Table 1 A sample of the data with Tweets and usernames anonymized.
Related Figures (10)
Figure 1. The data-driven detection process for the social bots. The total number of tweets is given as: Table 2. Distribution of Tweets by language. Table 3. Distribution of Tweets over the observation period. Figure 2. Monthly tweets between October 2015 and September 2016: (a) lognormal distribution curve and (b) lognormal P-P plot. Figure 3. Total tweet distribution according to days of the week. Figure 4. The top ten Twitter users with the highest number of twitter posts, as well as the number of hashtags, URLs, and retweets. Figure 5. User 1’s frequency, with total tweets per minute over a one-hour period. Figure 4 shows that Users 1 and 2 are the highest-ranking tweeters, and they were among the users identified via the application of methods one (1), two (2), and three (3). Further investigations on the regularity component and the number of tweets sent by these accounts resulted from this. Several tweets for each second as well as 21 tweets in 10 s indicate that the user is self-aware, as shown in Figure 4. Based on the overall 90,783 active accounts, User 1 and User 2 were categorized as bots or cyborgs since they generated roughly 4.57 percent of the 490,449 Twitter posts. Figure 5 depicts the tweeting habits of User 1 and shows a consistent pattern in its tweeting behavior over a 1-h period, with noticeable spikes at 5-min intervals. Hootsuite, a Twitter automation tool, was used mainly by User 1 for regularly tweeting various pieces of material. On 21 October 2015 at 10:53 a.m., the tweeting behavior of User 2, as seen in Figure 6, shows numerous tweets per second for eight consecutive seconds within 10 s. The horizontal axis shows time in the hour:minute:seconds format, while the vertical axis reflects the volume of tweets in that period. The results indicate a high activity level, particularly within 10 s and a minimum of two tweets per second in this dataset. User 2 tweeted using the IFTTT protocol. This is a frequently used technique for both building and generating bot applets. Table 4. Results of a descriptive test for total monthly tweets for #FeesMustFall from October 2015 to September 2016. Researchers discovered an unexpected social pattern in the top tweeters’ frequency, volume, and content during the data processing phase that points to automated behavior. Figure 5 shows an example of the tweeting of messages at regular intervals, whereas Figure 6 shows a surge of tweeting activity for a few seconds. Twitter bots and cyborgs Figure 6. Volume of User 2’s Twitter posts in a ten-second period.
