AAAI Spring Symp. on Observational Studies through Social Media and Other Human-Generated Content

About

Postscript: Thanks to everyone who joined us in March for the engaging, thought-provoking and lively OSSM symposium. And a special thanks to all our speakers and authors, our program committee and also to the organizers of the 2016 AAAI spring symposium series.

More information about the AAAI 2016 Spring Symposium Series.

While using the Internet and mobile devices, people create data, whether intentionally or unintentionally, through their interaction with messaging services, websites and other applications and devices. This means that experiments with heretofore unprecedented populations can be performed in a variety of topics. Our workshop will focus on observational studies which arise from these interactions and data, with a focus on experiments that can indicate causal inferences.

Human generated content in general, and social media in particular, are a rich repository of data for observational studies across many areas: public health, with research on prevalence of disease and on the effects of media on the development of disease; medicine, showing the ability to detect mental disease in individuals using social media; education, to optimize teaching and exams; and sociology, to prove theories previously tested on very small populations. These studies were conducted from data including social media, search engine logs, location traces, and other forms of human generated content.

While many past studies showed a correlation between variables of interest, some studies were able to show causal relationships through natural experiments or by linking data sources. Our workshop focuses on all aspects of causal inference from human generated content, with studies that developed novel methods of identifying and using natural experiments or other methods for inferring causality.

Topics include:
•Interpreting user-generated data, including text, structured data, and temporal data.
•Causal analyses in social media, for example, using propensity score matching and causal graphs.
•Identifying natural experiments and using them to understand causal inferences
•Identifying population, reporting and other biases in social media
•Applications and domain-specific explorations
•Novel methods for preserving privacy
•Ethical codes and implications

Doctoral feedback session: If you are a PhD student doing research that uses causal inference methods on social media and user generated data, we are planning to incorporate a session for a doctoral feedback during the symposium. Please submit an abstract and let us know that you would like to be considered for this. Thanks to Nathan Matias for helping organize this effort!

Agenda

Monday, March 21

9:00-10:30am
•Invited: The impact of social media on news consumption (Athey)
•Regular:The Spread of Cooperation: Peer Effects in Gift Giving (Kizilcec)

break

11:00-12:30pm
•Invited: Estimating peer effects without randomized experiments: Confounding bias and high-dimensional modeling (Eckles)
•Regular: Identifying social influence in online activity feeds: Preference-based Matched Estimation (Sharma)

lunch

2:00-3:30pm
•Invited: Subtle and not-so-subtle sources of bias in observational studies that use social media (Culotta)
•Regular: Estimating the causal impact of recommendation systems from observational data (Sharma)

break

4:00-5:30pm
•Invited: Antisocial Behavior in Online Discussion Communities (Leskovec)
•Regular: Actually, It’s About Ethics in Computational Social Science: A Multi-party Risk-Benefit Framework for Online Community Research (Keegan)

6:00-7:00pm
AAAI Spring Symposium Reception

Tuesday, March 22

9:00-10:30am
•Invited: Towards Participatory Data Analysis of Big Crisis Data (Castillo)
•Regular: Towards Real-Time Measurement of Public Epidemic Awareness: Monitoring Influenza Awareness through Twitter (Smith)

break

11:00-12:30pm
•Regular: Geolocated Twitter Panels to Study the Impact of Events (Zhang)
•Regular: Using Propensity Score Matching to Understand the Relationship Between Online Health Information Sources and Vaccination Sentiment (Rehman)
•Regular: Reducing confounding bias in observational studies that use text classification (Landeiro)

Lunch

2:00pm-3:30pm
Poster Session

4:00-5:30pm
•Invited: Causal Inference without Control Units (Glynn)
•Regular: Emoticons vs. Emojis on Twitter: A Causal Inference Approach (Pavalanathan)

6:00-7:00pm
AAAI Spring Symposium Plenary Session

Wednesday, March 23

9:00-10:30am
•Invited: A System for Extracting the Outcomes of Personal Experiences from Social Media Timelines (Kıcıman)
•Regular: Structural Causes of Bias in Crowd-derived Geographic Information: Towards a Holistic Understanding (Johnson)

break

11:00am-12:30pm
•Regular: The Monetization of Information Broadcasts: A Natural Experiment on an Online Social Network (Shmargad)
•Panel Discussion: TBD

