1. RESEARCH POSTER PRESENTATION DESIGN © 2015
www.PosterPresentations.com
How much can one know about your personality just by looking at
the way you use your phone? Smartphones are increasingly seen as
large-scale sensors of human activity recording data related both to
physical and social pace of people’s lives. This brings an enormous
potential to the use of phones as part of large-scale behavioral
studies. We describe a novel approach to model an individual’s
“risk taking propensity” based on his/her phoneotype i.e. a
composite of an individual’s behavioral traits as observable via a
mobile phone usage.
Risk Propensity as a field of study
Risk propensity is the stable tendency to choose options with a
lower probability of success, but greater rewards. The concept of
risk propensity has important implications for the theoretical
modeling of risk behavior and for practical insights into the
motives underlying individual level choices about engaging in
risky behavior. In organizational terms, a better understanding of
risk behavior could contribute significantly to risk management
programs.
ABSTRACT
INTRODUCTION
Data: The data used in this paper was obtained as part of the
Rutgers Well-being study. The empirical sections of this work are
based on a dataset collected in a major US research university.
Each participant was equipped with a Android smartphone running
the open sensing framework . Out of the 54 participants in the
resulting dataset, 35 were male, and 19 were female. The most
common age group was 18-21 years, most common marital status
was single, the most common education level was “some college”,
and the median annual family income was in the range US
$50,000- $74,999.
The framework is designed to collect a wide range of behavioral
data from the user’s phone such as phone calls, text messages sent
and received. The study also involved answering surveys related to
health, well being and demographic data.
METHODOLOGY
Classification: To build and test a classification approach for
cooperation propensities, we divided the risk scores based on the
median value of the sample. To build the predictive models we first
undertook feature selection based on robust subset selection. This
method evaluates the worth of a subset of attributes by considering
the individual predictive ability of each feature along with the
degree of redundancy between them.
These selected features were passed to different machine learning
algorithms for classification and decided to adopt a 10-fold cross
validation method to a tradeoff. During the process of model
building, we explore a few visualization and metrics tools that help
us on data preparation, model building and tuning and performance
characterizing.
MODELING RESULTS
CONCLUSION
Mobile cellular subscriptions have hit 6 billion throughout the
world and carriers have increasingly made available phone logs to
researchers as well as to their commercial partners . If predicted
correctly, mobile phones datasets could thus provide a valuable
unobtrusive and cost-effective alternative to survey based measures
of personality. For example, marketing and phone companies
might seek to access dispositional information about their
customers to design customized offers and advertisements
LIMITATIONS
Although these approaches are interesting, they either require to
have access to extensive information about people’s entire social
network or people to install a specific tracking application on their
phone. These constraints greatly undermine the use of such
classification methods for large-scale investigations.
Also multiple comparisons were undertaken in the correlation
analysis. While such multiple comparisons are often “corrected”
using Bonferroni or Bonferroni-Holm correction to maintain the
confidence in the associations found, we do not do so in this work.
This is because the analysis undertaken here is posthoc and
intended to help interpret the observed prediction results rather
than being prescriptive in its own right.
REFERENCES
1. Cooperative Phoneotypes: Exploring Phone-based Behavioral Markers of
Cooperation- Prof. Vivek Singh, Behaviroal Informatics Group, Rutges
University . http://ubicomp.org/ubicomp2016/program/program.ph
2. Predicting Personality Using Novel Mobile Phone-Based Metrics Yves-
Alexandre de Montjoye1,, Jordi Quoidbach2,∗, Florent Robic3,∗, and Alex
(Sandy) Pentland1
3. MIT Human Dynamics Lab, Reality Commons,
http://realitycommons.media.mit.edu/
4. Cho, E., Myers, S. A., & Leskovec, J. (2011, August). Friendship and
mobility: user movement in location- based social networks. In
Proceedings of the 17th ACM SIGKDD international conference on
Knowledge discovery and data mining (pp. 1082-1090). ACM.
