19 research outputs found
Modeling the Temporal Nature of Human Behavior for Demographics Prediction
Mobile phone metadata is increasingly used for humanitarian purposes in
developing countries as traditional data is scarce. Basic demographic
information is however often absent from mobile phone datasets, limiting the
operational impact of the datasets. For these reasons, there has been a growing
interest in predicting demographic information from mobile phone metadata.
Previous work focused on creating increasingly advanced features to be modeled
with standard machine learning algorithms. We here instead model the raw mobile
phone metadata directly using deep learning, exploiting the temporal nature of
the patterns in the data. From high-level assumptions we design a data
representation and convolutional network architecture for modeling patterns
within a week. We then examine three strategies for aggregating patterns across
weeks and show that our method reaches state-of-the-art accuracy on both age
and gender prediction using only the temporal modality in mobile metadata. We
finally validate our method on low activity users and evaluate the modeling
assumptions.Comment: Accepted at ECML 2017. A previous version of this paper was titled
'Using Deep Learning to Predict Demographics from Mobile Phone Metadata' and
was accepted at the ICLR 2016 worksho
Flooding through the lens of mobile phone activity
Natural disasters affect hundreds of millions of people worldwide every year.
Emergency response efforts depend upon the availability of timely information,
such as information concerning the movements of affected populations. The
analysis of aggregated and anonymized Call Detail Records (CDR) captured from
the mobile phone infrastructure provides new possibilities to characterize
human behavior during critical events. In this work, we investigate the
viability of using CDR data combined with other sources of information to
characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map
has been reconstructed using Landsat-7 images to identify the floods. Within
this frame, the underlying communication activity signals in the CDR data have
been analyzed and compared against rainfall levels extracted from data of the
NASA-TRMM project. The variations in the number of active phones connected to
each cell tower reveal abnormal activity patterns in the most affected
locations during and after the floods that could be used as signatures of the
floods - both in terms of infrastructure impact assessment and population
information awareness. The representativeness of the analysis has been assessed
using census data and civil protection records. While a more extensive
validation is required, these early results suggest high potential in using
cell tower activity information to improve early warning and emergency
management mechanisms.Comment: Submitted to IEEE Global Humanitarian Technologies Conference (GHTC)
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Predicting customer's gender and age depending on mobile phone data
In the age of data driven solution, the customer demographic attributes, such
as gender and age, play a core role that may enable companies to enhance the
offers of their services and target the right customer in the right time and
place. In the marketing campaign, the companies want to target the real user of
the GSM (global system for mobile communications), not the line owner. Where
sometimes they may not be the same. This work proposes a method that predicts
users' gender and age based on their behavior, services and contract
information. We used call detail records (CDRs), customer relationship
management (CRM) and billing information as a data source to analyze telecom
customer behavior, and applied different types of machine learning algorithms
to provide marketing campaigns with more accurate information about customer
demographic attributes. This model is built using reliable data set of 18,000
users provided by SyriaTel Telecom Company, for training and testing. The model
applied by using big data technology and achieved 85.6% accuracy in terms of
user gender prediction and 65.5% of user age prediction. The main contribution
of this work is the improvement in the accuracy in terms of user gender
prediction and user age prediction based on mobile phone data and end-to-end
solution that approaches customer data from multiple aspects in the telecom
domain
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
With the rapid expansion of mobile phone networks in developing countries,
large-scale graph machine learning has gained sudden relevance in the study of
global poverty. Recent applications range from humanitarian response and
poverty estimation to urban planning and epidemic containment. Yet the vast
majority of computational tools and algorithms used in these applications do
not account for the multi-view nature of social networks: people are related in
myriad ways, but most graph learning models treat relations as binary. In this
paper, we develop a graph-based convolutional network for learning on
multi-view networks. We show that this method outperforms state-of-the-art
semi-supervised learning algorithms on three different prediction tasks using
mobile phone datasets from three different developing countries. We also show
that, while designed specifically for use in poverty research, the algorithm
also outperforms existing benchmarks on a broader set of learning tasks on
multi-view networks, including node labelling in citation networks
The Role of Gender in Social Network Organization
The digital traces we leave behind when engaging with the modern world offer
an interesting lens through which we study behavioral patterns as expression of
gender. Although gender differentiation has been observed in a number of
settings, the majority of studies focus on a single data stream in isolation.
