{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T21:14:08Z","timestamp":1774127648881,"version":"3.50.1"},"reference-count":92,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T00:00:00Z","timestamp":1608768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Interpersonal trust mediates multiple socio-technical systems and has implications for personal and societal well-being. Consequently, it is crucial to devise novel machine learning methods to infer interpersonal trust automatically using mobile sensor-based behavioral data. Considering that social relationships are often affected by neighboring relationships within the same network, this work proposes using a novel neighbor-aware deep learning architecture (NADAL) to enhance the inference of interpersonal trust scores. Based on analysis of call, SMS, and Bluetooth interaction data from a one-year field study involving 130 participants, we report that: (1) adding information about neighboring relationships improves trust score prediction in both shallow and deep learning approaches; and (2) a custom-designed neighbor-aware deep learning architecture outperforms a baseline feature concatenation based deep learning approach. The results obtained at interpersonal trust prediction are promising and have multiple implications for trust-aware applications in the emerging social internet of things.<\/jats:p>","DOI":"10.3390\/computers10010003","type":"journal-article","created":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T09:02:44Z","timestamp":1608800564000},"page":"3","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["NADAL: A Neighbor-Aware Deep Learning Approach for Inferring Interpersonal Trust Using Smartphone Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9106-7133","authenticated-orcid":false,"given":"Ghassan F.","family":"Bati","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Umm Al-Qura University, Makkah 24381, Saudi Arabia"}]},{"given":"Vivek K.","family":"Singh","sequence":"additional","affiliation":[{"name":"School of Communication and Information, The State University of New Jersey, New Brunswick, NJ 08901, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,24]]},"reference":[{"key":"ref_1","unstructured":"Borum, R. (2018, August 11). The Science of Interpersonal Trust. Available online: https:\/\/scholarcommons.usf.edu\/cgi\/viewcontent.cgi?article=1573&context=mhlp_facpub."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/MIC.2015.24","article-title":"From the Internet of Things to the Internet of People","volume":"19","author":"Miranda","year":"2015","journal-title":"IEEE Internet Comput."},{"key":"ref_3","unstructured":"Sunds\u00f8y, P. (2017). Big Data for Social Sciences: Measuring patterns of human behavior through large-scale mobile phone data. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1038\/488448a","article-title":"Computational social science: Making the links","volume":"488","author":"Giles","year":"2012","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1177\/1745691612441215","article-title":"The smartphone psychology manifesto","volume":"7","author":"Miller","year":"2012","journal-title":"Perspect. Psychol. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s00779-005-0046-3","article-title":"Reality mining: Sensing complex social systems","volume":"10","author":"Eagle","year":"2006","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"De Montjoye, Y.A., Quoidbach, J., Robic, F., and Pentland, A. (2013, January 2\u20135). Predicting personality using novel mobile phone-based metrics. Proceedings of the 6th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, Washington, DC, USA.","DOI":"10.1007\/978-3-642-37210-0_6"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3201577","article-title":"Spatio-Temporal Routine Mining on Mobile Phone Data","volume":"12","author":"Qin","year":"2018","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yabe, T., Sekimoto, Y., Tsubouchi, K., and Ikemoto, S. (2019). Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0211375"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bosse, S., and Engel, U. (2019). Real-Time Human-In-The-Loop Simulation with Mobile Agents, Chat Bots, and Crowd Sensing for Smart Cities. Sensors, 19.","DOI":"10.3390\/s19204356"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1007\/s11036-018-1067-2","article-title":"Discovering Homophily in Online Social Networks","volume":"23","author":"Guidi","year":"2018","journal-title":"Mob. Netw. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3214269","article-title":"DeActive: Scaling Activity Recognition with Active Deep Learning","volume":"2","author":"Hossain","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3214277","article-title":"AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors","volume":"2","author":"Peng","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_14","unstructured":"Wang, J., Chen, Y., Hao, S., Peng, X., and Hu, L. (2017). Deep Learning for Sensor-based Activity Recognition: A Survey. arXiv."