Computational Social Science Winter Symposium

Invited Speakers

 

Department of Mathematics, City University London

The Spontaneous Emergence of Social Conventions: An Experimental Study of Cultural Evolution

Abstract

How do shared conventions emerge in complex decentralized social systems?

This question engages fields as diverse as linguistics, sociology, and cognitive science. Previous empirical attempts to solve this puzzle all presuppose that formal or informal institutions, such as incentives for global agreement, coordinated leadership, or aggregated information about the population, are needed to facilitate a solution. Evolutionary theories of social conventions, by contrast, hypothesize that such institutions are not necessary in order for social conventions to form. However, empirical tests of this hypothesis have been hindered by the difficulties of evaluating the real-time creation of new collective behaviors in large decentralized populations. Here, I will present experimental results—replicated at several scales—that demonstrate the spontaneous creation of universally adopted social conventions and show how simple changes in a population’s network structure can direct the dynamics of norm formation, driving human populations with no ambition for large scale coordination to rapidly evolve shared social conventions. I will also show that a simple model describes well the experimental results on different classes of social networks.

Reference:

D. Centola and A. Baronchelli, “The spontaneous emergence of conventions: An experimental study of cultural evolution”, Proc. Natl. Acad. Sci. USA 112, 1989 (2015) (more info here: http://bit.ly/1U3L7YF).

 

School of Informatics and Computing, Indiana University

Modeling critical transitions in mental health from longitudinal social media data

Abstract

Longitudinal studies of self-reported mood indicate the involvement of positive feedback loops and Critical Transitions (CT) in the development of clinical depression. Importantly, these studies show that mental health transitions are reliably preceded by early warning indicators such as increased autocorrelation of mood, and other common antecedents related to the phenomenon of “critical slowing down”. We use a text subjectivity analysis to estimate the evolving and longitudinal psychological mood state of over 700,000 Twitter users (``timelines’’) along a number of psychological dimensions over extended periods of time. We investigate the presence of CTs in these time series, particularly focusing on quantitative time series parameters such as increased autocorrelation, increased variance, and increased within-subjects cross-correlations between different psychological dimensions. Our results indicate that CT-related early warning indicators may be useful to predict the onset and development of pathological psychological states. Our work may lead to the development of advanced intervention strategies that leverage social media to mitigate mental health issues. Our methods may furthermore extend to large-scale collective social media data to predict social unrest and pending transitions of collective mood states.

 

Georgia Tech, School of Interactive Computing, GVU Center

Communities in Distress: Opportunities and Challenges of Social Media in Mental Well-being

Abstract

Social media sites are continually creating rich repositories of information relating to our activities, emotion and linguistic expression. In this talk I will discuss how such trails of data may be harnessed to reason about our mental health concerns. First, I will discuss a series of studies that examined how cues derived from social media around major life events (e.g., childbirth) and societal disruptions (e.g., violence) may help infer risks to well-being. Second, I will discuss threads of research in which we investigated the role of social media systems in providing a platform of expression to distressed communities and communities exhibiting deviant health behaviors. Broadly, I will reflect on how this new line of research bears potential in informing the design of interventions for improved well-being, privacy and ethical considerations, and in this context, the challenges and opportunities of the increasing ubiquity of social media.

Bio

Munmun De Choudhury is currently an assistant professor at the School of Interactive Computing, Georgia Tech. Munmun’s research interests are in computational social science, with a focus on reasoning about health and well-being from social digital footprints. She has been recipient of ACM SIGCHI 2014 best paper award and ACM SIGCHI honorable mention awards in 2012 and 2013, the Edenfield Faculty Fellowship, the Yahoo! Faculty Engagement award and has authored nearly 50 peer-reviewed publications. Her work has also been extensively covered by venues like the New York Times, the TIME magazine, the Wall Street Journal, and the NPR. Earlier, Munmun was a faculty associate with the Berkman Center for Internet and Society at Harvard, a postdoctoral researcher at Microsoft Research, and obtained her PhD in Computer Science from Arizona State University in 2011.

 

Northwestern University, McCormick School of Engineering, Department of Industrial Engineering and Mangagment Sciences, Department of Communication Studies

Leveraging Computational Social Science to address Grand Societal Challenges

Abstract

The increased access to big data about social phenomena in general, and network data in particular, has been a windfall for social scientists. But these exciting opportunities must be accompanied with careful reflection on how big data can motivate new theories and methods. Using examples of his research in the area of networks, Contractor will argue that Computational Social Science serves as the foundation to unleash the intellectual insights locked in big data. More importantly, he will illustrate how these insights offer social scientists in general, and social network scholars in particular, an unprecedented opportunity to engage more actively in monitoring, anticipating and designing interventions to address grand societal challenges.

Bio

Noshir Contractor is the Jane S. & William J. White Professor of Behavioral Sciences in the McCormick School of Engineering & Applied Science, the School of Communication and the Kellogg School of Management at Northwestern University, USA. He is the Director of the Science of Networks in Communities (SONIC) Research Group at Northwestern University and a board member of the Web Science Trust. He is investigating factors that lead to the formation, maintenance, and dissolution of dynamically linked social and knowledge networks in a wide variety of contexts. His research program has been funded continuously for almost two decades by major grants from the U.S. National Science Foundation with additional funding from the U.S. National Institutes of Health (NIH), Army Research Laboratory, Air Force Research Laboratory, Army Research Institute, NASA, Rockefeller Foundation, Gates Foundation, and the MacArthur Foundation. > His book titled Theories of Communication Networks (co-authored with Professor Peter Monge and published by Oxford University Press), received the 2003 Book of the Year award from the Organizational Communication Division of the National Communication Association. He was a recipient of the 2014 National Communication Association’s Distinguished Scholar Award and in 2015 he was elected a Fellow of the International Communication Association. He is also the co-founder and Chairman of Syndio, which offers organizations products and services based on network analytics. Professor Contractor has a Bachelor’s degree in Electrical Engineering from the Indian Institute of Technology, Madras and a Ph.D. from the Annenberg School of Communication at the University of Southern California.

