Understanding and Analyzing Social Network Structure Among University Students

Mapping Intimacies ◽

10.21203/rs.3.rs-705159/v1 ◽

2021 ◽

Author(s):

Md. Sharif Hossen ◽

Md. Aminul Islam

Keyword(s):

University Students ◽

Mobile Phone ◽

Strong Relationship ◽

Mobile Phone Data ◽

Human Beings ◽

Human Communication ◽

Multiple Sensors ◽

Social Network Structure ◽

Rich Data ◽

Public Health Epidemiology

Abstract Mobile phone arguably is one of the most reached and used technology in human history. Technology has become ubiquitous in the life of human beings. Equipped with multiple sensors and devices, smartphones can record each and every action, psychological and environmental states of users, making it a goldmine of rich data about and insight into the dynamics of human communication, human behavior, relationships, and social interaction. As a source of data for empirical research, this device has gotten much attention from scholars in various disciplines like sociology, social psychology, urban studies, communication and media studies, public health, epidemiology, and computer science. This research tries to understand the structure of social networks of university students by investigating their communication patterns using self-reported mobile phone data. We collected behavioral data for one month using a Call Log Analytics mobile phone app. The data contained information about respondents’ contacts, date and time of call, duration of the call, call type (e.g., incoming, outgoing, missed), and frequency of the call. We used UCINET to analyze the data. In this investigation, we can find those students who are connected to most of the classmates and maintain a strong relationship and perform a task successfully using the values of eigenvector, closeness, and betweenness centrality, respectively. Moreover, this study also helps us to find out the pattern of the students using contact duration, incoming and outgoing calls.

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Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1423542112 ◽

2015 ◽

Vol 112 (35) ◽

pp. 11114-11119 ◽

Cited By ~ 61

Author(s):

Amy Wesolowski ◽

C. J. E. Metcalf ◽

Nathan Eagle ◽

Janeth Kombich ◽

Bryan T. Grenfell ◽

...

Keyword(s):

Mobile Phone ◽

Low Income ◽

Spatial Data ◽

Disease Transmission ◽

Travel Behavior ◽

Human Mobility ◽

Mobile Phone Data ◽

Cross Sectional ◽

Unique Source ◽

Rich Data

Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or cross-sectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.

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Mobile Phone Data for Inferring Social Network Structure

Social Computing, Behavioral Modeling, and Prediction ◽

10.1007/978-0-387-77672-9_10 ◽

2008 ◽

pp. 79-88 ◽

Cited By ~ 27

Author(s):

Nathan Eagle ◽

Alex (Sandy) Pentland ◽

David Lazer

Keyword(s):

Social Network ◽

Mobile Phone ◽

Network Structure ◽

Mobile Phone Data ◽

Social Network Structure

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Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment

The World Bank Economic Review ◽

10.1093/wber/lhz006 ◽

2019 ◽

Vol 34 (3) ◽

pp. 618-634 ◽

Cited By ~ 2

Author(s):

Daniel Björkegren ◽

Darrell Grissen

Keyword(s):

Developing Countries ◽

Mobile Phone ◽

Mobile Phones ◽

Mobile Phone Data ◽

South American ◽

Time Period ◽

Behavioral Signatures ◽

Mobile Phone Usage ◽

Rich Data ◽

To Receive

Abstract Many households in developing countries lack formal financial histories, making it difficult for firms to extend credit, and for potential borrowers to receive it. However, many of these households have mobile phones, which generate rich data about behavior. This article shows that behavioral signatures in mobile phone data predict default, using call records matched to repayment outcomes for credit extended by a South American telecom. On a sample of individuals with (thin) financial histories, this article's method actually outperforms models using credit bureau information, both within-time and when tested on a different time period. But the method also attains similar performance on those without financial histories, who cannot be scored using traditional methods. Individuals in the highest quintile of risk by the measure used in this article are 2.8 times more likely to default than those in the lowest quintile. The method forms the basis for new forms of credit that reach the unbanked.

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Modelling urban vibrancy with mobile phone and OpenStreetMap data

PLoS ONE ◽

10.1371/journal.pone.0252015 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0252015

Author(s):

Federico Botta ◽

Mario Gutiérrez-Roig

Keyword(s):

Quantitative Analysis ◽

Mobile Phone ◽

Social Interactions ◽

Age Groups ◽

Spatial Models ◽

Strong Relationship ◽

Urban Environments ◽

Mobile Phone Data ◽

Third Places ◽

Urban Places

The concept of urban vibrancy has become increasingly important in the study of cities. A vibrant urban environment is an area of a city with high levels of human activity and interactions. Traditionally, studying our cities and what makes them vibrant has been very difficult, due to challenges in data collection on urban environments and people’s location and interactions. Here, we rely on novel sources of data to investigate how different features of our cities may relate to urban vibrancy. In particular, we explore whether there are any differences in which urban features make an environment vibrant for different age groups. We perform this quantitative analysis by extracting urban features from OpenStreetMap and the Italian census, and using them in spatial models to describe urban vibrancy. Our analysis shows a strong relationship between urban features and urban vibrancy, and particularly highlights the importance of third places, which are urban places offering opportunities for social interactions. Our findings provide evidence that a combination of mobile phone data with crowdsourced urban features can be used to better understand urban vibrancy.

