FLOCK: Combating Astroturfing on Livestreaming Platforms

TL;DR

This paper introduces FLOCK, an unsupervised method to detect viewbot fraud in livestreaming platforms, achieving high precision and recall on large-scale real-world data, thereby addressing a critical challenge in maintaining platform integrity.

Contribution

The paper is the first to formalize and characterize viewbot fraud in livestreaming, proposing an effective unsupervised detection approach called FLOCK that scales to large datasets.

Findings

FLOCK achieves over 98% precision in identifying botted broadcasts.

FLOCK attains over 90% precision and recall on large synthetic attack datasets.

The method is deployed in a major livestreaming platform, demonstrating practical effectiveness.

Abstract

Livestreaming platforms have become increasingly popular in recent years as a means of sharing and advertising creative content. Popular content streamers who attract large viewership to their live broadcasts can earn a living by means of ad revenue, donations and channel subscriptions. Unfortunately, this incentivized popularity has simultaneously resulted in incentive for fraudsters to provide services to astroturf, or artificially inflate viewership metrics by providing fake "live" views to customers. Our work provides a number of major contributions: (a) formulation: we are the first to introduce and characterize the viewbot fraud problem in livestreaming platforms, (b) methodology: we propose FLOCK, a principled and unsupervised method which efficiently and effectively identifies botted broadcasts and their constituent botted views, and (c) practicality: our approach achieves over 98% precision in identifying botted broadcasts and over 90% precision/recall against sizable synthetically generated viewbot attacks on a real-world livestreaming workload of over 16 million views and 92 thousand broadcasts. FLOCK successfully operates on larger datasets in practice and is regularly used at a large, undisclosed livestreaming corporation.