# The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race

arXiv: https://arxiv.org/abs/1701.03017 · HTML: https://mlsanity.com/paper/1701.03017/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2017.

## Built on by (most cited first, 30 of 49)

- [Detecting Propaganda Techniques in Memes](https://mlsanity.com/paper/2109.08013/) (2021)
- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search](https://mlsanity.com/paper/2206.06757/) (2022)
- [LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection](https://mlsanity.com/paper/2306.17408/) (2023)
- [MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark](https://mlsanity.com/paper/2301.01123/) (2023)
- [Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection](https://mlsanity.com/paper/2301.07015/) (2023)
- [BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning](https://mlsanity.com/paper/2304.03144/) (2023)
- [BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns](https://mlsanity.com/paper/2303.10214/) (2023)
- [Online User Profiling to Detect Social Bots on Twitter](https://mlsanity.com/paper/2203.05966/) (2022)
- [Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection](https://mlsanity.com/paper/2302.06900/) (2023)
- [Demystifying Misconceptions in Social Bots Research](https://mlsanity.com/paper/2303.17251/) (2023)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Cognitive network science for understanding online social cognitions: A brief review](https://mlsanity.com/paper/2102.12799/) (2021)
- [HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features](https://mlsanity.com/paper/2105.11602/) (2021)
- [RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection](https://mlsanity.com/paper/2304.08239/) (2023)
- [Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2110.10655/) (2021)
- [Adversarial machine learning for protecting against online manipulation](https://mlsanity.com/paper/2111.12034/) (2021)
- [Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study](https://mlsanity.com/paper/2112.04913/) (2021)
- [MulBot: Unsupervised Bot Detection Based on Multivariate Time Series](https://mlsanity.com/paper/2209.10361/) (2022)
- [Prioritizing Original News on Facebook](https://mlsanity.com/paper/2102.08465/) (2021)
- [Detection of Propaganda Techniques in Visuo-Lingual Metaphor in Memes](https://mlsanity.com/paper/2205.02937/) (2022)
- [From Online Behaviours to Images: A Novel Approach to Social Bot Detection](https://mlsanity.com/paper/2304.07535/) (2023)
- [Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective](https://mlsanity.com/paper/2307.01968/) (2023)
- [Multimodal Detection of Bots on X (Twitter) using Transformers](https://mlsanity.com/paper/2308.14484/) (2023)
- [Unmasking Social Bots: How Confident Are We?](https://mlsanity.com/paper/2407.13929/) (2024)
- [On the efficacy of old features for the detection of new bots](https://mlsanity.com/paper/2506.19635/) (2025)
- [RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction](https://mlsanity.com/paper/2510.16035/) (2025)
- [FakeSwarm: Improving Fake News Detection with Swarming Characteristics](https://mlsanity.com/paper/2305.19194/) (2023)

## Abstract (arXiv)

Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitter's capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study.