Networks are fundamental building block of communities, businesses, and the broader society and economy. DataLab researchers are developing and applying new network theory and methods to better understand the social, physical, and technical systems that surround us. Current research explores social media, scientific collaboration, economic exchange, and human mobility.
Understanding the causes and effects of internal migration is critical to the effective design and implementation of policies that promote human development. Here, we describe how large sources of geotagged data generated by mobile phones can provide a novel source of data on internal migration.
We examine the relationship between violence and financial decisions in Afghanistan. Using three separate data sources, we find that individuals experiencing violence retain more cash and are less likely to adopt and use mobile money, a new financial technology.
For hundreds of years, scientists have been laying down trails of citations. These trails form a vast network, where papers are nodes and citations are links. This network can tell us a lot about the formation of new ideas, fields, and technology. We can identify salient papers and authors. We can construct maps that help us navigate this ever growing network. And we can better understand how information flows in social systems. These are some of the goals of the Eigenfactor Project (http://www.eigenfactor.org).
This research seeks both to understand the patterns and mechanisms of the diffusion of misinformation on social media and to develop algorithms to automatically detect misinformation as events unfold. During natural disasters and other hazard events, individuals increasingly utilize social media to disseminate, search for and curate event-related information. There is great potential for this information to be used by affected communities and emergency responders to enhance situational awareness and improve decision-making, facilitating response activities and potentially saving lives. Yet several challenges remain; one is the generation and propagation of misinformation. Taking a novel and transformative approach, this project aims to utilize the collective intelligence of the crowd – the crowdwork of some social media users who challenge and correct questionable information – to distinguish misinformation and aid in its detection.
We provide empirical evidence that an early form of "mobile money" is used to share risk. Our analysis uses a unique dataset containing the entire universe of one country's mobile phone communications over a four-year period, and exploits spatio-temporal variation in communication caused by earthquakes and floods. We show that individuals are significantly more likely to send money to people affected by economic shocks, and that gifts are driven more by a desire for reciprocity than purely altruistic motives.
Gender disparities are decreasing overall in academia. However, we find in this data driven study that the story is a bit more complicated. Examining more than 8 million academic papers, we find that, in certain fields, women are underrepresented as signle authored papers and that men are over represented in the first and last author positions. You can explore the data for hundreds of fields of science using the following interactive visualization (http://www.eigenfactor.org/gender/).
Serial transmission - the passing on of information from one source to another - is a phenomenon of central interest in the study of informal communication in emergency settings. Microblogging services such as Twitter make it possible to study serial transmission on a large scale, and to examine the factors that make retransmission of messages more or less likely. Here, we consider factors predicting serial transmission at the interface of formal and informal communication during disaster; specifically, we examine the retransmission by individuals of messages (tweets) issued by formal organizations on Twitter. Our central question is the following: How do message content, message style, and public attention to tweets relate to the behavioral activity of retransmitting (i.e., retweeting) a message in disaster?
We exploit a novel source of data to model the impact of migration and urbanization on segregation in Estonia. Analyzing the complete mobile phone records of hundreds of thousands of Estonians, we observe the ethnicity of each individual on the network (Russian or Estonian), the complete history of locations visited by each individual, and every phone-based interactions taking place over the network. We find that the ethnic composition of an individual's geographic neighborhood heavily influences the structure of the individual's phone-based network. We further find that patterns of segregation are significantly different for migrants than for the at-large population: migrants are more likely to interact with coethnics than non-migrants, but are less sensitive to the ethnic composition of their immediate neighborhood than non-migrants.
When crises occur, including natural disasters, mass casualty events, political and social protests, etc., we observe potentially drastic changes in social behavior. Local citizens, emergency responders and aid organizations flock to the physical location of the event. Global onlookers turn to communication and information exchange platforms to seek and disseminate event-related content. This social convergence behavior, long known to occur in offline settings in the wake of crisis events, is now mirrored – perhaps enhanced – in online settings. This project looks specifically at the mass convergence of public attention during crisis events. Viewed through the framework of social network analysis, mass convergence of attention onto individual actors can be conceptualized in terms of degree dynamics. This project employs a longitudinal study of social network structures in a prominent online social media platform to characterize instances of social convergence behavior and subsequent decay of social ties over time, across different actors types and different event types.
The digital traces we leave on Twitter are fruitful sources of data for social science research. However, users do not directly report key demographic characteristics – such as age, race and gender – that are critical to social scientists. Given this challenge, this project focuses on using systematic and scalable methods to extract demographic information from Twitter users’ profiles and leverage this information to answer sociologically driven questions. One current application of these methods considers whether associative networks within Twitter are as segregated as acquaintanceship networks offline. Acknowledging past work on the role that social structure and agency play in influencing the racial composition of individuals’ networks, we argue that Twitter blurs the roles of these forces as users actively create and are influenced by their own “structure.” This may result in networks that are more or less diverse than what is seen offline. Another current application of these methods addresses the role of race in considering microdynamics between citizens and the police. This project uses unsolicited, user-generated Twitter content to characterize citizens’ attitudes toward law enforcement and examines how these opinions vary along geographic, social (i.e. influence of social contacts) and demographic characteristics of the individuals involved.