# Bot Datasets on Twitter: Analysis and Challenges
## 3 Database Analysis
### [PERSON] et al. Database (2015)
#### 3.2.2 Analysis  
This section aims to discover significant characteristics or behaviors that enable us to differentiate between classes. The analysis of the databases' datasets will also help us recognize distinct behaviors of the bot accounts inside these datasets and identify key challenges of working with databases from the literature of bot detection on Twitter in Sections 4 and 5. In this database, we divided our analysis into two parts: we first observe the differences between features of classes (humans in the TFP dataset and bots in the FSF dataset), which the authors distributed in user's (account) information and tweet's information for each dataset. We then inspect relationships of classes with other accounts, and also divided into user followers' information and user followings' information for each dataset. We started the feature comparison by examining the features extracted from the user's information. Our results exposed a pattern happening on the bot accounts: they leavetheir profile information empty. In particular, we discovered that bot class always has the default settings for both profile picture and background, never uses geolocation or appear in public lists, and has no location nor time zone. Meanwhile, the real (human) class exhibit a different behavior: almost every profile has changed their default picture, but their default background not so much (66%); about half of them have geolocation enabled as well as appearing in public lists. Around 70% have a location and 20% a time zone. Tweet features bring significant contrast between classes as well. Nearly all tweets from the bot class did not receive a favorite, compared to only one-quarter of human tweets. As in the user features, geolocation is a feature that can support adequately distinct classes: no bot has enabled it. None of the tweets published by bots was a reply, barely mentioning other users, and usually without being replied to. Finally, they posted almost all their content via the web (99%), contrary to humans, who used the web for one-third of their tweets, and then they diversify using different devices. To conclude this first part of the analysis, we selected two user and two tweet features (_favourites_count_, _friends_count_, _retweet_count_, and _source_) from all the available as partitions for the graphs previously obtained in Section 3.2.1. We made this choice from our comparison of user features and tweet features, selecting the ones that contrasted the classes better by first analyzing the PFD of each feature for both humans and bots. Then, if the change in the categories or their associated frequency was significant enough, we considered them in our initial selection. Finally, we narrowed this selection to the most contrasting features, i.e., the features having the most different PFD between classes. We repeated this method for the other databases in this work. Obtaining a simple random sample entails demonstrating that our results are valid or correct (consistent with if we had done the same analysis considering the complete dataset). So, we validate that it is representative of the total population when choosing the tweet features by performing three non-parametric tests: the chi-square goodness-of-fit test [52], the chi-square test of homogeneity [52], and the two-sample Kolmogorov-Smirnov test [53]. The null hypothesis (H\({}_{0}\)) for all three tests is that the sample and the actual population follow the same distribution. We used the chosen tweet features as input variables, and the results of the three tests (we obtained \(p\)-values of 0.57, 0.73, and d-stat of 0.003 respectively, with a significance level of 0.05) indicated that there is no sufficient evidence to reject H\({}_{0}\). Given that we followed the guidelines for doing simple random sampling [51] to reduce bias, we believe the previous results allow us to assume that the sample is representative of the total population.