},{"key":"ref_15","unstructured":"El Bolock, A., Abdelrahman, Y., and Abdennadher, S. (2020). Character-IoT (CIoT): Toward Human-Centered Ubiquitous Computing. Character Computing, Springer International Publishing."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1016\/j.jrp.2008.07.011","article-title":"Survey and behavioral measurements of interpersonal trust","volume":"42","author":"Evans","year":"2008","journal-title":"J. Res. Personal."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1146\/annurev.psych.50.1.625","article-title":"The Psychological Underpinnings of Democracy: A Selective Review of Research on Political Tolerance, Interpersonal Trust, and Social Capital","volume":"50","author":"Sullivan","year":"1999","journal-title":"Annu. Rev. Psychol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1002\/ejsp.256","article-title":"A social identity approach to trust: Interpersonal perception, group membership and trusting behaviour","volume":"35","author":"Tanis","year":"2005","journal-title":"Eur. J. Soc. Psychol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.joep.2009.10.001","article-title":"Trusting and trustworthiness: What are they, how to measure them, and what affects them","volume":"31","author":"Halldorsson","year":"2010","journal-title":"J. Econ. Psychol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1111\/j.1467-985X.2009.00591.x","article-title":"Measuring people\u2019s trust","volume":"172","author":"Ermisch","year":"2009","journal-title":"J. R. Stat. Soc. Ser. A"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s40750-017-0061-4","article-title":"Breaking Bread: The Functions of Social Eating","volume":"3","author":"Dunbar","year":"2017","journal-title":"Adaptive Human Behavior and Physiology"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1108\/IJRDM-10-2015-0157","article-title":"Role of Trusting Beliefs in Predicting Purchase Intentions","volume":"44","author":"Sahi","year":"2016","journal-title":"Int. J. Retail. Distrib. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/2.970591","article-title":"Trust-based security in pervasive computing environments","volume":"34","author":"Kagal","year":"2001","journal-title":"Computer"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Golbeck, J. (2008). Computing with Social Trust, Springer Science & Business Media.","DOI":"10.1007\/978-1-84800-356-9"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1086\/225469","article-title":"The Strength of Weak Ties","volume":"78","author":"Granovetter","year":"1973","journal-title":"Am. J. Sociol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2379","DOI":"10.1109\/TKDE.2018.2875914","article-title":"Organizing an Influential Social Event Under a Budget Constraint","volume":"31","author":"Han","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/TCSS.2014.2307438","article-title":"Sensing, understanding, and shaping social behavior","volume":"1","author":"Shmueli","year":"2014","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1037\/0021-9010.92.4.909","article-title":"Trust, Trustworthiness, and Trust Propensity: A Meta-Analytic Test of Their Unique Relationships with Risk Taking and Job Performance","volume":"92","author":"Colquitt","year":"2007","journal-title":"J. Appl. Psychol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/3-540-45547-7_3","article-title":"Trust and distrust definitions: One bite at a time","volume":"2246","author":"McKnight","year":"2001","journal-title":"Trust Cyber-Soc."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bati, G.F., and Singh, V.K. (2018, January 21\u201326). \u201cTrust Us\u201d: Mobile Phone Use Patterns Can Predict Individual Trust Propensity. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, QC, Canada.","DOI":"10.1145\/3173574.3173904"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.evolhumbehav.2004.04.002","article-title":"Trustworthiness and Competitive Altruism Can Also Solve the \u201cTragedy of the Commons\u201d","volume":"25","author":"Barclay","year":"2004","journal-title":"Evol. Hum. Behav."