# Bot Datasets on Twitter: Analysis and Challenges
## 3 Database Analysis
### [PERSON] et al. Database (2019)
#### 3.5.2 Analysis  
For our analysis, we first made a comparison between the three datasets to discover important behaviors or patterns of the classes (human, bot, and politician). We separated the original set of 109 features proposed in [33] that mixed user, tweet, sentiment analysis, and usage frequency information into three categories: the user-related features, the tweet-related features, and the sentiment analysis and usage frequency-related features.  
From our comparison of the first category, we discovered that features related to the date of creation prove a systematic generation of bot accounts, as more than 15% of the total share the same hour, day, month, or year. They also tend to leave their user description empty, have zero favorites issued, or do not appear in public lists, similarly to the observed in the previous databases analyzed.  
In the second category, tweet-related features, we observe a similar situation in other databases analyzed: tweets from bots do not have geolocation, replies, hashtags, or retweets. The reason for this situation is that they are a mix of traditional and social spambots from [PERSON] et al.'s [35] 2017 database and thus share similar characteristics.  
The features related to sentiment analysis from our third category, being novel and inviting, reveal new information: the profile descriptions of bots are less objective than their human counterparts. Unfortunately, a sentiment analysis of tweets does not offer a clear distinction between humans and bots that speak English, as they express their feelings in a similar way. Meanwhile, frequency features disclose a schedule-oriented nature of the bot class for posting: they tweet more from 2 p.m. to 8 p.m. but not so much on Sundays, and have the least inactive users through the week of all three classes.  
After this comparison, we selected eight features to create distinct partitions that will help us differentiate between classes using the graph structures of the previous section. For determining the features that contrast the accounts the most, we performed the same selection process as in the previous databases starting with a manual inspection of each one, and then compared their PFD [49] for the three classes.  
From the user-related features available, we selected these three: its language (_lang_), the times it appears in public lists (_listed_count_), and its current place (_location_). We also chose the following three from all the tweet-related features available: the medium used for posting (_source_), how many retweets it has (_retweet_count_), and the number of mentions in the tweet (_num_mentions_). Finally, from the sentiment analysis and usage frequency features, we picked two: the frequency of posting on Sunday (_sunday_tweetdist_) and the score obtained in the sentiment analysis, ranging from very negative to very positive (_sa_score_tag_).  
From the resulting partitions, we identified similar behaviors of the bot class with those of the other databases, but also some notable differences with the Spanish-speaking politician accounts. We present our findings summarized in Table 4 and the tweet partition of the sentiment analysis feature _sa_score_tag_ in Figure 3.