@INPROCEEDINGS{Alipourfard2018wsdm,
  author =       {Nazanin Alipourfard and Peter Fennell and Kristina Lerman},
  title =        {Can you Trust the Trend? Discovering Simpson�s Paradoxes in Social Data},
  booktitle =    {Proceedings of the 11th International ACM Conference on Web Search and Data Mining (WSDM)},
  year =         {2018},
  pages =        {},
  doi={10.1145/3159652.3159684},
  publisher =    {ACM},
  abstract =     {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
quite 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
provided 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.},
  keywords =     {social-dynamics},
}

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