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.
@cite_26: We conducted a mixed methods study of the use of the Meerkat and Periscope apps for live streaming video and audio broadcasts from a mobile device. We crowdsourced a task to describe the content, setting, and other characteristics of 767 live streams. We also interviewed 20 frequent streamers to explore their motivations and experiences. Together, the data provide a snapshot of early live streaming use practices. We found a diverse range of activities broadcast, which interviewees said were used to build their personal brand. They described live streaming as providing an authentic, unedited view into their lives. They liked how the interaction with viewers shaped the content of their stream. We found some evidence for multiple live streams from the same event, which represent an opportunity for multiple perspectives on events of shared public interest.
@cite_8: 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.
@cite_28: Live video streaming from mobile devices is quickly becoming popular through services such as Periscope, Meerkat, and Facebook Live. Little is known, however, about how such services tackle the challenges of the live mobile streaming scenario. This work addresses such gap by investigating in details the characteristics of the Periscope service. A large number of publicly available streams have been captured and analyzed in depth, in particular studying the characteristics of the encoded streams and the communication evolution over time. Such an investigation allows to get an insight into key performance parameters such as bandwidth, latency, buffer levels and freezes, as well as the limits and strategies adopted by Periscope to deal with this challenging application scenario.
@cite_21: Mobile personal livecast (MPL) services are emerging and have received great attention recently. Unlike traditional livecast services with commercial content providers (e.g., live TV), the live contents in MPL are crowdsourced from and consumed among geo-distributed individuals. Although there exist typical social relationships in MPL (i.e., follower-followee), different from conventional social networking services like Twitter and Facebook, which have much of a tolerance for interaction delay, the interactions in MPL must be in real-time. These unique characteristics intrigue us to investigate how the relationships (e.g., social links and geo-locations) between viewers and broadcasters influence the user behaviors, which has yet to be explored in depth. In this paper, we carry out extensive measurements of Inke, one of the most popular MPL providers, with a large-scale dataset containing 11M users. Our key findings are as follows. First, compared with traditional livecast services, the user interests shift much more frequently and the average viewing duration is considerably shorter in MPL. Second, the existence of social relationships significantly strengthens viewer stickiness---followers dedicating longer viewing time (contributing 81 of the total viewing time) and being 2x more patient when suffering poor network connectivity than non-followers. Third, most of the broadcasts in MPL are geographically local-popular (the majority of the views come from the same region of the broadcaster). Based on these critical observations, we provide insights that can enhance the MPL system design from the perspectives of efficient resource allocation and envision a future MPL framework that collaboratively utilizes the cloud and edge computing resources to improve efficiency and scalability for Inke-like services.
@cite_31: 
@cite_10: 
Since SLSS are quite new, the literature in the field is rather limited. Some of these studies investigate the performance of such services, e.g., Meerkat and Periscope @cite_8 @cite_10 , Periscope @cite_28 and Twitch @cite_31 . Human factors and user experience were studied in @cite_26 . Having access to a large dataset of Inke, a Chinese SLSS, @cite_21 identified several patterns in the users, e.g., fast interest shifts, user dedication to broadcasters as well as the locality bonds between users.