell University) · doi:10.13140/rg.2.2.18349.10721 · Cited by 3
The Need for Systems Thinking in Digital Health Transformation
Khayal, I. S. (2025) · INCOSE International Symposium · doi:10.1002/iis2.70089 · Cited by 2
A data-rich approach for investigating social mechanisms in the wild
Aharony, N. (2011) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2030112.2030191 · Cited by 1
Hanel, A., Kloeden, H., Ludwig Hoegner, B., & Stilla, U. (2015) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-3-w3-161-2015 · Cited by 1
Automatic Discovery of Emerging Trends using Cluster Name Synthesis on User Consumption Data
Chattopadhyay, T., Maiti, S., Pal, A., Ghose, A., Pal, A., Viswanathan, S., & Sivakumar, N. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872518.2891116 · Cited by 1
Pervasive Data Capturing and Analysis for Patients with Alzheimer's Disease
Lam, K.-Y., Tsang, N. W.-H., Ng, J. K., Wang, J., Kam, C. H. C., & Han, S. (2015) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-4666-7481-3.ch012 · Cited by 1
The Improvement of Safety Climate of Migrant Workers Based on Social Mining Technologies
Xu, S. (2017) · Springer eBooks · doi:10.1007/978-981-10-6190-5_126 · Cited by 1
Barnett, N. P., Sokolovsky, A. W., Meisel, M. K., Forkus, S. R., & Jackson, K. M. (2023) · JMIR Research Protocols · doi:10.2196/50241 · Cited by 4
Vianna, H. D., Barbosa, J. L. V., Gluz, J. C., & Marques, E. B. (2016) · Latin American Computer Conference (CLEI) · doi:10.1109/clei.2016.7833368 · Cited by 1
Charting the Past, Present, and Future in Mobile Sensing Research and Development
Castro, L. A., Rodríguez, M. D., Martínez, F., Rodríguez, L.-F., Andrade, Á. G., & Cornejo, R. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812130-6.00001-9 · Cited by 1
An Exploratory Study to Detect Temporal Orientation Using Bluetooth's sensor
Hernandez-Cruz, N., Demiray, B., Bert Arnrich, A., & Favela, J. (2019) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3329189.3329223 · Cited by 1
Privacy preservation and auditing for ubiquitous mobile sensing applications and services
Sumter, G. C. (2012) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1
Resilient Big Data MonetizationPreprint
Kamal, R., & Hong, C. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.04545 · Cited by 1
Unsupervised Online Bayesian Autonomic Happy Internet-of-Things ManagementPreprint
Kamal, R., & Hong, C. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.06856 · Cited by 1
COLLABORATIVE OPPORTUNISTIC SENSING OF HUMAN BEHAVIOR WITH MOBILE PHONES
Castro, L. A., Beltrán, J., Favela, J., Chávez, E., Perez, M., Rodríguez, M. D., Navarro, R. F., & Quintana, E. (2016) · doi:10.1002/9781118993620.ch5 · Cited by 1
Analysing crowd behaviours using mobile sensing
Katevas, K. (2018) · Queen Mary Research Online (Queen Mary University of London) · Cited by 1
Yan, R., Liu, X., Dutcher, J. M., Tumminia, M. J., Villalba, D. K., Cohen, S., Creswell, D. J., Creswell, K. G., Mankoff, J. C., Dey, A. K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.10.244020 · Cited by 1
Social Genome Mining for Crisis PredictionPreprint
Wlodarczak, P., Qian, S., Ally, M., & Soar, J. (2015) · SSRN Electronic Journal
Human Dynamic Networks in Opportunistic Routing and Epidemiology
Hashemian, M. S. (2011) · University Library - University of Saskatchewan (University of Saskatchewan)
Detecting expectation-based spatio-temporal clusters formed during opportunistic sensing
Orlinski, M., & Filer, N. P. (2014) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2014.6815271
Osmani, V., Mayora, O., & Imrich Chlamtac, A. (2012)
Towards a unified modelling framework for adaptive networks.
Liu, X. (2014) · University of the Western Cape Electronic Theses and Dissertations Repository (University of the Western Cape)
Metadata
- Authors
- Madan, A., Cebrian, M., Lazer, D., & Pentland, A.
- Affiliations
- Massachusetts Institute of Technology (US)
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard University (US)
- Venue
- Proceedings of the 12th ACM international conference on Ubiquitous computing
- Publisher
- ACM
- Published
- 2010-09-26
- Pages
- 291-300
- OpenAlex
- W2171436624
- Cited by
- 219
- References
- 28
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 6 · 2022: 7 · 2021: 14 · 2020: 14 · 2019: 14 · 2018: 17 · 2017: 15 · 2016: 24 · 2015: 22 · 2014: 19 · 2013: 20 · 2012: 25
- FWCI
- 42.39
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- social sensing, behavioral epidemiology, mobile phone data, face-to-face interaction, movement patterns, health prediction, mental health, ubiquitous computing
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