Here we use a dataset of high resolution data collected using mobile phones, as
well as detailed questionnaires, to study gender differences in a large cohort.
We consider mobility behavior and individual personality traits among a group
of more than university students. We also investigate interactions among
them expressed via person-to-person contacts, interactions on online social
networks, and telecommunication. Thus, we are able to study the differences
between male and female behavior captured through a multitude of channels for a
single cohort. We find that while the two genders are similar in a number of
aspects, there are robust deviations that include multiple facets of social
interactions, suggesting the existence of inherent behavioral differences.
Finally, we quantify how aspects of an individual's characteristics and social
behavior reveals their gender by posing it as a classification problem. We ask:
How well can we distinguish between male and female study participants based on
behavior alone? Which behavioral features are most predictive
Improving Individual Predictions using Social Networks Assortativity
Social networks are known to be assortative with
respect to many attributes, such as age, weight, wealth, level
of education, ethnicity and gender. This can be explained by
influences and homophilies. Independently of its origin, this
assortativity gives us information about each node given its
neighbors. Assortativity can thus be used to improve individual
predictions in a broad range of situations, when data are missing
or inaccurate. This paper presents a general framework based on
probabilistic graphical models to exploit social network structures
for improving individual predictions of node attributes. Using
this framework, we quantify the assortativity range leading to an
accuracy gain in several situations. We finally show how specific
characteristics of the network can improve performances further.
For instance, the gender assortativity in real-world mobile phone
data changes significantly according to some communication
attributes. In this case, individual predictions with 75% accuracy
are improved by up to 3%
Resembling Population Density Distribution with Massive Mobile Phone Data
As the mobile phone data (CDR data) has gained an increasing interest in research, such as social science, transportation, urban informatics, and big data, this study aims at examining the representativeness of the CDR data in terms of resemblance of the actual population density distribution from three perspectives; operator’s market share, urban-rural user population ratio, and user gender ratio. The results reveal that the representativeness of the data does not scale at the same rate with the operator’s market share, the urban-rural user population ratio of 80:20 can best represent the population density distribution, and an equal mixture of male and female user population can best resemble the population density distribution. This study is the first investigation into the representativeness of the CDR data. The findings provide useful information, which can serve an insightful guideline when dealing with the CDR data
Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.This work is written on behalf of the Women’s Brain Project (WBP) (www.womensbrainproject.com/), an international organization advocating for women’s brain and mental health through scientific research, debate and public engagement. The authors would like to gratefully acknowledge Maria Teresa Ferretti and Nicoletta Iacobacci (WBP) for the scientific advice and insightful discussions; Roberto Confalonieri (Alpha Health) for reviewing the manuscript; the Bioinfo4Women programme of Barcelona Supercomputing Center (BSC) for the support. This work has been supported by the Spanish Government (SEV 2015–0493) and grant PT17/0009/0001, of the Acción Estratégica en Salud 2013–2016 of the Programa Estatal de Investigación Orientada a los Retos de la Sociedad, funded by the Instituto de Salud Carlos III (ISCIII) and European Regional Development Fund (ERDF). EG has received funding from the Innovative Medicines Initiative 2 (IMI2) Joint Undertaking under grant agreement No 116030 (TransQST), which is supported by the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA).Peer ReviewedPostprint (published version
Gender gaps in urban mobility
Abstract Mobile phone data have been extensively used to study urban mobility. However, studies based on gender-disaggregated large-scale data are still lacking, limiting our understanding of gendered aspects of urban mobility and our ability to design policies for gender equality. Here we study urban mobility from a gendered perspective, combining commercial and open datasets for the city of Santiago, Chile. We analyze call detail records for a large cohort of anonymized mobile phone users and reveal a gender gap in mobility: women visit fewer unique locations than men, and distribute their time less equally among such locations. Mapping this mobility gap over administrative divisions, we observe that a wider gap is associated with lower income and lack of public and private transportation options. Our results uncover a complex interplay between gendered mobility patterns, socio-economic factors and urban affordances, calling for further research and providing insights for policymakers and urban planners