TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook - CrossMinds.ai
TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook
Aug 13, 2020368 views
Nima Noorshams
Abstract: 
Since its inception, Facebook has become an integral part of the,online social community. People rely on Facebook to connect with,others and build communities. As a result, it is paramount to protect,the integrity of such a large 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 Facebook platform’s integrity.

Authors: Nima Noorshams, Saurabh Verma, Aude Hofleitner @ Facebook Core Data Science Team

Paper URL: https://research.fb.com/wp-content/uploads/2020/08/TIES-Temporal-Interaction-Embeddings-For-Enhancing-Social-Media-Integrity-At-Facebook.pdf
SIGKDD_2020
Applied Research
Recommended