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Estimating tie strength in social networks using temporal communication data

EPJ Data Science ◽

10.1140/epjds/s13688-020-00256-5 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Javier Ureña-Carrion ◽

Jari Saramäki ◽

Mikko Kivelä

Keyword(s):

Time Series ◽

Social Network ◽

Contact Time ◽

Temporal Stability ◽

Strong Relationship ◽

Tie Strength ◽

Topological Features ◽

Social Network Structure ◽

Temporal Features ◽

Rich Data

AbstractEven though the concept of tie strength is central in social network analysis, it is difficult to quantify how strong social ties are. One typical way of estimating tie strength in data-driven studies has been to simply count the total number or duration of contacts between two people. This, however, disregards many features that can be extracted from the rich data sets used for social network reconstruction. Here, we focus on contact data with temporal information. We systematically study how features of the contact time series are related to topological features usually associated with tie strength. We focus on a large mobile-phone dataset and measure a number of properties of the contact time series for each tie and use these to predict the so-called neighbourhood overlap, a feature related to strong ties in the sociological literature. We observe a strong relationship between temporal features and the neighbourhood overlap, with many features outperforming simple contact counts. Features that stand out include the number of days with calls, number of bursty cascades, typical times of contacts, and temporal stability. These are also seen to correlate with the overlap in diverse smaller communication datasets studied for reference. Taken together, our results suggest that such temporal features could be useful for inferring social network structure from communication data.

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The Potential GTX Travel Demand using Mobile Phone Data

Journal of The Korean Society For Urban Railway ◽

10.24284/jkosur.2019.3.7.1.077 ◽

2019 ◽

Vol 7 (1) ◽

pp. 77-84

Author(s):

Jin Ki Eom ◽

Kwang-Sub Lee ◽

Ho-Chan Kwak ◽

Ji Young Song ◽

Myeong-Eon Seong

Keyword(s):

Mobile Phone ◽

Travel Demand ◽

Mobile Phone Data

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Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data

Scientific Reports ◽

10.1038/s41598-021-81308-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hamid Khataee ◽

Istvan Scheuring ◽

Andras Czirok ◽

Zoltan Neufeld

Keyword(s):

Mobile Phone ◽

Basic Reproductive Number ◽

Mobile Phone Data ◽

Reproductive Number ◽

Expansion Phase ◽

Social Distancing ◽

Exponential Expansion ◽

Total Death ◽

Mobility Reduction ◽

Epidemiological Parameters

AbstractA better understanding of how the COVID-19 pandemic responds to social distancing efforts is required for the control of future outbreaks and to calibrate partial lock-downs. We present quantitative relationships between key parameters characterizing the COVID-19 epidemiology and social distancing efforts of nine selected European countries. Epidemiological parameters were extracted from the number of daily deaths data, while mitigation efforts are estimated from mobile phone tracking data. The decrease of the basic reproductive number ($$R_0$$ R 0 ) as well as the duration of the initial exponential expansion phase of the epidemic strongly correlates with the magnitude of mobility reduction. Utilizing these relationships we decipher the relative impact of the timing and the extent of social distancing on the total death burden of the pandemic.

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Did the NSA and GCHQ Diminish Our Privacy? What the Control Account Should Say

Moral Philosophy and Politics ◽

10.1515/mopp-2019-0063 ◽

2020 ◽

Vol 7 (1) ◽

pp. 29-48 ◽

Cited By ~ 1

Author(s):

Leonhard Menges

Keyword(s):

Mobile Phone ◽

Personal Information ◽

Real Life ◽

Mobile Phone Data ◽

Standard Account ◽

Do So

AbstractA standard account of privacy says that it is essentially a kind of control over personal information. Many privacy scholars have argued against this claim by relying on so-called threatened loss cases. In these cases, personal information about an agent is easily available to another person, but not accessed. Critics contend that control accounts have the implausible implication that the privacy of the relevant agent is diminished in threatened loss cases. Recently, threatened loss cases have become important because Edward Snowden’s revelation of how the NSA and GCHQ collected Internet and mobile phone data presents us with a gigantic, real-life threatened loss case. In this paper, I will defend the control account of privacy against the argument that is based on threatened loss cases. I will do so by developing a new version of the control account that implies that the agents’ privacy is not diminished in threatened loss cases.

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