Abstracts

  • “#FailedRevolutions: Using Twitter to Study the Antecedents of ISIS Support,” Walid Magdy, Qatar Computing Research Institute; Kareem Darwish; Ingmar Weber, Qatar Computing Research Institute
  • “Actually, It’s About Ethics in Computational Social Science: A Multi-party Risk-Benefit Framework for Online Community Research,” Brian Keegan, Harvard Business School; J. Nathan Matias, MIT Media Lab
  • “Characterizing the Demographics Behind the #BlackLivesMatter Movement,” Alexandra Olteanu, EPFL; Ingmar Weber, QCRI; Daniel Gatica-Perez, IDIAP
  • “Computational Causal Inference in Populations of Quasi-Experiments,” Aaron Shaw, Northwestern University; Benjamin Mako Hill, University of Washington
  • “Cultural Influences on the Measurment of Personal Values through Words,” Rada Mihalcea, University of Michigan
  • “Does Copyright Affect Reuse? Evidence from the Google Books Digitization Project,” Abhishek Nagaraj, MIT
  • “Emoticons vs. Emojis on Twitter: A Causal Inference Approach,” Umashanthi Pavalanathan, Georgia Institute of Technology; Jacob Eisenstein, Georgia Institute of Technology
  • “Enhancing Validity in Observational Settings When Replication Is Not Possible,” Christopher Fariss, Penn State University; Zachary Jones, Penn State University
  • “Estimating the causal impact of recommendation systems from observational data,” Amit Sharma, Microsoft Research; Jake Hofman, Microsoft Research; Duncan Watts, Microsoft Research
  • “Geolocated Twitter Panels to Study the Impact of Events,” Han Zhang, Princeton University; David Rothschild, Microsoft Research; Shawndra Hill, Microsoft Research
  • “Identifying social influence in online activity feeds: Preference-based Matched Estimation,” Amit Sharma, Microsoft Research; Dan Cosley, Cornell University
  • “Left-handed or Right-handed? A Data-driven Approach to Analysing Characteristics of Handedness based on Language Use,” Ho-gene Choe, University of Michigan; Rada Mihalcea, University of Michigan
  • “Predicting Student Engagement Through Trace Data,” Jeffrey Rokkum, Iowa State University; Reynol Junco, Iowa State University
  • “Reducing confounding bias in observational studies that use text classification,” Virgile Landeiro, Illinois Institute of Technology; Aron Culotta, Illinois Institute of Technology
  • “Risk Sharing and Mobile Phones: Evidence in the Aftermath of Natural Disasters,” Joshua Blumenstock, University of Washington; Marcel Fafchamps, Stanford University; Nathan Eagle, Santa Fe Institute
  • “Structural Causes of Bias in Crowd-derived Geographic Information: Towards a Holistic Understanding,” Isaac Johnson, University of Minnesota; Brent Hecht, University of Minnesota
  • “The Monetization of Information Broadcasts: A Natural Experiment on an Online Social Network,” Yotam Shmargad, University of Arizona
  • “The Spread of Cooperation: Peer Effects in Gift Giving,” Rene Kizilcec, Stanford University; Eytan Bakshy, Facebook; Dean Eckles, MIT; Moira Burke, Facebook
  • “Towards Real-Time Measurement of Public Epidemic Awareness: Monitoring Influenza Awareness through Twitter,” Michael Smith, George Washington University; David Broniatowski, George Washington University; Michael Paul, University of Colorado Boulder; Mark Dredze, Johns Hopkins University
  • “Using Propensity Score Matching to Understand the Relationship Between Online Health Information Sources and Vaccination Sentiment,” Nabeel Rehman, New York University; Jason Liu, New York University; Rumi Chunara, New York University

Speakers

Committee

Organizing Committee: Please contact us if you have any questions.

Program Committee: