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International Conference on Asian Digital Libraries

ICADL 2020: Digital Libraries at Times of Massive Societal Transition pp 287–296Cite as

Identifying the Types of Digital Footprint Data Used to Predict Psychographic and Human Behaviour

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12504))

Abstract

Digital footprints can be defined any data related to any online activity. When engaging, the user leaves digital footprints that can be tracked across a range of digital activities, such as web explorer, checked-in location, YouTube, photo-tag and record purchase. Indeed, the use of all social media applications is also part of the digital footprint. This research was, therefore conducted to classify the types of digital footprint data used to predict psychographic and human behaviour. A systematic analysis of 48 studies was undertaken to examine which form of digital footprint was taken into account in ongoing research. The results show that there are different types of data from digital footprints, such as structured data, unstructured data, geographic data, time-series data, event data, network data, and linked data. In conclusion, the use of digital footprint data is a practically new way of completing research into predicting psychographic and human behaviour. The use of digital footprint data also provides a tremendous opportunity for enriching insights into human behaviour.

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References

  1. Merriam-Webster Dictionary (1828). https://www.merriam-webster.com/dictionary/psychographics

  2. Walker, B., Albertson, C., Freeberg, R.: Psychographic Segmentation and the Health Care Consumer. TPG, Philadelphia (2014)

    Google Scholar 

  3. Chen, Y.J., Chen, Y.M., Hsu, Y.J., Wu, J.H.: Predicting consumers’ decision-making styles by analyzing digital footprints on Facebook. Int. J. Inf. Technol. Decis. Making 18, 601–627 (2019)

    Article  Google Scholar 

  4. Lambiotte, R., Kosinski, M.: Tracking the digital footprints of personality. Proc. IEEE 102, 1934–1939 (2014)

    Article  Google Scholar 

  5. Herring, S.C., Kapidzic, S.: Teens, gender, and self-presentation in social media. In: International Encyclopedia of the Social & Behavioral Sciences, pp. 146–152 (2015)

    Google Scholar 

  6. Keresteš, G., Štulhofer, A.: Adolescents’ online social network use and life satisfaction: a latent growth curve modeling approach. Comput. Hum. Behav. 104, 106187 (2019)

    Article  Google Scholar 

  7. Ophir, Y., Asterhan, C.S.C., Schwarz, B.B.: The digital footprints of adolescent depression, social rejection and victimization of bullying on Facebook. Comput. Hum. Behav. 91, 62–71 (2018)

    Article  Google Scholar 

  8. Martin, F., Gezer, T., Wang, C.: Educators’ perception of student digital citizenship practices. Comput. Sch. 36(4), 238–254 (2019)

    Article  Google Scholar 

  9. Tommasel, A., Rodriguez, J.M., Godoy, D.: Textual aggression detection through deep learning. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying, pp. 177–187 (2018)

    Google Scholar 

  10. Vigna, F.D., Cimino, A., Dell’Orletta, F., Petrocchi, M., Tesconi, M.: Hate me, hate me not: hate speech detection on Facebook. In: Proceedings of the First Italian Conference on Cybersecurity, pp. 86–95 (2017)

    Google Scholar 

  11. Byrne, E., Vessey, J.A., Pfeifer, L.: Cyberbullying and social media: information and interventions for school nurses working with victims, students, and families. J. School Nurs. 34(1), 38–50 (2017)

    Article  Google Scholar 

  12. Van Ouytsel, J., Ponnet, K., Walrave, M., d’Haenens, L.: Adolescent sexting from a social learning perspective. Telematics Inform. 34(1), 287–298 (2017)

    Google Scholar 

  13. Morelli, M., Bianchi, D., Baiocco, R., Pezzuti, L., Chirumbolo, A.: Sexting behaviors and cyber pornography addiction among adolescents: the moderating role of alcohol consumption. Sex. Res. Soc. Policy 14(2), 113–121 (2016). https://doi.org/10.1007/s13178-016-0234-0

