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OSTS 2016 - Symposium on OBSERVATIONAL STUDIES THROUGH SOCIAL MEDIA AND OTHER HUMAN-GENERATED CONTENT

Date2016-03-21 - 2016-03-23

Deadline2015-12-01

VenueStanford University, USA - United States USA - United States

Keywords

Websitehttps://www.aaai.org/Symposia/Spring/spr...

Topics/Call fo Papers

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 symposium 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 symposium 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
Topics include the following:
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

Last modified: 2015-09-01 23:11:56