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Evaluating the Impact of Social Media in Detecting Health-Violating Restaurants
Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Netowrks Analysis and Mining, ASONAM 2016
  • Mikel Joaristi, Boise State University
  • Edoardo Serra, Boise State University
  • Francesca Spezzano, Boise State University
Document Type
Conference Proceeding
Publication Date
1-1-2016
Disciplines
Abstract
Nowadays, detecting health-violating restaurants is a serious problem due to the limited number of health inspectors in a city as compared to the number of restaurants. Rarely inspectors are helped by formal complains, but many complaints are reported as reviews on social media such as Yelp. In this paper we propose new predictors to detect health-violating restaurants based on restaurant sub-area location, previous inspections history, Yelp reviews content, and Yelp users behavior. The resulting method outperforms past work, with a percentage of improvement in Cohen’s kappa and Matthews correlation coefficient of at least 16%. In addition, we define a new method that directly evaluates the benefit of a classifier on the ability of an inspector in detecting health-violating restaurants. We show that our classification method really improves the ability of the inspector and outperforms previous solutions.
Citation Information
Mikel Joaristi, Edoardo Serra and Francesca Spezzano. "Evaluating the Impact of Social Media in Detecting Health-Violating Restaurants" Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Netowrks Analysis and Mining, ASONAM 2016 (2016)
Available at: http://works.bepress.com/francesca_spezzano/9/