Unpacking the Social Media Bot: A Typology to Guide Research and Policy (1801.06863v2)

Abstract: Amidst widespread reports of digital influence operations during major elections, policymakers, scholars, and journalists have become increasingly interested in the political impact of social media 'bots.' Most recently, platform companies like Facebook and Twitter have been summoned to testify about bots as part of investigations into digitally-enabled foreign manipulation during the 2016 US Presidential election. Facing mounting pressure from both the public and from legislators, these companies have been instructed to crack down on apparently malicious bot accounts. But as this article demonstrates, since the earliest writings on bots in the 1990s, there has been substantial confusion as to exactly what a 'bot' is and what exactly a bot does. We argue that multiple forms of ambiguity are responsible for much of the complexity underlying contemporary bot-related policy, and that before successful policy interventions can be formulated, a more comprehensive understanding of bots --- especially how they are defined and measured --- will be needed. In this article, we provide a history and typology of different types of bots, provide clear guidelines to better categorize political automation and unpack the impact that it can have on contemporary technology policy, and outline the main challenges and ambiguities that will face both researchers and legislators concerned with bots in the future.

Unpacking the Social Media Bot: A Typology to Guide Research and Policy

The academic paper by Gorwa and Guilbeault offers a critical examination of the notion of social media bots, underscoring the definitional ambiguities and complexities that have hampered effective policy interventions in the field of digital influence operations. This paper comes at a pertinent time when social media platforms are increasingly scrutinized for their role in enabling political manipulation via automated accounts, notably highlighted by their involvement in the 2016 US Presidential election. The authors attempt to provide a comprehensive framework for categorizing and understanding the different forms of bots in order to aid scholarly inquiry and inform policy-making.

Key Contributions

The paper delineates the challenges faced in bot conceptualization and proposes a typological framework based on three considerations: structure, function, and use. The authors meticulously dissect various bot forms, from chatbots and spambots to social bots, sock puppets, and cyborgs, emphasizing that each type presents unique features and implications. By doing so, they illuminate past and current uses of these automated agents across different platforms and contexts.

1. Typological Clarification: The authors provide a history and development of bots, starting from early computing applications to their current manifestations in social media and cybersecurity. By mapping out their evolution, they offer insight into the diverse functions bots can perform, whether as web crawlers, facilitators of spam, or as political tools in social networks.
2. Structural, Functional, and Ethical Framework: The proposed framework aids in understanding bots beyond mere operational capacity by focusing on ethical implications, such as the societal impact of political bots. This approach resonates with broader issues of content moderation and platform governance, extending to the normative judgments of positive versus negative bot use.
3. Policy Implications: The paper addresses the absence of adequate bot-related policy interventions and highlights contemporary concerns, including data access limitations for research, technical challenges in bot detection, and the misalignment between corporate incentives and public interest. Notably, the authors underscore the urgent need for more precise definitions and typologies to craft effective regulatory measures.

Implications and Future Directions

The research underscores the necessity for improved conceptual clarity and measurement accuracy in bot detection, advocating for collaborative efforts between academics, policymakers, and technology companies. Practically, this calls for enhanced transparency in technology governance and a reevaluation of platform policies that may inadvertently stifle beneficial automation. The paper’s findings are especially relevant for developing nuanced regulatory frameworks that distinguish between legitimate bot applications and those employed for malicious purposes.

Theoretically, the paper contributes to a deeper understanding of automated agents and their intersection with political processes, potentially paving the way for expanded discourse on platform accountability and ethical digital practices. Future explorations could delve into machine learning advancements for bot classification, explore the implications of emerging regulatory norms, and address the socio-political ramifications of automated influences.

In conclusion, Gorwa and Guilbeault’s paper provides a significant scholarly resource for unpacking the complexities surrounding social media bots. Through their typology and framework, they lay the groundwork for richer academic inquiry and informed policy design that could effectively counteract the challenges posed by political automation in digital communication platforms.