# Fame for sale: efficient detection of fake Twitter followers

arXiv: https://arxiv.org/abs/1509.04098 · HTML: https://mlsanity.com/paper/1509.04098/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2015.

## Built on by (most cited first, 30 of 33)

- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go](https://mlsanity.com/paper/2103.00153/) (2021)
- [The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention](https://mlsanity.com/paper/2307.11864/) (2023)
- [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)
- [BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency](https://mlsanity.com/paper/2208.08320/) (2022)
- [Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection](https://mlsanity.com/paper/2301.07015/) (2023)
- [Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning](https://mlsanity.com/paper/2202.12382/) (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)
- [RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection](https://mlsanity.com/paper/2304.08239/) (2023)
- [MulBot: Unsupervised Bot Detection Based on Multivariate Time Series](https://mlsanity.com/paper/2209.10361/) (2022)
- [How to Use Graph Data in the Wild to Help Graph Anomaly Detection?](https://mlsanity.com/paper/2506.04190/) (2025)
- [Prioritizing Original News on Facebook](https://mlsanity.com/paper/2102.08465/) (2021)
- [Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective](https://mlsanity.com/paper/2307.01968/) (2023)
- [CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks](https://mlsanity.com/paper/2308.04961/) (2023)
- [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)
- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)
- [Stealthy Dual-Trigger Backdoors: Attacking Prompt Tuning in LM-Empowered Graph Foundation Models](https://mlsanity.com/paper/2510.14470/) (2025)
- [Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection](https://mlsanity.com/paper/2602.00318/) (2026)
- [RABot: Reinforcement-Guided Graph Augmentation for Imbalanced and Noisy Social Bot Detection](https://mlsanity.com/paper/2602.21749/) (2026)
- [Beyond Individual Mimicry: Constructing Human-Like Social network with Graph-Augmented LLM Agents](https://mlsanity.com/paper/2605.12512/) (2026)
- [Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19](https://mlsanity.com/paper/2108.10791/) (2021)
- [BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline](https://mlsanity.com/paper/2306.00037/) (2023)
- [The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset](https://mlsanity.com/paper/2308.15154/) (2023)
- [BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference](https://mlsanity.com/paper/2503.03775/) (2025)
- [A Comparative Analysis of Transformer Models in Social Bot Detection](https://mlsanity.com/paper/2509.14936/) (2025)
- [RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection](https://mlsanity.com/paper/2510.23648/) (2025)

## Abstract (arXiv)

textitFake followers are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere - hence impacting on economy, politics, and society. In this paper, we contribute along different dimensions. First, we review some of the most relevant existing features and rules (proposed by Academia and Media) for anomalous Twitter accounts detection. Second, we create a baseline dataset of verified human and fake follower accounts. Such baseline dataset is publicly available to the scientific community. Then, we exploit the baseline dataset to train a set of machine-learning classifiers built over the reviewed rules and features. Our results show that most of the rules proposed by Media provide unsatisfactory performance in revealing fake followers, while features proposed in the past by Academia for spam detection provide good results. Building on the most promising features, we revise the classifiers both in terms of reduction of overfitting and cost for gathering the data needed to compute the features. The final result is a novel textitClass A classifier, general enough to thwart overfitting, lightweight thanks to the usage of the less costly features, and still able to correctly classify more than 95% of the accounts of the original training set. We ultimately perform an information fusion-based sensitivity analysis, to assess the global sensitivity of each of the features employed by the classifier. The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the novel issue of fake Twitter followers.