## 2 Background Information
### Infodemiology  
Traditional surveillance systems involved to track data from different sources, such as school absenteeism logs or emergency call systems. However, the design of effective and reliable methods to detect outbreaks is a challenge because the interval between a person becomes infected until their diagnosis is confirmed can be delayed from weeks to months. Moreover, this situation is aggravated in areas where there are not reliable resources for diagnosis, so the infection may remain undetected or be diagnosed when the treatment is less effective.  
Infodemiology may be defined as the science that deals with information on the Internet with the aim to inform public health and public policy [2]. This research field has different benefits. For example, infodemiology facilitates the extraction of feedback from patients, since they have more confidence and are calmer at home, an environment that encourages sincerity, since its privacy is often perceived as absolute.  
To conduct infodemiology studies it is necessary to find data sources that can be processed. On the one hand, some researches used search queries to obtain the information. In this sense, Google Trends is the de facto data source. [PERSON] et al. [3] used Google Trends to analyze search queries regarding cancer to determine the distribution of information seeking in English-speaking countries.  
Their results show a seasonality pattern in searching for cancer. [PERSON] et al. [4] measured the online seeking behaviour towards asthma in the United States from 2004 to 2015 with the objective of predict future trends regarding asthma. Their results show robust forecasting models for a five-year period. However, Google Trends is not the only data-source used for search queries. Wikipedia, for example, is another popular data-source that has been used to analyze the searching behaviour towards epilepsy and epileptic seizures for English-speaking internet users [5]. On the other hand, social networks, especially Twitter, is the other major Infodemiology data source. In this sense, [PERSON] et al. [6] analyzed tweets related to the health domain, showing how and when people make use of the internet with the aim of providing clues about future health concerns and expectations. Other works, such as [7] explored the reliability of Twitter as a real-time content and sentiment trend-tracking tool by the analysis of two million Twitter posts about swine flu, swineflu, and H1N1. Their results indicate that tweets containing information from news websites were the most popular sources, over government and health agencies.  
Ideal infodemiology systems should work in real time and produce reliable evidence-based content [8]. To achieve these goals, it is necessary the existence of corpora that permit to validate new tools and methods. [PERSON] et al. [9] published a gold-standard corpus to test how syndromic surveillance systems are able to identify semantics with respect to disease outbreak events. This corpus contains 200 documents based on news, with geographic metadata and specific information of different infectious diseases such as bird flu. A more recent corpus can be found in [10], where the authors compiled 1090 tweets to identify texts written in Filipino related to infodemiology. However, as far as our knowledge, the corpus is not publicly available.