# An Evolutionary Computation Approach for Twitter Bot Detection
## 5 Experimental Phase
This may pose a security problem on diverse data with different distributions since prediction does not take into account tweets content and, for this reason, bot accounts can publish any kind of content while ensuring that their meta-data properties are similar to the ones of a legit human profile (e.g., by increasing the number of followers with fake accounts);
* GA and GP models are the third best models with respect to accuracy and MCC and they are the second best models with respect to F1 score, on a par with [PERSON] et al. [37]. This shows that our models are capable of exposing good detection capabilities that are close to the best existing models while ensuring high interpretability of predictions. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **GA** & **GP** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON]** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] at al.** \\  & & & [36] & [44] & [45] & [19] & [38] & [43] & [23] & [37] \\ \hline Accuracy & 0.76 & 0.76 & **0.82** & **0.82** & 0.71 & 0.48 & 0.56 & 0.68 & 0.48 & 0.75 \\ F1 score & 0.78 & 0.78 & **0.85** & 0.75 & 0.75 & 0.11 & 0.49 & 0.73 & 0.63 & 0.78 \\ MCC & 0.51 & 0.52 & 0.66 & **0.67** & 0.42 & 0.08 & 0.16 & 0.35 & \(-\)0.14 & 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the baseline methods on _TwiBot-20_ test set with respect to the accuracy, F1 score, and MCC, together with qualitative performance of the GA model and the GP model. Each of these models has been trained using the training set, validated using the validation set, and tested for a final assessment using the test set. The results related to the baseline methods are based on the results presented in [6]. For each chosen evaluation metric, we highlight in bold the highest value. Table 5 shows encouraging results with respect to detection accuracy. As already stated in Section 3.1, the _TwiBot-20_ dataset is extremely challenging due to the high heterogeneity of the data contained in it and the multimodal information it can encode. The diversity of users represented in the dataset in terms of domains of interest and geographical locations allows the detection of diversified bot accounts instead of domain-constrained ones. For this reason, this dataset is a valid benchmark dataset for the Twitter bot detection problem. As we can see from the results, even state-of-the-art methods fail in reaching very high accuracy. This means that additional research effort is required to build effective models. Our models are close to the qualitative performance of state-of-the-art methods and, additionally, they are capable of providing interpretable predictions since it is possible to inspect the single weights and components of our classification methods. Therefore, our models represent valid alternatives to existing methods. Figures 5 and 6 show that the error rate converges after approximately 50 generations and remains approximately constant to a value of 0.23-0.25 for the entire evolutionary process. In particular, the error rate in the GA model keeps itself a bit higher than the error rate in the GP model. After the evaluation phase carried out on the test set, we analyzed some of the classification errors made by the GP model to understand the features that contribute the most to the erroneous predictions. In particular, we analyzed four false positives and four false negatives. False positives are human profiles that are predicted as bots, while false negatives are bot profiles that are predicted as humans. False positives were erroneously predicted as bot accounts because of extremely low reputation and followers growth rate values, often combined with high frequencies of hashtags, retweets, and URLs. Specifically, if the reputation and followers growth rate are not high, then profiles only associated with retweets containing many hashtags and URLs that, at the same time, have no self-published tweets, will probably be predicted as bots in any case.