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1038\/srep01213","article-title":"Experimental Subjects Are Not Different","volume":"3","author":"Exadaktylos","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13173","DOI":"10.1016\/j.eswa.2012.05.084","article-title":"A Group Trust Metric for Identifying People of Trust in Online Social Networks","volume":"39","author":"Kim","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Adali, S., Escriva, R., Goldberg, M., Hayvanovych, M., Magdon-Ismail, M., Szymanski, B., Wallace, W., and Williams, G. (2010, January 23\u201326). Measuring behavioral trust in social networks. Proceedings of the International Intelligence and Security Informatics (ISI), Vancouver, BC, Canada.","DOI":"10.1109\/ISI.2010.5484757"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1002\/asi.20722","article-title":"Trust in digital information","volume":"59","author":"Kelton","year":"2008","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13673-016-0085-y","article-title":"Trust reality-mining: Evidencing the role of friendship for trust diffusion","volume":"7","author":"Farrahi","year":"2017","journal-title":"Hum. Cent. Comput. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Roy, A., Sarkar, C., Srivastava, J., and Huh, J. (2016, January 18\u201321). Trustingness & trustworthiness: A pair of complementary trust measures in a social network. Proceedings of the IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA.","DOI":"10.1109\/ASONAM.2016.7752289"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.procs.2010.12.137","article-title":"Evolution of trust networks in social web applications using supervised learning","volume":"3","author":"Zolfaghar","year":"2011","journal-title":"Procedia Comput. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1109\/TNNLS.2016.2514368","article-title":"On Deep Learning for Trust-Aware Recommendations in Social Networks","volume":"28","author":"Deng","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2140","DOI":"10.3390\/e17042140","article-title":"Deep belief network-based approaches for link prediction in signed social networks","volume":"17","author":"Liu","year":"2015","journal-title":"Entropy"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bonchi, F. (2011, January 22\u201327). Influence Propagation in Social Networks: A Data Mining Perspective. Proceedings of the 2011 IEEE\/WIC\/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Lyon, France.","DOI":"10.1109\/WI-IAT.2011.286"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bisgin, H., Agarwal, N., and Xu, X. (September, January 31). Investigating homophily in online social networks. Proceedings of the Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE\/WIC\/ACM International Conference, Toronto, ON, Canada.","DOI":"10.1109\/WI-IAT.2010.61"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1093\/socrel\/srz001","article-title":"The Influence of Your Neighbors\u2019 Religions on You, Your Attitudes and Behaviors, and Your Community","volume":"80","author":"Olson","year":"2019","journal-title":"Sociol. Relig."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Fudolig, M.I.D., Bhattacharya, K., Monsivais, D., Jo, H.-H., and Kaski, K. (2020). Link-centric analysis of variation by demographics in mobile phone communication patterns. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0227037"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lane, N.D., Li, P., Zhou, L., and Zhao, F. (2014, January 13\u201317). Connecting personal-scale sensing and networked community behavior to infer human activities. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA.","DOI":"10.1145\/2632048.2636094"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/SURV.2012.031412.00077","article-title":"Mobile Phone Sensing Systems: A Survey","volume":"15","author":"Khan","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_47","unstructured":"Singh, V.K., and Agarwal, R.R. (2016, January 12\u201316). Cooperative phoneotypes: Exploring phone-based behavioral markers of cooperation. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ponciano, V., Pires, I.M., Ribeiro, F.R., Villasana, M.V., Teixeira, M.C., and Zdravevski, E. (2020). Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test. Computers, 9.","DOI":"10.20944\/preprints202008.0159.v1"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3090076","article-title":"Ensembles of Deep LSTM Learners for Activity Recognition using Wearables","volume":"1","author":"Guan","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3161201","article-title":"An LSTM Based System for Prediction of Human Activities with Durations","volume":"1","author":"Krishna","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.pmcj.2011.09.004","article-title":"Social fMRI: Investigating and shaping social mechanisms in the real world","volume":"7","author":"Aharony","year":"2011","journal-title":"Pervasive Mob. Comput."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Adali, S. (2013). Modeling Trust Context in Networks, Springer.","DOI":"10.1007\/978-1-4614-7031-1"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3134730","article-title":"Inferring Individual Social Capital Automatically via Phone Logs","volume":"1","author":"Singh","year":"2017","journal-title":"Proc. ACM Hum. Comput. Interact."