Pith. sign in

REVIEW 1 cited by

Demystifying Misconceptions in Social Bots Research

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2303.17251 v4 pith:6NTBDVJV submitted 2023-03-30 cs.SI cs.AIcs.CYcs.LG

classification cs.SIcs.AIcs.CYcs.LG
keywords researchsocialbotsmisconceptionscommondiscussissuesmanipulation
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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. Here, we discuss a broad set of consequential methodological and conceptual issues that affect current social bots research, illustrating each with examples drawn from recent studies. 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Graph-based Fake Account Detection: A Survey

    cs.SI 2025-07 conditional novelty 4.0 of 10

    A structured survey of graph-based fake account detection methods, organizing classical, traditional machine learning, and deep learning approaches and their datasets.

Pith tools