# Bot Datasets on Twitter: Analysis and Challenges
## 3 Database Analysis
### [PERSON] et al. Database (2015)
#### 3.2.2 Analysis
Next, we used Gephi 0.9.2 [30] to create the categories for each chosen feature using its data along with the percentage of users or tweets belonging to that category. Then, we picked unique colors to distinguish the nine categories with the most percentages, having the tenth color (gray) for the sum of the others. Finally, we colored the nodes of each dataset's user or tweet graph based on the category they fell in. From the user features, we have chosen the number of times the account has given a favorite (_favourites_count_) and the number of followings (friends) they have (_friends_count_) from the available user features. In the case of the tweet features, we selected the number of times it is retweeted (_retweet_count_) and the device where the tweet originated (_source_) for partitioning. We present a summary of our findings in Table 1.  
The second part of the analysis begins with the estimation of the in-degree (incoming connections) and out-degree (outgoing connections) of our graphs created before from following and follower connections (Section 3.2.1). In these graphs, a node represents a user from the dataset, and a directed edge a connection between two nodes, made either because the original user is following another user or another user is a follower of the original user. We colored the nodes based on their number of relationships, following a color gradient for a smoother transition: purple is assigned for the ones with fewest relationships, pink for fewer, red for few, orange for many, yellow for more, and green for the nodes with the most relationships. The resulting graphs for the followers of both datasets are available in Figure 2.  
Next, we calculated graph metrics [54] for these graphs. For the followers' graphs (Figure 2), the average degree for dataset TFP is 1.3, and for dataset FSF is 1.1. The diameter of the graph for the TFP dataset and the FSF dataset is 10.0 and 4.0, respectively. The average path length for the TFP dataset is 3.8 and 1.1 for the FSF dataset. For the followings' graphs, the average degree we obtained is 1.7 for the TFP dataset and 20.1 for the FSF dataset. The diameter of the graph calculated is 12.0 and 3.0 for the TFP dataset and the FSF dataset, respectively. The average path length estimated for the TFP dataset is 4.3 and 1.0 for the FSF dataset. \begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Key Findings** \\ \hline _frowites\_count_ & 94\% of bots gave no favorite since created, which is drastically different from humans, which have 20\%. \\ _friends\_count_ & The average of bots’ followings is around 210, and several of them follow each other. \\ _retweet\_count_ & There is a retweeting behavior for a small group of bot accounts that could be the work of a botnet. \\ _source_ & Almost all of the bots’ tweets come from the web, compared to the diversity of sources of humans’ tweets. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of our findings when analyzing the graph partitions of our chosen user (first two) and tweet (last two) features from the TFP dataset (certified human accounts) and the FSF dataset (fake accounts bought online) of [PERSON] et al.’s [34] 2015 database. Figure 2: Graph coloring using in-degree (follower relationships) for the TFP dataset (**left**) and the FSF dataset (**right**), both from [PERSON] et al.’s [34] 2015 database. It follows a color gradient, starting with purple for nodes with the fewest relationships and ending with green for most. Bots (**right**) have more high-connected nodes (red color) than humans (**left**), which have more low-connected nodes (purple color). From all these results, we observe that fake accounts (FSF dataset) are slightly less popular and influential than real accounts (TFP dataset), as the average degree in their followers' graph is smaller than the TFP dataset. However, fake (bot) accounts are less distanced from each other than real (human) accounts, which have a greater diameter and average path length.