Abstract
The participation of automated software agents known as social bots within online social network (OSN) engagements continues to grow at an immense pace. Choruses of concern speculate as to the impact social bots have within online communications as evidence shows that an increasing number of individuals are turning to OSNs as a primary source for information. This automated interaction proliferation within OSNs has led to the emergence of social bot detection efforts to better understand the extent and behavior of social bots. While rapidly evolving and continually improving, current social bot detection efforts are quite varied in their design and performance characteristics. Therefore, social bot research efforts that rely upon only a single bot detection source will produce very limited results. Our study expands beyond the limitation of current social bot detection research by introducing an ensemble bot detection coverage framework that harnesses the power of multiple detection sources to detect a wider variety of bots within a given OSN corpus of Twitter data. To test this framework, we focused on identifying social bot activity within OSN interactions taking place on Twitter related to the 2018 U.S. Midterm Election by using three available bot detection sources. This approach clearly showed that minimal overlap existed between the bot accounts detected within the same tweet corpus. Our findings suggest that social bot research efforts must incorporate multiple detection sources to account for the variety of social bots operating in OSNs, while incorporating improved or new detection methods to keep pace with the constant evolution of bot complexity.
Highlights
The 2016 U.S presidential election broke traditional campaign communication norms, as legacy institutions such as mainstream media sources and political-party organizations ceded much power and influence to unmediated, Internet-based technological platforms (e.g. online social networks (OSNs), online political blogs) [1]
As the results of the 2015 Defense Advanced Research Projects Agency (DARPA) Twitter Bot Challenge summarized, no single detection algorithm is able to account for the myriad of social bots operating in OSNs [30]
The multi-detection platform comparative analysis of intra-group and cross-group interactions shows that bots detected by DeBot and Bot-hunter persistently engaged humans at rates much higher than bots detected by Botometer
Summary
The 2016 U.S presidential election broke traditional campaign communication norms, as legacy institutions such as mainstream media sources (e.g. print, television and radio) and political-party organizations ceded much power and influence to unmediated, Internet-based technological platforms (e.g. online social networks (OSNs), online political blogs) [1]. As the results of the 2015 Defense Advanced Research Projects Agency (DARPA) Twitter Bot Challenge summarized, no single detection algorithm is able to account for the myriad of social bots operating in OSNs [30]. It is from this perspective that the following study expands current social bot analysis research by incorporating multiple social bot detection services to determine the prevalence and relative importance of social bots within an OSN conversation of tweets. The intra-group and cross-group analysis of the constructed retweet network shows that bots detected by DeBot and Bot-hunter persistently engaged humans at rates much higher (5.03% and 6.09%, respectively) than bots detected by Botometer (2.27%). The Results and Discussion section presents the pertinent findings of the study, and the paper closes with the Conclusion section
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