Can you Trust the Trend: Discovering Simpson's Paradoxes in Social Data
| Autor: | Kristina Lerman, Peter G. Fennell, Nazanin Alipourfard |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Entire population Computer science Regression analysis 02 engineering and technology Simpson's paradox Computer Science - Computers and Society Exploratory data analysis 020204 information systems Computers and Society (cs.CY) 0202 electrical engineering electronic engineering information engineering Econometrics 020201 artificial intelligence & image processing Aggregate data Stack (mathematics) |
| Zdroj: | WSDM |
| DOI: | 10.48550/arxiv.1801.04385 |
| Popis: | We investigate how Simpson's paradox affects analysis of trends in social data. According to the paradox, the trends observed in data that has been aggregated over an entire population may be different from, and even opposite to, those of the underlying subgroups. Failure to take this effect into account can lead analysis to wrong conclusions. We present a statistical method to automatically identify Simpson's paradox in data by comparing statistical trends in the aggregate data to those in the disaggregated subgroups. We apply the approach to data from Stack Exchange, a popular question-answering platform, to analyze factors affecting answerer performance, specifically, the likelihood that an answer written by a user will be accepted by the asker as the best answer to his or her question. Our analysis confirms a known Simpson's paradox and identifies several new instances. These paradoxes provide novel insights into user behavior on Stack Exchange. Comment: to appear in the Proceedings of WSDM-2018 |
| Databáze: | OpenAIRE |
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