## 3 Methods
### Word Gram Model  
To generate the features for the word-gram model, we defined the word-delimiter token. In Spanish, words with underscores and/or hyphens are usually treated as individual units because hyphens as used in Spanish (1) to join two first names, when the second one can be confused with the surname; (2) to establish circumstantial relationships among proper names; (3) to create compounds terms by the union of two nouns, of which the second acts, in opposition, as modifier of the first, forming both a unitary concept; (4) to establish relationships among concepts or units; and (5) to create a division of words at the end of the line. Then, HTML tags, numbers, and hyperlinks were stripped from the text and sequences of two or more consecutive spaces and line-breaks were merged. Once the normalization process was finished, the unigrams, bigrams and trigrams were extracted. For example, the tweet virus con sintoma del zika avanza en el Zulia (in English, virus with symptoms of Zika advances in Zulia) produced the following unigrams: avanza, con, del, el, en, sintoma, virus, zika and zulia. Finally, a vector containing the TF-IDF scores per feature was generated. These vectors were used as input for the machine-learning classifiers. The results of the different configurations are described below. First, the results of the performance of applying unigrams, bigrams, and trigrams, used in isolation, for the word-gram model are shown in Table 3. The results highlight that unigrams achieve better accuracy than bigrams and trigrams. SMO achieved its best result on the corpus-3 with an accuracy of 90.50%. The accuracy of RF was slightly lower, with 88.00% with the same corpus. The relationship between the accuracy of unigrams with the corpus size is shown in Figure 4 for RF and in Figure 5 for SMO. In these figures we can observe how RF and SMO behaved similar from corpus-1 (100 tweets) to corpus-6 (3200 tweets) for unigrams. The accuracy decreased in corpus-7 (6164 tweets) with 77.90% for RF and 77.30% for SMO. This loss of accuracy is caused by the cut-off filter because, as the corpus size gets larger, some features do not pass the threshold. Regarding the bigrams, the results shown in Table 3 indicate that word-pairs achieve good performance in all corpus for both classifiers, RF with a mean of 73.76% and a standard deviation of 5.46% and SMO with a mean of 75.06% and a standard deviation of 6.23%. As expected, the accuracy of the bigrams was less than in the unigrams. The relationship between the accuracy of bigrams with the corpus size is shown in Figure 4 for RF and in Figure 5 for SMO. In these figures we can observe how both models begin with little accuracy in the small corpus, reach their peaks of accuracy in the intermediate-sized corpus and, finally, decrease linearly their accuracy each time the corpus size is doubled. The use of trigrams achieved the lowest results, behaving in some cases as random classifiers. However, the use of trigrams (or even bigger grams) should not be dismissed so lightly, because they can represent idioms or quotes much better than unigrams. With respect to the classification algorithms, it can be seen that SMO achieved better results than RF in unigrams, bigrams and trigrams models. The difference in unigrams is significant, SMO achieves an accuracy of 90.50% for corpus-3 since RF achieves an accuracy of 88.00%. On the other hand, the differences in the results of SMO and RF in trigrams decreases. SMO model achieves better accuracy than RF because SMO is performs better on sparse data. Word-grams model tends to produce large amounts of features but only a small fraction of these features may have a value greater than zero. In addition, we can observe that the difference between SMO and RF was stable among the different corpus. Figures 4 and 5 show that unigrams always achieved better accuracy than bigrams or trigrams.