# Unmasking Social Bots: How Confident Are We?

arXiv: https://arxiv.org/abs/2407.13929 · HTML: https://mlsanity.com/paper/2407.13929/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 61 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 24 of 24)

- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Multiplicative Normalizing Flows for Variational Bayesian Neural Networks](https://mlsanity.com/paper/1703.01961/) (2017)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Online Human-Bot Interactions: Detection, Estimation, and Characterization](https://mlsanity.com/paper/1703.03107/) (2017)
- [Scalable and Generalizable Social Bot Detection through Data Selection](https://mlsanity.com/paper/1911.09179/) (2019)
- [The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race](https://mlsanity.com/paper/1701.03017/) (2017)
- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [A Decade of Social Bot Detection](https://mlsanity.com/paper/2007.03604/) (2020)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [Anatomy of an AI-powered malicious social botnet](https://mlsanity.com/paper/2307.16336/) (2023)
- [Detection of Novel Social Bots by Ensembles of Specialized Classifiers](https://mlsanity.com/paper/2006.06867/) (2020)
- [RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter](https://mlsanity.com/paper/1902.04506/) (2019)
- [Botometer 101: Social bot practicum for computational social scientists](https://mlsanity.com/paper/2201.01608/) (2022)
- [Arming the public with artificial intelligence to counter social bots](https://mlsanity.com/paper/1901.00912/) (2019)
- [LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection](https://mlsanity.com/paper/2306.17408/) (2023)
- [Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal](https://mlsanity.com/paper/2104.10635/) (2021)
- [Demystifying Misconceptions in Social Bots Research](https://mlsanity.com/paper/2303.17251/) (2023)
- [Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter](https://mlsanity.com/paper/1804.04406/) (2018)
- [ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering](https://mlsanity.com/paper/2310.02913/) (2023)
- [A General Language for Modeling Social Media Account Behavior](https://mlsanity.com/paper/2211.00639/) (2022)
- [UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples](https://mlsanity.com/paper/2204.09502/) (2022)

## Built on by (most cited first, 2 of 2)

- [Uncertainty Quantification with Bayesian Higher Order ReLU KANs](https://mlsanity.com/paper/2410.01687/) (2024)
- [Characterising Behavioural Families and Dynamics of Promotional Twitter Bots via Sequence-Based Modelling](https://mlsanity.com/paper/2512.18077/) (2025)

## Abstract (arXiv)

Social bots remain a major vector for spreading disinformation on social media and a menace to the public. Despite the progress made in developing multiple sophisticated social bot detection algorithms and tools, bot detection remains a challenging, unsolved problem that is fraught with uncertainty due to the heterogeneity of bot behaviors, training data, and detection algorithms. Detection models often disagree on whether to label the same account as bot or human-controlled. However, they do not provide any measure of uncertainty to indicate how much we should trust their results. We propose to address both bot detection and the quantification of uncertainty at the account level - a novel feature of this research. This dual focus is crucial as it allows us to leverage additional information related to the quantified uncertainty of each prediction, thereby enhancing decision-making and improving the reliability of bot classifications. Specifically, our approach facilitates targeted interventions for bots when predictions are made with high confidence and suggests caution (e.g., gathering more data) when predictions are uncertain.