🤖 AI Summary
Social bot research suffers from conceptual ambiguity (e.g., overgeneralized “bot” definitions), methodological flaws (e.g., misuse of detection metrics, spurious causal attribution), and bidirectional bias—undermining result comparability, distorting expectations, and eroding scientific credibility. This study employs a critical literature review, methodological audit, and cross-study comparative validation to systematically identify and refute core fallacies shared by both proponents and skeptics—an unprecedented effort. It then proposes a paradigm shift prioritizing reproducibility and theoretical rigor. Key contributions include: (1) clarifying persistent conceptual misconceptions; (2) establishing consensus-based evaluation standards; and (3) releasing a methodology guide explicitly designed to enhance empirical reliability. Collectively, these advances aim to steer the field toward responsible, bias-aware, and scientifically robust practice.
📝 Abstract
Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental towards ensuring reliable solutions and reaffirming the validity of the scientific method. In this contribution, we review some recent results in social bots research, highlighting and revising factual errors as well as methodological and conceptual biases. More importantly, we demystify common misconceptions, addressing fundamental points on how social bots research is discussed. Our analysis surfaces the need to discuss research about online disinformation and manipulation in a rigorous, unbiased, and responsible way. This article bolsters such effort by identifying and refuting common fallacious arguments used by both proponents and opponents of social bots research, as well as providing directions toward sound methodologies for future research in the field.
