You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Social Live Stream Services (SLSS) exploit a new level of social interaction. One of the main challenges in these services is how to detect and prevent deviant behaviors that violate community guidelines. In this work, we focus on adult content production and consumption in two widely used SLSS, namely Live.me and Loops Live, which have millions of users producing massive amounts of video content on a daily basis. We use a pre-trained deep learning model to identify broadcasters of adult content. Our results indicate that moderation systems in place are highly ineffective in suspending the accounts of such users. We create two large datasets by crawling the social graphs of these platforms, which we analyze to identify characterizing traits of adult content producers and consumers, and discover interesting patterns of relationships among them, evident in both networks.

Cited Abstract(s):
[1]: With smartphones making video recording easier than ever, new apps like Periscope and Meerkat brought personalized interactive video streaming to millions. With a touch, viewers can switch between first person perspectives across the globe, and interact in real-time with broadcasters. Unlike traditional video streaming, these services require low-latency video delivery to support high interactivity between broadcasters and audiences. We perform a detailed analysis into the design and performance of Periscope, the most popular personal livestreaming service with 20 million users. Using detailed measurements of Periscope (3 months, 19M streams, 705M views) and Meerkat (1 month, 164K streams, 3.8M views), we ask the critical question: Can personalized livestreams continue to scale, while allowing their audiences to experience desired levels of interactivity?' We analyze the network path of each stream and break down components of its end-to-end delay. We find that much of each stream's delay is the direct result of decisions to improve scalability, from chunking video sequences to selective polling for reduced server load. Our results show a strong link between volume of broadcasts and stream delivery latency. Finally, we discovered a critical security flaw during our study, and shared it along with a scalable solution with Periscope and Meerkat management.
Contrary to other popular SLSS like Periscope [1] , all the broadcasts in LM and LL are public. All active broadcasts are visible on a global public list. In both platforms, the concept of re-sharing re-posting broadcasted content across different users is not present. Nevertheless, users are able to get shareable links to live shows that can be used for promoting broadcasters on other social media.