# Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
## 3 Current State of Bot-Related Data
### Data Collection and Exploratory Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \# & **Dataset Name** & **H/Av.** & **B/Av.** & **C/Av.** & **Description** \\ \hline
1 & This paper (2021) & - & - & 232 & Popular companies \\ \hline
2 & This paper (2021) & - & - & 160 & News agencies \\ \hline
3 & [PERSON] et al. [9] & - & 585/442 & - & Active political bots \\ \hline
4 & & - & 1488/1198 & - & Self-declared bots Botwiki \\ \hline
5 & [PERSON] et al. [8] & 2000/1976 & - & - & Verified human accounts \\ \hline
6 & & 8092/7369 & 42,466/42 & - & Political accounts \\ \hline
7 & & 386/331 & 143/101 & - & Labeled by Botometer \\ \hline
8 & & - & 21,964/1733 & - & Porn spammers \\ \hline
9 & [PERSON] et al. [5] & - & 62/13 & - & Active political bots \\ \hline
10 & & - & 1088/707 & - & Vendor-Purchased bots \\ \hline
11 & & - & - & 5971/5499 & Celebrity accounts \\ \hline
12 & [PERSON] et al. [51] & 368/313 & 391/311 & - & Bot and human accounts \\ \hline
13 & [PERSON] et al. [18] & 7479/5797 & 18,508/6752 & - & Stock related \\ \hline
14 & [PERSON] et al. [28] & 1747/1291 & 826/657 & - & Simple bots and humans \\ \hline
15 & & - & 1506/1356 & - & Bots spamming content \\ \hline
16 & [PERSON] et al. [6, 61] & 3474/2397 & - & - & Genuine Twitter accounts \\ \hline
17 & & - & 5915/4166 & - & Bots “hunting” followers \\ \hline
18 & [PERSON] et al. [62] & 1522/1281 & 1130/980 & - & Simple bots and humans \\ \hline
19 & & 1481/1162 & - & - & Italian elections E13 \\ \hline
20 & & 469/417 & - & - & Fake Project TFP \\ \hline
21 & [PERSON] et al. [10] & - & 1337/60 & - & InterTwitter INT \\ \hline
22 & & - & 845/588 & - & Technology spam TWT \\ \hline
23 & & - & 1170/28 & - & Fast Followerz FSF \\ \hline
24 & [PERSON] et al. [4] & 19,277/8238 & 22,224/13,394 & - & Content polluters and humans \\ \hline \# & Total Accounts & 46,295/30,572 & 121,648/32,528 & 6363/5891 & 174,306/68,991 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Outline of collected datasets. Columns **H**, **B** and **C** refer to Humans, Bots and Cyborgs, respectively. The actual data are those presented as **Av**. Only **40%** of the total shared labeled accounts are still available in Twitter. It is obvious, that most bot accounts are inactive, while humans and cyborgs continue their online activity. This fact, points out a problem that has been discussed a lot by other researchers during the last years. Thus, the availability and credibility of up-to-date data has become a major issue and since the evolving nature of bots renders this data out of date, there is a strong need to constantly update and open bot-relevant databases. There have been various efforts from researchers to provide up-to-date labeled data, such as DeBot [50] which supposedly offers an API and access to lists of users that have been deleted by Twitter after their detection, or Botometer [5; 8; 42] which provides a public API which evaluates the possibility of an account being a bot. Unfortunately, such services are either no longer functional or highly criticized, thus they do not offer solid ground to build on [6; 55]. Here, we will only use publicly available shared data that have been used by other researchers, and which have been filtered in such a way that only include accounts that have not been either suspended or deleted. Unlike most other previous work, which use data that are no longer available, we follow a more realistic approach, based on the truly and actually available data.