# TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook

arXiv: https://arxiv.org/abs/2002.07917 · HTML: https://mlsanity.com/paper/2002.07917/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models](https://mlsanity.com/paper/2109.06777/) (2021)
- [Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms](https://mlsanity.com/paper/2211.06516/) (2022)
- [ConTIG: Continuous Representation Learning on Temporal Interaction Graphs](https://mlsanity.com/paper/2110.06088/) (2021)
- [SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis](https://mlsanity.com/paper/2104.00764/) (2021)
- [Adversarial machine learning for protecting against online manipulation](https://mlsanity.com/paper/2111.12034/) (2021)
- [Preemptive Detection of Fake Accounts on Social Networks via Multi-Class Preferential Attachment Classifiers](https://mlsanity.com/paper/2308.05353/) (2023)
- [Learning User Embeddings from Temporal Social Media Data: A Survey](https://mlsanity.com/paper/2105.07996/) (2021)
- [ROBAD: Robust Adversary-aware Local-Global Attended Bad Actor Detection Sequential Model](https://mlsanity.com/paper/2507.15067/) (2025)

## Abstract (arXiv)

Since its inception, Facebook has become an integral part of the online social community. People rely on Facebook to make connections with others and build communities. As a result, it is paramount to protect the integrity of such a rapidly growing network in a fast and scalable manner. In this paper, we present our efforts to protect various social media entities at Facebook from people who try to abuse our platform. We present a novel Temporal Interaction EmbeddingS (TIES) model that is designed to capture rogue social interactions and flag them for further suitable actions. TIES is a supervised, deep learning, production ready model at Facebook-scale networks. Prior works on integrity problems are mostly focused on capturing either only static or certain dynamic features of social entities. In contrast, TIES can capture both these variant behaviors in a unified model owing to the recent strides made in the domains of graph embedding and deep sequential pattern learning. To show the real-world impact of TIES, we present a few applications especially for preventing spread of misinformation, fake account detection, and reducing ads payment risks in order to enhance the platform's integrity.