    Article  Google Scholar 

  14. Gok, T.: The effects of social networking sites on students’ studying and habits. Int. J. Res. Educ. Sci. (IJRES) 2(1), 85–93 (2016)

    Article  MathSciNet  Google Scholar 

  15. Bányai, F., Zsila, Á., Király, O., Maraz, A., Elekes, Z., Griffiths, M.D., Demetrovics, Z.: Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS ONE 12(1), e0169839 (2017)

    Article  Google Scholar 

  16. Fardouly, J., Magson, N.R., Johnco, C.J., Oar, E.L., Rapee, R.M.: Parental control of the time preadolescents spend on social media: links with preadolescents’ social media appearance comparisons and mental health. J. Youth Adolesc. 47(7), 1456–1468 (2018). https://doi.org/10.1007/s10964-018-0870-1

    Article  Google Scholar 

  17. Sierra-Correa, P.C., Cantera Kintz, J.R.: Ecosystem-based adaptation for improving coastal planning for sea-level rise: a systematic review for mangrove coasts. Mar. Policy, 51, 385–393 (2015)

    Google Scholar 

  18. Wook, T.S.M., Mohamed, H., Noor, S.F.M., Muda, Z., Zairon, I.Y.: Awareness of digital footprints management in the new media amongst youth. Malays. J. Commun. 35(3), 407–421 (2019)

    Google Scholar 

  19. Phillips, J.G., Sargeant, J., Ogeil, R.P., Chow, Y.-W., Blaszczynski, A.: Self-reported gambling problems and digital traces. Cyberpsychol. Behav. Soc. Network. 17(12), 742–748 (2014)

    Article  Google Scholar 

  20. Rafaeli, A., Ashtar, S., Altman, D. Digital traces: new data, resources, and tools for psychological-science research. Curr. Dir. Psychol. Sci. 096372141986141 (2019)

    Google Scholar 

  21. Vianna, D., Kalokyri, V., Borgida, A., Marian, A., Nguyen, T.: Searching heterogeneous personal digital traces. Proc. Assoc. Inf. Sci. Technol. 56(1), 276–285 (2019)

    Article  Google Scholar 

  22. Zhang, H.Z., Xie, C., Nourian, S.: Are their designs iterative or fixated? Investigating design patterns from student digital footprints in computer-aided design software. Int. J. Technol. Des. Educ. 28(3), 819–841 (2017). https://doi.org/10.1007/s10798-017-9408-1

    Article  Google Scholar 

  23. Ndumbaro, F.: Understanding user-system interactions: an analysis of OPAC users’ digital footprints. Inf. Dev. 34(3), 297–308 (2017)

    Article  Google Scholar 

  24. Songsom, N., Nilsook, P., Wannapiroon, P.: The synthesis of the student relationship management system using the Internet of Things to collect the digital footprint for higher education institutions. Int. J. Online Biomed. Eng. (iJOE) 15, 99 (2019)

    Google Scholar 

  25. Harjumaa, M., Saraniemi, S., Pekkarinen, S., Lappi, M., Similä, H., Isomursu, M.: Feasibility of digital footprint data for health analytics and services: an explorative pilot study. BMC Med. Inform. Decis. Making 16(1) (2016)

    Google Scholar 

  26. Kim, C., Gupta, R., Shah, A., Madill, E., Prabhu, A.V., Agarwal, N.: Digital footprint of neurological surgeons. World Neurosurg. 113, e172–e178 (2018)

    Article  Google Scholar 

  27. Buchanan, R., Southgate, E., Smith, S.P., Murray, T., Noble, B.: Post no photos, leave no trace: children’s digital footprint management strategies. E-Learn. Digit. Media 14(5), 275–290 (2017)

    Article  Google Scholar 

  28. Lee, M.-H., Cha, S., Nam, T.-J.: Impact of digital traces on the appreciation of movie contents. Digit. Creativity 26(3–4), 287–303 (2015)

    Article  Google Scholar 

  29. Liu, L., Andris, C., Ratti, C.: Uncovering cabdrivers’ behavior patterns from their digital traces. Comput. Environ. Urban Syst. 34(6), 541–548 (2010)