  

Department of Sociology, Inter-university Center for Social Science Theory and Methodology (ICS), University of Groningen

Ethnic segregation and the fragility of opinion pluriformity in a diverse society

Abstract

Due to mass migration, ethnic diversity is increasing in many developed countries. Scholars as well as policy makers debate effects on social integration. One concern in particular is that ethnic diversity seems to come almost inevitably with segregation of different subgroups in their spatial and social networks. Segregation may threaten opinion pluriformity and foster instead a polarized society with large opinion divisions between and strong coherence within opposed factions. The current paper presents computational multi-agent models based on social theory that highlight alternative and competing theoretical perspectives on the conditions under which segregation in a diverse society may weaken opinion pluriformity and foster polarization. Two classes of models are compared. Models assuming negative influence explain pluriformity based on the psychological mechanism to strengthen opinion differences with highly dissimilar others. Models rooted in the theory of persuasive influence assume that opinion changes are driven by the exchange of arguments. It is shown how the different models entail competing implications for the consequences of segregation: while models assuming negative influence suggest that pluriformity can benefit from segregation, models assuming persuasive influence imply the opposite effect. It will be discussed how an integration of computational modelling with empirical data – both big and small – is needed to better understand how segregation in a diverse society affects pluriformity and polarization.

 

DTU Compute, Technical University of Denmark

The fundamental structures of complex social systems

Abstract

Complex social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns expressed on the timescale of years. Models of such social dynamics are crucial for understanding spreading of influence or diseases in the society, formation of friendships, or productivity of teams. Despite significant progress in modeling of social networks in recent years, little is known about the fundamental structures that govern social dynamics. Dynamic social systems have proven to be difficult to model, showing community-overlap and complex hierarchies, displaying dynamics on multiple timescales, etc. As a result, most current approaches struggle to provide meaningful and interpretable picture of dynamic complex social systems. Here we explore high-resolution social dynamics in a densely connected population, uncovering a class of fundamental structures embedded in the social fabric. We show how social complexity can be disentangled using a new kind of temporal communities - cores - inferred directly from high-resolution data, offering new possibilities for quantitative analysis of social systems. Cores represent social contexts, creating a vocabulary describing social lives, thus providing a powerful simplification of social lives. This simplification is so efficient that we use it to show that social dynamics are highly predictable.

 

Carnegie Mellon University, Institute for Software Research, School of Computer Science

Know Your Data! Know Your Methods!

Abstract

In this talk Jürgen Pfeffer focuses on two fundamental issues related to Computational Social Science (CSS). First, CSS data are almost always secondary data and often researchers have only limited information about how the data were collected, stored, manipulated, and filtered. In comparative or overtime analysis interesting results can be created by data artifacts rather than behavioral difference. Second, the majority of social science based methods were developed in the context of small groups. Applying the same methods to thousands or millions of actors raises questions whether algorithmic assumptions or the interpretation of results of these metrics are still valid. What do some of these metrics, that we apply every day, really do? Do all my metrics fit to all of my data? Issues related to data and methods call for higher awareness which might lead to less spectacular results.

 

University of Oxford, Oxford Internet Institute

Big Data and Social Theory

Abstract

Publications about big data currently fall into two camps: on the one hand are those which announce new discoveries about online social behaviour based on regularities in large-scale digital data. On the other hand, there are those which criticize the social implications big data applications and also the epistemological validity of big data research. This talk will address a related but hitherto unexplored question: how do big data findings fit into and advance social theory? To answer this question requires defining data and big data, and identifying the workings and social implications of the ‘objects’, mostly social media, to which these data belong. Once the roles of social media have been located among the overall uses of information and communication technology, it is also becomes possible to put big data findings in their place: Examples include how Wikipedia provides a popular source of information, how Twitter mobilizes political protest, and the correspondence between online and offline characteristics of Facebook users. While these examples provide interesting new findings, it is necessary to put them into the larger context of how social media are changing the role of media in society: for example, do social media change the agenda-setting process compared to traditional media? How do social media add to the maintenance of interpersonal ties in everyday life? The importance of ‘context’ in big data research is therefore not about the contextual nature of data, but rather to about how social media uses fit into larger patterns of political and cultural change resulting from the use of new digital technologies. Data-driven discoveries do not advance social science knowledge in a vacuum. Nor are they constrained by epistemological conundrums. But the main advances of computational social science will take place when it makes sense of the main objects from which we derive big data.

Bio

Ralph Schroeder is Professor and director of the Master's degree in Social Science of the Internet at the Oxford Internet Institute. Before coming to Oxford University, he was Professor in the School of Technology Management and Economics at Chalmers University in Gothenburg (Sweden). His recent books are Rethinking Science, Technology and Social Change (Stanford University Press, 2007) and, co-authored with Eric T. Meyer, Knowledge Machines: Digital Transformations of the Sciences and Humanities (MIT Press 2015). He is the author of six books, editor and co-editor of four volumes, and has published more than 125 papers on virtual environments, Max Weber, sociology of science and technology, e-Research and other topics. Recent projects have focused on big data, including ‘Accessing and Using Big Data to Advance Social Science Knowledge’ (2012-2014), funded by the Sloan Foundation, and ‘Data-driven Approaches to Evidence-informed Policymaking’ for the European Commission (2015).