# Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
## 3 Current State of Bot-Related Data
### Not All Bots Are Created Equal  
Before proceeding into the feature extraction process, we defined multiple classes for the retrieved accounts. Thus, we proceeded beyond a simple binary categorization of either bot or human to explore different bot classes which are presented in Table 2, along with a short description and the datasets that comprise them. The numbers of the datasets, refer to the serial number of each dataset, as presented in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bot Class** & **Description** & **Class Datasets (\#)** & **Instances** \\ \hline _Spam Bot_ & Accounts that post spam content & 24,22,15,8 & 17,071 \\ \hline _Social Bot_ & Bots that try to attract followers & 17,13,10,23 & 11,653 \\ \hline _Political Bot_ & Bots that deal with politics & 3,6,9 & 497 \\ \hline _Cyborg_ & Human monitored bots & 1,2,11 & 5891 \\ \hline _Self-declared_ & Accounts that state they are bots & 4 & 1198 \\ \hline _Other Bot_ & Other type of simple bots & 7,12,18,21,14 & 2109 \\ \hline _Human_ & Genuine human accounts & 5,6,7,12,13,14,16,18,19,20,24 & 30,752 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mapping of bot types and datasets and their description. The numbers of the datasets, refer to the serial number of each dataset, as presented in Table 1.  
Figure 2: The figure on the left shows the percentage of accounts that stopped posting content per year. Interestingly more than 75% of bots remained inactive after 2017. The figure on the right shows the number of each type of account to the year they posted their last tweet. We observe that most labeled bot accounts that are included in the public datasets are no longer active. As presented in Section 1 and Table 2, the following bot classes appear in recent literature and have been retrieved in combination with the information contained in the datasets that we utilized. * Spam bots encapsulate every type of bot that is related to continuously posting spam content. * Social bots are related to impersonators, influence bots and paybots. * Political bots are a rather unique class, including accounts that have been used for political purposes. * Self-declared bots refer to accounts that identify themselves as bots. * Cyborgs include celebrities, news agencies and organizations, and as discussed in Section 2, they have not been included in recent multi-class bot detection approaches. * Other bots include all bots that do not fall into any of the previous categories according to the dataset description. It is important to clarify that across these umbrella classes, there are malevolent and benevolent bots as well. To measure the efficiency of the defined bot classes we trained six binary Random Forest classifiers, one for each bot type. As depicted in Section 2, Random Forests have proven to be quite effective for similar bot classification tasks. The hyperparameters of each classifier are similar to the ones defined in Section 5, while the features that have been used for training our classifiers are thoroughly presented in Section 4.1. Each classifier has been trained on a balanced subset of data, using ADASYN [63] as discussed in Section 4.2. Each subset includes human accounts and instances of each bot type, split into train and test samples. The produced classifiers were thus trained on each subset and tested across all the others. The rationale behind this approach was to evaluate the cross-type performance of the individual binary classifiers, validating our hypothesis. We measured the performance of each bot classifier targeting the same or other bot types by calculating the precision and recall metrics. We can easily observe from our exploratory analysis (Figure 3) that classification precision and recall scores are strong for every classifier that targets the same bot type as the one it has been trained on. This is true for all bot types, as indicated by the dark colored diagonal blocks in Figure 3, except for \"other bots\".