Publication

Validating models for disease detection using twitter

Marcel Salathé
2013
Conference paper
Abstract

Data mining social media has become a valuable resource for infectious disease surveillance. However, there are considerable risks associated with incorrectly predicting an epidemic. The large amount of social media data combined with the small amount of ground truth data and the general dynamics of infectious diseases present unique challenges when evaluating model performance. In this paper, we look at several methods that have been used to assess influenza prevalence using Twitter. We then validate them with tests that are designed to avoid and illustrate issues with the standard k-fold cross validation method. We also find that small modifications to the way that data are partitioned can have major effects on a model's reported performance

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.