    Article  Google Scholar 

  30. Chen, C., Zhang, D., Guo, B., Ma, X., Pan, G., Wu, Z.: TripPlanner: personalized trip planning leveraging heterogeneous crowdsourced digital footprints. IEEE Trans. Intell. Transp. Syst. 16(3), 1259–1273 (2015)

    Article  Google Scholar 

  31. Yi, J., Du, Y., Liang, F., Tu, W., Qi, W., Ge, Y.: Mapping human’s digital footprints on the Tibetan Plateau from multi-source geospatial big data. Sci. Total Environ. 711, 134540 (2019)

    Article  Google Scholar 

  32. Traunmueller, M.W., Johnson, N., Malik, A., Kontokosta, C.E.: Digital footprints: using WiFi probe and locational data to analyze human mobility trajectories in cities. Computers, Environment and Urban Systems (2018)

    Google Scholar 

  33. Chen, C., et al.: MA-SSR: a memetic algorithm for skyline scenic routes planning leveraging heterogeneous user-generated digital footprints. IEEE Trans. Veh. Technol. 66(7), 5723–5736 (2017)

    Article  Google Scholar 

  34. Zinman, O., Lerner, B.: Utilizing digital traces of mobile phones for understanding social dynamics in urban areas. Pers. Ubiquit. Comput. 24(4), 535–549 (2019). https://doi.org/10.1007/s00779-019-01318-w

    Article  Google Scholar 

  35. Lin, Y.H., Wong, B.Y., Pan, Y.C., Chiu, Y.C., Lee, Y.H.: Validation of the mobile app-recorded circadian rhythm by a digital footprint. JMIR Mhealth Uhealth. 7(5), e13421 (2019)

    Article  Google Scholar 

  36. Garcia, D., Tessone, C.J., Mavrodiev, P., Perony, N.: The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy. J. R. Soc. Interface 11(99), 20140623 (2014)

    Article  Google Scholar 

  37. Garcia, D., Rimé, B.: Collective emotions and social resilience in the digital traces after a terrorist attack. Psychol. Sci. 30, 617–628 (2019)

    Article  Google Scholar 

  38. Obschonka, M., Fisch, C., Boyd, R.: Using digital footprints in entrepreneurship research: a twitter-based personality analysis of superstar entrepreneurs and managers. J. Bus. Ventur. Insights 8, 13–23 (2017)

    Article  Google Scholar 

  39. Liu, X., Huang, Q., Gao, S.: Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN. Int. J. Geogr. Inf. Sci. 33, 1196–1223 (2019)

    Article  Google Scholar 

  40. Chen, B., Seo, D.-C., Lin, H.-C., Crandall, D.: Framework for estimating sleep timing from digital footprints. BMJ Innov. 4(4), 172–177 (2018)

    Article  Google Scholar 

  41. Salas-Olmedo, M.H., Moya-Gómez, B., García-Palomares, J.C., Gutiérrez, J.: Tourists’ digital footprint in cities: comparing Big Data sources. Tour. Manag. 66, 13–25 (2018)

    Article  Google Scholar 

  42. Yang, D., Zhang, D., Yu, Z., Yu, Z., Zeghlache, D.: SESAME: mining user digital footprints for finegrained preference-aware social media search. ACM Trans. Internet Technol. 14(4), 1–24 (2014)

    Google Scholar 

  43. Preis, T., Moat, H.S., Bishop, S.R., Treleaven, P., Stanley, H.E.: Quantifying the digital traces of Hurricane Sandy on Flickr. Sci. Rep. 3(1), 1–3 (2013)

    Google Scholar 

  44. Luo, J., Pan, X., Zhu, X.: Identifying digital traces for business marketing through topic probabilistic model. Technol. Anal. Strateg. Manage. 27(10), 1176–1192 (2015)

    Article  Google Scholar 

  45. Arya, V., Sethi, D., Paul, J.: Does digital footprint act as a digital asset? – Enhancing brand experience through remarketing. Int. J. Inf. Manage. 4, 142–156 (2019)

    Article  Google Scholar 

  46. Bach, R.L., et al.: Predicting voting behavior using digital trace data. Soc. Sci. Comput. Rev. 1–22 (2019)

    Google Scholar 

  47. Marda, V.: Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making. Philos. Trans. Roy. Soc. A Math. Phys. Eng. Sci. 376(2133), 20180087 (2018)

    Article  Google Scholar 

  48. Favaretto, M., De Clercq, E., Elger, B.S.: Big Data and discrimination: perils, promises and solutions. A systematic review. J. Big Data 6, 12 (2019)

    Article  Google Scholar 

  49. Veale, M., Kleek, M.V., Binns, R.: Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Paper No 440 (2018)

    Google Scholar 

  50. Kroll, J.A.: The fallacy of inscrutability. Philos. Trans. Roy. Soc. A Math. Phys. Eng. Sci. 376(2133), 1–14 (2018). https://doi.org/10.1098/rsta.2018.0084

    Article  Google Scholar 

  51. Kesan, J.P., Hayes, C.M.: Liability for data injuries. Univ. Ill. Law Rev. 2019(1), 295–363 (2018)

    Google Scholar 

  52. Helbing, D.: Societal, economic, ethical and legal challenges of the digital revolution: from big data to deep learning, artificial intelligence, and manipulative technologies. In: Helbing, D. (ed.) Towards Digital Enlightenment. Essays on the Dark and Light Sides of the Digital Revolution, pp. 47–72. Springer, Cham (2019)

    Google Scholar 

  53. Nawi, A.: Early exploration towards issues and impact the use of artificial intelligence technology towards human beings. Asian J. Civiliz. Stud. 1(4), 24–33 (2019)

    Google Scholar 

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Acknowledgement

This study was funded by Ministry of Higher Education Malaysia (MOHE) with a grant from Fundamental Research Grant Scheme for Research Acculturation of Early Career Researchers (FRGS-RACER) SO Code 14424 and FRGS-2019 SO Code 14396. Researchers would like to express special thanks to the Research & Innovation Management Centre Universiti Utara Malaysia (RIMC UUM) for the support and assistance provided throughout this research. Finally, we thank the three anonymous reviewers for their helpful comments.

Author information

Authors and Affiliations

  1. School of Education, Universiti Utara Malaysia, 06010, Kedah, Malaysia

    Aliff Nawi

  2. School of Applied Psychology, Social Work and Policy, Universiti Utara Malaysia, 06010, Kedah, Malaysia

    Zalmizy Hussin & Nurfatin Syahirah Norsaidi

  3. School of Business and Social Sciences, Albukhary International University, 05200, Kedah, Malaysia

    Chua Chy Ren

  4. School of Computing, Universiti Utara Malaysia, 06010, Kedah, Malaysia

    Muhammad Syafiq Mohd Pozi

Authors
  1. Aliff Nawi
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  2. Zalmizy Hussin
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  3. Chua Chy Ren
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  4. Nurfatin Syahirah Norsaidi
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  5. Muhammad Syafiq Mohd Pozi
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Corresponding author

Correspondence to Aliff Nawi .

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Editors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Emi Ishita

  2. National University of Singapore, Singapore, Singapore

    Natalie Lee San Pang

  3. Wuhan University, Wuhan, China

    Lihong Zhou

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Nawi, A., Hussin, Z., Ren, C.C., Norsaidi, N.S., Mohd Pozi, M.S. (2020). Identifying the Types of Digital Footprint Data Used to Predict Psychographic and Human Behaviour. In: Ishita, E., Pang, N.L.S., Zhou, L. (eds) Digital Libraries at Times of Massive Societal Transition. ICADL 2020. Lecture Notes in Computer Science(), vol 12504. Springer, Cham. https://doi.org/10.1007/978-3-030-64452-9_26

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  • DOI: https://doi.org/10.1007/978-3-030-64452-9_26

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