# HOFA: Twitter Bot Detection with Homophily-Oriented Augmentation and Frequency Adaptive Attention

arXiv: https://arxiv.org/abs/2306.12870 · HTML: https://mlsanity.com/paper/2306.12870/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2023.

## Built on by (most cited first, 7 of 7)

- [The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges](https://mlsanity.com/paper/2407.09618/) (2024)
- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective](https://mlsanity.com/paper/2307.01968/) (2023)
- [A Graph Neural Architecture Search Approach for Identifying Bots in Social Media](https://mlsanity.com/paper/2411.16285/) (2024)
- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)
- [Social Media Bot Detection using Dropout-GAN](https://mlsanity.com/paper/2311.05079/) (2023)
- [RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection](https://mlsanity.com/paper/2510.23648/) (2025)

## Abstract (arXiv)

Twitter bot detection has become an increasingly important and challenging task to combat online misinformation, facilitate social content moderation, and safeguard the integrity of social platforms. Though existing graph-based Twitter bot detection methods achieved state-of-the-art performance, they are all based on the homophily assumption, which assumes users with the same label are more likely to be connected, making it easy for Twitter bots to disguise themselves by following a large number of genuine users. To address this issue, we proposed HOFA, a novel graph-based Twitter bot detection framework that combats the heterophilous disguise challenge with a homophily-oriented graph augmentation module (Homo-Aug) and a frequency adaptive attention module (FaAt). Specifically, the Homo-Aug extracts user representations and computes a k-NN graph using an MLP and improves Twitter's homophily by injecting the k-NN graph. For the FaAt, we propose an attention mechanism that adaptively serves as a low-pass filter along a homophilic edge and a high-pass filter along a heterophilic edge, preventing user features from being over-smoothed by their neighborhood. We also introduce a weight guidance loss to guide the frequency adaptive attention module. Our experiments demonstrate that HOFA achieves state-of-the-art performance on three widely-acknowledged Twitter bot detection benchmarks, which significantly outperforms vanilla graph-based bot detection techniques and strong heterophilic baselines. Furthermore, extensive studies confirm the effectiveness of our Homo-Aug and FaAt module, and HOFA's ability to demystify the heterophilous disguise challenge.