# Bot Datasets on Twitter: Analysis and Challenges
## 3 Database Analysis
### Cresci et al. Database (2017)
#### 3.3.2 Analysis  
[PERSON] et al.'s [34] 2015 database (Section 3.2) is the only one with relationship information, so we focused our analysis of the next three databases on the discovery of patterns, behaviors, and differences between the classes using the available features for that database. We present our analysis of [PERSON] et al.'s [35] 2017 database in the following paragraphs.  
We started with the comparison of the datasets selected (genuine accounts and social spambots #1) and discovered that even when genuine users outnumber fake ones by 2483 accounts, they have similar percentages in some user features. Therefore, a copycat phenomenon is observed happening in some of them, from the fake accounts to the real accounts.  
We also encountered some unique features that can help identify bot accounts in a significant way, especially in our observation of tweet features. From profile information, we selected four features that showed a significant difference between classes for the partitions: the number of times an account appears in a public list (_listed_count_), its public position (_location_), the number of tweets it has published (_statuses_count_), and its time zone (_time_zone_). Regarding tweet information, we used the following four: the number of URLs inside tweets (_num_urls_), from where it is published (_source_), the number of mentions appearing in the tweet (_num_mentions_), and the number of times it was retweeted (_retweet_count_). We base our selection process on analyzing each user and tweet feature's PFD for both classes (human and bots). If we find that the categories or their associated frequency are significantly different, we add them to our initial selection. We then narrow this list to the features with the most contrasting PFD between classes. We performed the same three non-parametric tests described in Section 3.2.2 for these four tweet features and obtained the same results as before. Therefore, we assume we are working with a representative sample of the total tweets.  
From the graphs designed in Section 3.3.1, we applied eight distinct partitions using these features extracted from profile and tweet information to help visualize differences between humans and social spambots. The resulting partitions expose an automatic behavior of social spambots, which we detail in our summary of findings in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Key Findings** \\ \hline _listed\_count_ & Half of the social spambots do not appear on public lists compared to one-quarter of the genuine accounts. \\ _location_ & Most of the bots are from Italy as they are retweeters of an Italian politician, contrary to genuine accounts that are scattered throughout the world. \\ _statuses\_count_ & More than 15\% of bot accounts have a similar number of tweets with increments of 0.10\% in their PFD [49]. \\ _time\_zone_ & The time zone of the bots is mostly from Europe, which agrees with their reported location. \\ _num\_urls_ & About 6\% of bot tweets have at least one URL, compared to 15\% of human tweets. \\ _source_ & The genuine accounts usually utilize iPhone to tweet, while bot accounts diversify between many devices. \\ _num\_mentions_ & 97\% of the bots’ tweets do not mention other users, different to humans with about 40\%. \\ _retweet\_count_ & Three-quarters of the bots’ tweets have no retweets, while humans’ tweets have more than half. \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of our findings when analyzing our graph partitions of the chosen user (first four) and tweet (last four) features from the genuine accounts dataset and the social spambots #1 dataset of [PERSON] et al.’s [35] 2017 database.