# User to Video: A Model for Spammer Detection Inspired by Video Classification Technology

arXiv: https://arxiv.org/abs/2510.06233 · HTML: https://mlsanity.com/paper/2510.06233/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [CED: Credible Early Detection of Social Media Rumors](https://mlsanity.com/paper/1811.04175/) (2018)
- [Robust Spammer Detection by Nash Reinforcement Learning](https://mlsanity.com/paper/2006.06069/) (2020)
- [CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation](https://mlsanity.com/paper/2406.14830/) (2024)
- [Modeling User Behavior With Interaction Networks for Spam Detection](https://mlsanity.com/paper/2207.10767/) (2022)

## Abstract (arXiv)

This article is inspired by video classification technology. If the user behavior subspace is viewed as a frame image, consecutive frame images are viewed as a video. Following this novel idea, a model for spammer detection based on user videoization, called UVSD, is proposed. Firstly, a user2piexl algorithm for user pixelization is proposed. Considering the adversarial behavior of user stances, the user is viewed as a pixel, and the stance is quantified as the pixel's RGB. Secondly, a behavior2image algorithm is proposed for transforming user behavior subspace into frame images. Low-rank dense vectorization of subspace user relations is performed using representation learning, while cutting and diffusion algorithms are introduced to complete the frame imageization. Finally, user behavior videos are constructed based on temporal features. Subsequently, a video classification algorithm is combined to identify the spammers. Experiments using publicly available datasets, i.e., WEIBO and TWITTER, show an advantage of the UVSD model over state-of-the-art methods.