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Context detection and effects on behavior


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Context detection and effects on behavior

  1. 1. Context detection and effectson behaviorElisa workshop on “Lifestyle Sensing and BehavioralAnalytics”, June 29th, 2012Dr. Timo Smura, Dep. of Communications and Networking(presented work by T. Soikkeli, J. Karikoski, H.-H. Jo, M. Karsai, et al.)
  2. 2. Outline• Behavioral data collection in Aalto / Comnet – Multi-point measurements – Examples of data sets – Holistic view of service usage• Ongoing work related to contexts and behavior – Handset based measurements – Location detection – Context detection algorithms – Context dependence of application and service usage
  3. 3. Behavioral data collection
  4. 4. Multi-point measurements Potential sources of digital behavioral dataOur data sources:• Handset monitoring panels + questionnaires• IP traffic measurements• Web analytics systems• Mobile operator accounting systems
  5. 5. Holistic view of service usageMeasurement points vs. service components Modified from: Smura, Kivi, Töyli 2009
  6. 6. Context detection andeffects on behavior
  7. 7. Handset-based measurementsResearch process and data• Based on a software client installed to a panel of smartphones• Collects rich data about handset usage: – What: Application, bearer – Where: Base station cell IDs (hashed), WLAN SSID – When: Time stamps – How much: Time stamps, amount of generated traffic• Gives a detailed view of the usage patterns and behavior of panelists – All applications, also offline and WLAN usage – Location / context detection Source: Karikoski 2012
  8. 8. Handset-based measurementsCurrent focus areas Shares of time and1. Multi-channel communications usage per context services 17 24 – Diversification of communications 29 7 channels (phone calls, SMS, 8 8 email, social media services) 9 12 Elsewhere – Effect of relationship type on 12 Other meaningful channel selection Office Home – Mobile social phonebooks 66 Abroad 532. Location and context detection 47 – Context detection algorithms – Human behavior and time use in 2 3 3 different locations and contexts Share of Share of Share of total time sessions interaction – Effects on usage: e.g., sessions, spent (%) (%) time (%) applications / services Sources: Karikoski & Soikkeli
  9. 9. Location detection based on cell ID Source: Jo et al. 2012
  10. 10. Context detection algorithms Simplified version, not utilizing WLAN SSID dataA) Temporal boundaries for user’s trajectory in cells: E) Criteria for assigning other contexts:B) Duration, i.e., time spent by user in cell c:C) ”Abroad” context determined by Mobile NetworkCode (MNC)D) For the cells in Finland, more detailed durations: Sources: Soikkeli 2011, Jo et al. 2012
  11. 11. Application usage by contextExemplary data from a single user during two days Source: Jo et al. 2012
  12. 12. Context dependence of service usageFractions and intensities of service usage by context Source: Jo et al. 2012
  13. 13. Conclusions (1/2)• Aalto / Comnet collects rich data on mobile usage – Continues a series of measurements since 2005 – Holistic view of mobile devices and services in Finland• Each measurement methods has its pros and cons – Level of: Granularity, Coverage, Representativity – In terms of: Devices, Applications, Networks, Content• Actors have different views to mobile usage and users – E.g., Device vendors vs. Operators vs. Content providers – Increasing value of user data induces competition • May lead to, e.g., traffic encryption, routing via own gateways
  14. 14. Conclusions (2/2)• Data collected by current smartphones can be used to infer the context of people – Then use it as a variable to explain behavior• By far, research has focused on developing and testing the technical algorithms for detecting the contexts – Demonstration of value with descriptive analysis of usage data• Examples of statistical analyses on the effect of context on behavior are still rare – Typically based on survey-based studies and self-reported context and usage information – Ongoing / future work: combine existing theories and hypothesis- based statistical methods to the data collected in smartphone monitoring panels
  15. 15. References• Soikkeli, T. (2011). The effect of context on smartphone usage sessions. M.Sc. Thesis.• Karikoski, J., & Soikkeli, T. (In Press) Contextual usage patterns in smartphone communication services, Personal and Ubiquitous Computing.• H.-H. Jo, M. Karsai, J. Karikoski, and K. Kaski, Spatiotemporal correlations of handset-based service usages, arXiv:1204.2169 (2012)• Smura, T., Kivi, A., & Töyli, J. (2009). A Framework for Analysing the Usage of Mobile Services, info, vol. 11, no. 4, pp. 53-67.
  16. 16. Useful contacts in Aalto / Comnet• Project management: – Prof. Heikki Hämmäinen, Timo Smura• Researchers: – Handset-based measurements • Juuso Karikoski, Tapio Soikkeli – Mobile network traffic measurements • Antti Riikonen – Handset features and evolution • Timo Smura, Antti Riikonen – Web analytics –based research • Timo Smura – Bayesian Belief Networks –based analytics • Pekka Kekolahti••