A general method for estimating the prevalence of influenza-like-symptoms with Wikipedia data.

Influenza is an acute respiratory seasonal disease that affects millions of people worldwide and causes thousands of deaths in Europe alone. Estimating in a fast and reliable way the impact of an illness on a given country is essential to plan and organize effective countermeasures, which is now possible by leveraging unconventional data sources like web searches and visits. In this study, we show the feasibility of exploiting machine learning models and information about Wikipedia's page views of a selected group of articles to obtain accurate estimates of influenza-like illnesses incidence in four European countries: Italy, Germany, Belgium, and the Netherlands. We propose a novel language-agnostic method, based on two algorithms, Personalized PageRank and CycleRank, to automatically select the most relevant Wikipedia pages to be monitored without the need for expert supervision. We then show how our model can reach state-of-the-art results by comparing it with previous solutions.

CycleRank, or there and back again: personalized relevance scores from cyclic paths on directed graphs.

Surfing the links between Wikipedia articles constitutes a valuable way to acquire new knowledge related to a topic by exploring its connections to other pages. In this sense, Personalized PageRank is a well-known option to make sense of the graph of links between pages and identify the most relevant articles with respect to a given one; its performance, however, is hindered by pages with high indegree that function as hubs and obtain high scores regardless of the starting point. In this work, we present CycleRank, a novel algorithm based on cyclic paths aimed at finding the most relevant nodes related to a topic. To compare the results of CycleRank with those of Personalized PageRank and other algorithms derived from it, we perform three experiments based on different ground truths. We find that CycleRank aligns better with readers' behaviour as it ranks in higher positions the articles corresponding to links that receive more clicks; it tends to identify in higher position related articles highlighted by editors in 'See also' sections; and it is more robust to global hubs of the network having high indegree. Finally, we show that computing CycleRank is two orders of magnitude faster than computing the other baselines.