## 1 Introduction  
Epidemics exert great pressure on public health services because they can collapse health services and cause panic among society. Although epidemic outbreaks have been appearing since antiquity (black plague, Spanish flu, cholera, etc.), even in the 21 st century they are still one of the most serious problems in current society. Recent events, such as the Severe Acute Respiratory Syndrome (SARS) in 2012, the Influenza H1H1 Pandemic in 2009, or recent cases of the Zika virus in Central America, remind us of the serious impact that the outbreaks of infectious diseases cause on many spheres of the society. The World Health Organisation (WHO) annually offers a series of global health statistics, consisting of data from 194 countries. In its 2018 report ([[https://apps.who.int/iris/bitstream/handle/10665/272596/9789241565585-eng.pdf?ua=1](https://apps.who.int/iris/bitstream/handle/10665/272596/9789241565585-eng.pdf?ua=1)]([https://apps.who.int/iris/bitstream/handle/10665/272596/9789241565585-eng.pdf?ua=1](https://apps.who.int/iris/bitstream/handle/10665/272596/9789241565585-eng.pdf?ua=1))), WHO reported that neglected tropical diseases (NTDs) required mass or individual treatment for 1.5 billion people.  
Infodemiology is a novel research field that consists of the analysis of online information regarding public health that aims to complement traditional surveillance methods and, therefore, improve the early detection of these outbreaks [1]. Internet content can be exploited, either by monitoring query searches and by the analysis of the user's opinions on social networks. However, both scenarios deal with large volumes of information. It is necessary, therefore, to develop efficient algorithms for Information Retrieval (IR) specialized in surveillance tasks, but the challenge lies in the fact that these algorithms need to handle texts written in natural language that express subjective concerns in an efficient way.  
Consequently, in this work we conducted an infodemiology-based study to detect positive or negative utterances related to infectious diseases so that future syndromic surveillance systems could be improved. In addition, this study compares two different information retrieval models: (1) word-grams and (2) char-grams. As a final contribution, we provide a balanced corpus of tweets compiled from countries from Central America related to the mosquito-borne infectious diseases domain.  
Specifically, the contributions of this work can be summarized as:  
1. Development of a balanced corpus of mosquito-borne infectious diseases composed by 6124 tweets written in the Spanish. This corpus was manually labelled by different volunteers that worked individually. Therefore, the corpus is ordered by consensus, with the tweets that the volunteers agreed on the most in the first positions.
2. Compare the accuracy between two Information Retrieval models: Word-grams and Char-grams. The comparison is performed against different partitions of the corpus and between two machine-learning classifiers: random-forest (RF), a decision-tree classifier, and sequential minimal optimization (SMO), a support vector machines (SVM) classifier.  
The remainder of this paper is organized as follows: Section 2 provides some background information concerning infodemiology and opinion mining. Section 3 describes the development of the corpora used in this research. Section 4 describes the experiment performed whereas Section 5 contains the link to the resources generated. Finally, Section 6 summarizes the conclusions of the paper and indicates further line actions.