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Call for Contributions

Workshop: Making Sense of Online Data for Population Research

Boy contributing to the production of big data for potential population research.

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We are inviting submissions for contributions to the workshop Making Sense of Online Data for Population Research at the International Conference on Web and Social Media (ICWSM) to be held in Stanford/USA on June 25, 2018.

Submission deadline is April 25, 2018.

The goal of this workshop is to provide a space for researchers with background in social sciences, quantitative methods and computational approaches, and from academia and industry, to come together and discuss how to appropriately interpret online data and share creative ways for grappling with issues related to bias.

The workshop will be organized along three main themes:

  1. understanding underlying populations and demographic processes,
  2. understanding data-generating behaviors, and
  3. leveraging scale to ask new questions.

Call for Contributions

Participants who would like to present must submit an extended abstract (2-4 pages) or a full paper via this GoogleForm by April 25, 2018.The submissions will be evaluated by the Organizing Committee on the basis of quality and fit to the workshop theme. Accepted abstracts and papers will be presented as short presentations.

All presentations must be in English. All participants, regardless of whether they are interested in presenting or only attending the workshop must register for the workshop via the ICWSM registration webpage  by June 1, 2018

Key Dates

  • Workshop date: June 25, 2018
  • Deadline to submit extended Abstract: April 25, 2018 (23:59 Pacific Standard Time)
  • Deadline for Registration: June 1, 2018

Workshop Rationale: Demography, Online Data, and Bias

The global spread of social media has generated new opportunities for demographic research, as individuals leave increasing quantities of traces online that can be aggregated and mined for population research.However, whether population research is carried out on traditional data or online data, the key questions remain: What are we aggregating? And how are we aggregating it? To understand population processes requires thoughtful consideration of populations and behaviors as they are manifested in data.

We see growing opportunities for collaboration between computer scientists and social scientists specifically centered on population research methodology. Demography has been a data-driven discipline since its birth, and data collection and the development of formal, i.e. mathematical, methods have sustained most of the major advances in our understanding of population processes.

All data come with some type of bias, and an essential responsibility of scientists in any field is to understand the nature of the bias manifested in the data from which they draw inference and test hypotheses. In this respect, data generated from human interaction with digital technology are no different from data traditionally used in social science research, but they do pose new and interesting problems.

Speakers (confirmed)

© Ingmar Weber

Ingmar Weber is a senior scientist in the Social Computing group at the Qatar Computing Research Institute (QCRI). His interdisciplinary research uses large amounts of online data from social media and other sources to study human behavior at scale.

Particular topics of interest include studying lifestyle diseases and population health, quantifying international migration using digital methods, and looking at political polarization and extremism.

 

© Joshua Blumenstock

Joshua Blumenstock is an Assistant Professor at the U.C. Berkeley School of Information, and the Director of the Data-Intensive Development Lab. His research lies at the intersection of machine learning and development economics, and focuses on using novel data and methods to better understand the causes and consequences of global poverty.

Dr. Blumenstock's research projects have spanned a variety of substantive topics from analyzing the response to armed conflict and natural disasters to mapping poverty using mobile phone data.

 

 

Program Committee And Panelists

Lee Fiorio (University of Washington) is a PhD student in geography whose primary research agenda is to use computational and statistical methods to bring together demographic and geographic perspectives on migration and urban development. He has a keen interest in developing methods that use large datasets from social media and other sources to achieve new understandings of population processes. Most of his research deals with measuring flows of people at multiple scales with the goal of studying systemic disadvantage.

Emilio Zagheni (Max Planck Institute for Demographic Research/University of Washington) is a demographer who uses mathematical, statistical, and computationally-intensive approaches to study the causes and consequences of population dynamics. Motivated by the ambition to improve people's lives through the scientific study of our societies, he is consolidating a portfolio that leverages interdisciplinary approaches to monitor demographic change, to explain population processes, and to predict future demographic outcomes.

More specifically, his research addresses three main inter-related topics:

  1. combining large social media data with traditional sources to track and understand migrations,
  2. evaluating the consequences of population aging on intergenerational transfers,
  3. modeling the relationships between population dynamics, the environment and infectious diseases. A common thread across his substantive interests is a consistent drive to develop methods and to analyze data in creative ways that further advance our understanding of social phenomena.

Afra Mashhadi (University of Washington) is an Affiliate Assistant Professor of Sociology at the University of Washington and was formerly a senior research scientist at Bell Laboratories, Nokia. She is interested in developing mathematical and computational models that leverage the proliferation of sensors and breakthroughs in machine learning to

  1. understand societies and social phenomena at different spatial scales,
  2. model social dynamics of human behavior. More specifically her research focus is on sensing, modeling, understanding and predicting human behavior using the digital traces that are generated daily in online and offline lives.

Bogdan State (Stanford Unversity/Facebook) is a computational social scientist and an MS candidate in Computer Science at Stanford. Bogdan has been working on the Facebook Core Data Science team for the past four years. His industry contributions have ranged from developing large-scale business intelligence systems to improving the performance of ranking models. Academically, he is interested in using Internet data to decipher the basic mechanisms of human social interaction.

Dennis Feehan (University of California, Berkeley) is a demographer and quantitative social scientist.  His research interests lie at the intersection of networks, demography, and quantitative methodology. He is currently Assistant Professor of Demography at the University of California, Berkeley. Previously, he worked as a Research Scientist at Facebook.

Previous Workshops

Acknowledgments

This workshop is organized in partnership with the IUSSP Panel on Big Data and Population Processes with support from the Max Planck Institute for Demographic Research (MPIDR).

Contact

Questions, comments or suggestions? Please contact us by email to odfpr18@googlegroups.com
 

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