Research on bot detection on online social networks has received a considerable amount of attention in Swedish news media. Recently however, criticism of the research field of bot detection on onlinesocial networks has been presented, highlighting the need to investigate the research field to determine if information based on flawed research has been spread. To investigate the research field, this study has attempted to review the process of bot detection on online social networks and evaluate the proposed criticism of current bot detection research by: conducting a literature review of bots on online social networks, conducting a literature review of methods for bot detection on online social networks, and detecting bots in three different politically associated data sets with Swedish Twitter accounts usingfive different bot detection methods. Results of the study showed minor evidence that previous research may have been flawed. Still, based on the literature review of bot detection methods, it was determined that this criticism was not extensive enough to critique the research fieldof bot detection on online social networks as a whole. Further, problems highlighted in the criticism were recognized to potentially have arose from a lack of differentiation between bot types in research. An insufficient differentiation between bot types in research was also acknowledged as a factor which could lead to difficulties ingeneralizing the results from bot detection studies measuring the effect of bots on political opinions. Instead, the study acknowledged that a good bot differentiation could potentially improve bot detection.

abstract: Social media bot detection has been a signature challenge in recent years in online social networks. Many scholars agree that the bot detection problem has become an "arms race" between malicious actors, who seek to create bots to influence opinion on these networks, and the social media platforms to remove these accounts. Despite this acknowledged issue, bot presence continues to remain on social media networks. So, it has now become necessary to monitor different bots over time to identify changes in their activities or domain. Since monitoring individual accounts is not feasible, because the bots may get suspended or deleted, bots should be observed in smaller groups, based on their characteristics, as types. Yet, most of the existing research on social media bot detection is focused on labeling bot accounts by only distinguishing them from human accounts and may ignore differences between individual bot accounts. The consideration of these bots' types may be the best solution for researchers and social media companies alike as it is in both of their best interests to study these types separately. However, up until this point, bot categorization has only been theorized or done manually. Thus, the goal of this research is to automate this process of grouping bots by their respective types. To accomplish this goal, the author experimentally demonstrates that it is possible to use unsupervised machine learning to categorize bots into types based on the proposed typology by creating an aggregated dataset, subsequent to determining that the accounts within are bots, and utilizing an existing typology for bots. Having the ability to differentiate between types of bots automatically will allow social media experts to analyze bot activity, from a new perspective, on a more granular level. This way, researchers can identify patterns related to a given bot type's behaviors over time and determine if certain detection methods are more viable for that type. / Dissertation/Thesis / Presentation Materials for Thesis Defense / Masters Thesis Computer Science 2019

With more political and social discourse taking place online, particularly on social media, theorists have started labeling digital communicative realms as “online public spheres.” However, with the modern public sphere comes modern challenges to political communication; a core antagonist of which is political bots. Political bots are automated accounts that produce content and interact with individuals on political topics on social networks. In this thesis, I analyzed the presence of political bots on Twitter during the 2022 Swedish general election, and by examining the content posted by the bots, I investigated whether they jeopardized democratic functions of the online public sphere by publishing misrepresentation (i.e., artificially increasing the popularity of political actors and political ideas). The analysis uncovered significant bot presence during the 2022 Swedish general election; more than one-fifth of all election-related content was produced by bots, ~90% of which produced misrepresentation. I concluded that political bots jeopardized democratic functions of the online public sphere during the 2022 Swedish general election.