},{"key":"ref_54","unstructured":"Rauber, J., Fox, E.B., and Gatys, L.A. (2019). Modeling patterns of smartphone usage and their relationship to cognitive health. arXiv."},{"key":"ref_55","first-page":"41","article-title":"Social capital: Measurement and consequences","volume":"2","author":"Putnam","year":"2001","journal-title":"Can. J. Policy Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1353\/jod.1995.0002","article-title":"Bowling alone: America\u2019s declining social capital","volume":"6","author":"Putnam","year":"1995","journal-title":"J. Democr."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1111\/j.1083-6101.2006.00029.x","article-title":"On and Off the\u2019 Net: Scales for Social Capital in an Online Era","volume":"11","author":"Williams","year":"2006","journal-title":"J. Comput. Mediat. Commun."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yakoub, F., Zein, M., Yasser, K., Adl, A., and Hassanien, A.E. (2015). Predicting personality traits and social context based on mining the smartphones SMS data. Intelligent Data Analysis and Applications, Springer.","DOI":"10.1007\/978-3-319-21206-7_44"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Jin, L., Gao, S., Li, Z., and Tang, J. (2014, January 10\u201312). Hand-Crafted Features or Machine Learnt Features? Together They Improve RGB-D Object Recognition. Proceedings of the 2014 IEEE International Symposium on Multimedia, Taichung, Taiwan.","DOI":"10.1109\/ISM.2014.56"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Li, W., Manivannan, S., Akbar, S., Zhang, J., Trucco, E., and McKenna, S.J. (2016, January 13\u201316). Gland segmentation in colon histology images using handcrafted features and convolutional neural networks. Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic.","DOI":"10.1109\/ISBI.2016.7493530"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Majtner, T., Yildirim-Yayilgan, S., and Hardeberg, J.Y. (2016, January 12\u201315). Combining Deep Learning and Hand-Crafted Features for Skin Lesion Classification. Proceedings of the 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), Oulu, Finland.","DOI":"10.1109\/IPTA.2016.7821017"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Antipov, G., Berrani, S.-A., Ruchaud, N., and Dugelay, J.-L. (2015, January 26\u201330). Learned vs. Hand-Crafted Features for Pedestrian Gender Recognition. Proceedings of the 23rd ACM international conference on Multimedia\u2014MM \u201915, Brisbane, Australia.","DOI":"10.1145\/2733373.2806332"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1145\/1183463.1183470","article-title":"Inferring binary trust relationships in web-based social networks","volume":"6","author":"Golbeck","year":"2006","journal-title":"ACM Trans. Internet Technol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Greenspan, S., Goldberg, D., Weimer, D., and Basso, A. (2000, January 2\u20136). Interpersonal Trust and Common Ground in Electronically Mediated Communication. Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, Philadelphia, PA, USA.","DOI":"10.1145\/358916.358996"},{"key":"ref_65","first-page":"5","article-title":"On the Trusted Use of Large-Scale Personal Data","volume":"35","author":"Wang","year":"2012","journal-title":"IEEE Data Eng. Bull."},{"key":"ref_66","first-page":"99","article-title":"Classifying spending behavior using socio-mobile data","volume":"2","author":"Singh","year":"2013","journal-title":"Hum. J."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Gilbert, E., and Karahalios, K. (2009, January 4\u20139). Predicting tie Strength with Social Media. Proceedings of the 27th International Conference on Human Factors in Computing Systems, Boston, MA, USA.","DOI":"10.1145\/1518701.1518736"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"377","DOI":"10.2307\/256367","article-title":"The strength of strong ties: Social networks and intergroup conflict in organizations","volume":"32","author":"Nelson","year":"1989","journal-title":"Acad. Manag. J."},{"key":"ref_69","unstructured":"Gao, J., Schoenebeck, G., and Yu, F.-Y. (2019, January 13\u201317). The Volatility of Weak Ties: Co-evolution of Selection and Influence in Social Networks. Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, Montreal, QC, Canada."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Singh, V.K., Bozkaya, B., and Pentland, A. (2015). Money walks: Implicit mobility behavior and financial well-being. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0136628"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.paid.2013.05.001","article-title":"Creatures of the night: Chronotypes and the Dark Triad traits","volume":"55","author":"Jonasona","year":"2013","journal-title":"Personal. Individ. Differ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.paid.2015.06.039","article-title":"Feeling me, feeling you? Links between the Dark Triad and internal body awareness","volume":"86","author":"Lyons","year":"2015","journal-title":"Personal. Individ. Differ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/0191-8869(91)90110-W","article-title":"Horne & \u00d6stberg morningness-eveningness questionnaire: A reduced scale","volume":"12","author":"Adan","year":"1991","journal-title":"Personal. Individ. Differ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s11859-014-1027-z","article-title":"Temporal dynamics in social trust prediction","volume":"19","author":"Cai","year":"2014","journal-title":"Wuhan Univ. J. Nat. Sci."},{"key":"ref_75","first-page":"559","article-title":"Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning","volume":"18","author":"Nogueira","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_76","unstructured":"Batista, G.E., Bazzan, A.L., and Monard, M.C. (2003, January 3\u20135). Balancing Training Data for Automated Annotation of Keywords: A Case Study. Proceedings of the Brazilian Workshop on Bioinformatics, Maca\u00e9, RJ, Brazil."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ijaz, M.F., Attique, M., and Son, Y. (2020). Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods. Sensors, 20.","DOI":"10.3390\/s20102809"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"129678","DOI":"10.1109\/ACCESS.2019.2940061","article-title":"SMOTETomek-Based Resampling for Personality Recognition","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_80","unstructured":"(2018, August 11). Techniques to Deal with Imbalanced Data Kaggle. Available online: https:\/\/www.kaggle.com\/npramod\/techniques-to-deal-with-imbalanced-data."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.socnet.2010.05.001","article-title":"Trust in triads: An experimental study","volume":"32","author":"Buskens","year":"2010","journal-title":"Soc. Netw."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.im.2016.12.003","article-title":"Applying network analysis to investigate interpersonal influence of information security behaviours in the workplace","volume":"54","author":"Pittayachawan","year":"2017","journal-title":"Inf. Manag."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1145\/3161174","article-title":"Multimodal deep learning for activity and context recognition","volume":"1","author":"Radu","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_84","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_85","unstructured":"Chollet, F. (2018, August 10). Others Keras. Available online: https:\/\/keras.io."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Chawla, N.V. (2009). Data mining for imbalanced datasets: An overview. Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/978-0-387-09823-4_45"},{"key":"ref_87","unstructured":"Zheng, A. (2015). Evaluating Machine Learning Models a Beginner\u2019s Guide to Key Concepts and Pitfalls, O\u2019Reilly Media, Inc."},{"key":"ref_88","first-page":"203","article-title":"Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency","volume":"13","author":"Tufekci","year":"2015","journal-title":"Colo. Technol. Law J."},{"key":"ref_89","first-page":"143","article-title":"Privacy and security in mobile health (mHealth) research","volume":"36","author":"Shifali","year":"2014","journal-title":"Alcohol Res. Curr. Rev."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Jin, H., Su, L., Ding, B., Nahrstedt, K., and Borisov, N. (2016, January 27\u201330). Enabling Privacy-Preserving Incentives for Mobile Crowd Sensing Systems. Proceedings of the 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), Nara, Japan.","DOI":"10.1109\/ICDCS.2016.50"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.hlpt.2015.04.008","article-title":"Exploring factors impacting sharing health-tracking records","volume":"4","author":"Ivanov","year":"2015","journal-title":"Health Policy Technol."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"M\u00f6hlmann, M., and Geissinger, A. (2018). Trust in the Sharing Economy: Platform-Mediated Peer Trust. Cambridge Handbook on the Law of the Sharing, Cambridge University Press.","DOI":"10.1017\/9781108255882.003"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/1\/3\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:45:29Z","timestamp":1760179529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/1\/3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,24]]},"references-count":92,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["computers10010003"],"URL":"https:\/\/doi.org\/10.3390\/computers10010003","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,24]]}}}