## 3 Methods
### Char-Grams Models  
In this suite of experiments, we applied the char-gram model instead of the word-gram model. Char-grams were generated similar to word-grams. In a nutshell, the pipeline can be described as follows. First, tweets were transformed to lowercase; then, non-informative characters, such as line-breaks, tabs, html entities, or non-ascii characters were removed and, finally, multiple spaces were collapsed into single spaces. If we take the example that we used above to illustrate the output features  
Figure 6: Combinations of unigrams and bigrams, bigrams and trigrams, and unigrams, bigrams and trigrams for the random-forest (RF) classifier. Figure 7: Combinations of unigrams and bigrams, bigrams and trigrams, and unigrams, bigrams and trigrams for the Sequential Minimal Optimization (SMO) classifier. for the word-gram models, the same text (virus con sintima del zika avanza en el Zulia), generates the following five-length char-grams: _avan, _con_, _del_, _el_z_, _en_e_, _sint_, _zika, _zuli_, a_ava, a_del, a_en_, anza_, avanz, con_s, del_z, el_zi, el_zu, en_el, ika_a, intom, irus_, ka_av, l_zik, l_zul, ma_de, n_el_, n_sin, ntoma, nza_e, oma_d, on_si, rus_c, s_con, sinto, toma_, us_co, vanza, virus, za_en, zika_. First, the results of the performance of applying five-, six-, seven- and eight-length char-grams for the char-gram model are shown in Table 5.  
The results concerning fixed length char-grams, shown in Table 5, indicate a minor improvement con respect word-grams. First, the results for five-char-grams achieve its best accuracy for the RF classifier and corpus-3 with a 90.70%; SMO, however, requires the double of tweets to reach until an accuracy of 90.00%. With sequences of six-char-grams, the overall accuracy of both classifiers decreases,  
\begin{table} \begin{tabular}{l r r r r r r r r} \hline \hline  & \multicolumn{6}{c}{**Five-Char-Grams**} \\ \hline  & \multicolumn{6}{c}{**RF**} & \multicolumn{6}{c}{**SMO**} \\ \hline
**Set** & **P** & **R** & **F1** & **ACC** & **P** & **R** & **F1** & **ACC** \\ \hline corpus-1 & 87.00 & 87.00 & 87.00 & 87.00 & 81.80 & 80.00 & 79.70 & 80.00 \\ corpus-2 & 89.30 & 89.00 & 89.00 & 89.00 & 84.00 & 84.00 & 84.00 & 84.00 \\ corpus-3 & 91.00 & 90.80 & 90.70 & **90.80** & 89.80 & 89.80 & 89.70 & 89.80 \\ corpus-4 & 90.10 & 90.10 & 90.10 & 90.10 & 90.00 & 90.00 & 90.00 & **90.00** \\ corpus-5 & 89.30 & 89.30 & 89.20 & 89.30 & 89.50 & 89.50 & 89.50 & 89.50 \\ corpus-6 & 88.10 & 88.10 & 88.10 & 88.10 & 86.90 & 86.90 & 86.90 & 86.90 \\ corpus-7 & 80.60 & 80.60 & 80.60 & 80.60 & 81.10 & 81.00 & 81.00 & 81.00 \\ \hline \hline \end{tabular}   
**Six-Char-Grams** \begin{tabular}{l r r r r r r r r} \hline \hline  & \multicolumn{6}{c}{**RF**} & \multicolumn{6}{c}{**SMO**} \\ \hline
**Set** & **P** & **R** & **F1** & **ACC** & **P** & **R** & **F1** & **ACC** \\ corpus-1 & 78.00 & 78.00 & 78.00 & 78.00 & 70.30 & 70.00 & 69.90 & 70.00 \\ corpus-2 & 86.70 & 86.00 & 85.90 & **85.00** & 82.50 & 82.50 & 82.50 & 82.50 \\ corpus-3 & 89.90 & 89.80 & 89.70 & 84.30 & 89.80 & 89.80 & 89.70 & **89.80** \\ corpus-4 & 85.90 & 85.90 & 85.90 & 85.90 & 83.30 & 87.60 & 87.60 & 87.60 \\ corpus-5 & 85.00 & 84.90 & 84.90 & 83.60 & 85.00 & 84.90 & 84.90 & 84.90 \\ corpus-6 & 83.10 & 83.00 & 82.90 & 79.80 & 85.30 & 85.10 & 85.10 & 85.10 \\ corpus-7 & 75.60 & 75.20 & 75.20 & 72.70 & 77.60 & 77.10 & 77.00 & 77.10 \\ \hline \hline \end{tabular}   
**Seven-Char-Grams**  
\begin{tabular}{l r r r r r r r r} \hline \hline  & \multicolumn{6}{c}{**RF**} & \multicolumn{6}{c}{**SMO**} \\ \hline
**Set** & **P** & **R** & **F1** & **ACC** & **P** & **R** & **F1** & **ACC** \\ corpus-1 & 79.00 & 79.00 & 79.00 & 76.00 & 72.10 & 72.00 & 72.00 & 72.00 \\ corpus-2 & 85.50 & 85.00 & 84.90 & 83.00 & 84.10 & 84.00 & 84.00 & 84.00 \\ corpus-3 & 85  
but in different level; whereas SMO only losses an 0.20% of accuracy (89.80%), RF decreases an 5.70% (85.00%). Similar to the five-char-grams, SMO also requires the double of tweets than RF to achieve its best result.