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.
Contrary to other popular SLSS like Periscope @cite_8 , 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.
@cite_8
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Over the past few years, the popularity of Social Live Streaming Services (SLSSs) like Periscope, Ustream, or YouNow, has been on the rise. The services offer their users the opportunity to interact with the viewers in real time while broadcasting themselves. With this kind of human-computer interaction legal dangers are a possibility. We performed an empirical investigation on law infringements on YouNow, Periscope, and Ustream. To this end, a content analysis of live-streams was applied. We developed a codebook based on literature regarding the usage of social media and on the conducted observations of streams. Based upon the most restrictive law (German law), researchers defined categories on potential law infringements while using SLSSs which were the following: copyright violations regarding music, video and picture, violation of personality rights with an additional focus on data protection and insults, committed road traffic acts, the violations of the sports broadcasting rights, as well as violations of the Health Insurance Portability and Accountability Act. In a time span of four weeks we observed 7,621 streams from SLSSs in Japan, Germany, and the U.S. We further examined if there are differences regarding law infringements between age groups, genders, motivations, contents, countries, and platforms. In the last few years, a new type of synchronous social networking services (SNSs) has emerged—social live streaming services (SLSSs). Studying SLSSs is a new and exciting research field in information science. What information behaviors do users of live streaming platforms exhibit? In our empirical study we analyzed information production behavior (i.e., broadcasting) as well as information reception behavior (watching streams and commenting on them). We conducted two quantitative investigations, namely an online survey with YouNow users (N 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. 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.