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

TL;DR

This paper introduces TIES, a deep learning model that captures both static and dynamic social interactions to detect malicious activities and enhance Facebook's platform integrity at scale.

Contribution

The paper presents TIES, a novel supervised deep learning model that unifies static and dynamic social interaction features for integrity protection in social media.

Findings

TIES effectively detects rogue social interactions.

TIES helps prevent misinformation and fake accounts.

TIES reduces ad payment risks.

Abstract

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.