Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media

Alexandra Olteanu, Onur Varol, Emre Kiciman

Proceedings of The 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing |

Published by Association for Computing Machinery, Inc.

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Millions of people regularly report the details of their real-world experiences on social media. This provides an opportunity to observe the outcomes of common and critical situations. Identifying and quantifying these outcomes may provide better decision-support and goal-achievement for individuals, and help policy-makers and scientists better understand important societal phenomena.
We address several open questions about using social media data for open-domain outcome identification: Are the words people are more likely to use after some experience relevant to this experience? How well do these words cover the breadth of outcomes likely to occur for an experience? What kinds of outcomes are discovered? Studying 3-months of Twitter data capturing people who experienced 39 distinct situations across a variety of domains, we find that these outcomes are generally found to be relevant (55-100% on average) and that causally related concepts are more likely to be discovered than conceptual or semantically related concepts.

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Longitudinal Tweet ID dataset for a selection of Health, Social, and Business Experiences

April 11, 2017

This data set consists of the tweet IDs collected for the propensity-score analysis of longitudinal social media messages posted by people who mention specific health, social and business domains. This data set accompanies the paper, “Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media.”

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