5. 5Risk Propensity and Personality, Nigel Nicholson, Mark Fenton-
O’Creevy , Emma Soane , Paul Willman
The goal of this research is to show that users’ personalities can be
reliably inferred from basic information accessible from all mobile
phones and to all service providers. In other words, we address two
fundamental questions: Which are the main emergent patterns of
usage? To define a machine learning model to automatically infer
an individual’s propensity to take risks.
Specifically, we introduce following sets of psychology informed
metrics–Social Activity Level, Engagement Ties Ratio, Mobility,
Diurnal Ratio and Diversity and In-Out Ratio.
Based on a 10-week field study involving 54 participants, we
report that: (1) multiple phone-based signals were significantly
associated with participant’s risk taking attitudes; and (2)
combining phone-based signals yielded a classification predictive
model with Accuracy of 0.72 that performed significantly better
than a comparable demography-based model at predicting
individual cooperation propensities.
Rushil Goyal (under Prof. Vivek Singh, Behavioral Informatics Research Group
Exploring Phone-Based Behavioral Markers of Risk Taking Attitude
Regression- We undertook regression analysis with the risk score
as a continuous dependent variable and the aforementioned socio-
mobile variables as the input variables. Specifically, we tried
different models. The demographic variables were also included
onto the selected subset features to test the hypothesis about they
improving the model. Lasso Regression and Wrapper Method were
implemented to select optimal feature subset.
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FEATURE INDEX
Robust Feature Selection- Importance Plot
Average all_methods Correlation Lasso Random Forest Recursive Feature Elimination Ridge Regression
COMMERICIAL IMPLICATION
Fig 6. Feature Importance - Classification
Fig 4. Data Summary
Fig 2. Broad Category of Features.
Fig 5 Exploratory Analysis
Fig 3. Meta-Data Summary Snapshot
Fig 7. Decision Tree Model Fig. 8 Performance Evaluation
Following information were collected from the participants for
the ten week period:
● Communication log data: This included date and time of
communication event, the type of communication (text/call),
incoming or outgoing, duration
● Physical location: This included the location of the device
using GPS and approximate location area based on the cell
tower
Although more research is needed to validate our model and the
robustness of our indicators for use on a large-scale and more
diverse population, we believe that our findings open the door to
exciting avenues of research in social sciences. Our personality
indicators and the ability to predict personality using readily
available mobile phone data may enable cost-effective,
questionnaire-free investigation of personality-related questions at
the scale of entire countries.
For example, such results could be used by an individual to
identify the most cooperative peers for engaging in mobile
commerce or by community designers to recruit suitable early
volunteers. For future work, it is possible to further improve the
model performance by fine tuning the model parameters or deeper
understanding features.
Fig 1. Rutgers Well Being App.
Rsquared p_val index
0.206654411 0.006140066 15+19
0.274421906 0.002976239 13+15+19
0.3244694 0.002067336 6+9+15+19
0.381895644 0.001047517 6+9+15+17+19
0.416681726 0.000952634 9+14+15+16+17+19
0.441002964 0.001120881 5+9+14+15+16+17+19
0.464268863 0.001301668 2+5+9+14+15+16+17+19
0.471091875 0.00234983 2+5+9+11+14+15+16+17+19
0.477614896 0.004063538 2+5+6+7+9+14+15+16+17+19
0.494187662 0.005161415 2+4+6+7+8+9+14+15+16+17+19
0.511620546 0.006265241 2+3+4+6+7+8+9+14+15+16+17+19
0.53254726 0.006812313 2+3+4+6+7+8+9+13+14+15+16+17+19
0.543794763 0.009365572 2+3+4+5+6+7+8+9+13+14+15+16+17+19
0.552747265 0.013342508 2+3+4+5+6+7+8+9+13+14+15+16+17+18+19
0.554839459 0.021781009 2+3+4+5+6+7+8+9+13+14+15+16+17+18+19+20
0.555427596 0.035327953 2+3+4+5+6+7+8+9+10+13+14+15+16+17+18+19+20
Fig. 11 Feature Importance- RegressionFig. 10 Lasso Regression
Fig. 12 Feature Selection: Brutal Force Wrapper Analysis
Fig. 9 Fine Tuning Random Forest