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.
In SLSS users are able to stream their own live shows in real time as broadcasters, and to join the live shows of other users as viewers audience. The audience is able to interact with the streamers through a chat and reward them with virtual rewards, e.g., points, gifts, badges (some of which are purchasable), or money. Also, various SLSS give broadcasters the opportunity to monetize part of the virtual gifts they receive from the audience during their brαodcasts. Users of SLSS employ their own mobile devices (e.g. smartphones, tablets) or their PCs and webcams for broadcasting. In contrast to other social media, SLSS are mostly synchronous @cite_4 @cite_27 , but they can also support asynchronous interactions between users, like direct messages and comments on broadcast video replays.
@cite_27 @cite_4
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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 There has been explosive growth in Mobile Personal Livestreaming (MPL) market since 2016. MPL services are booming not only because they introduce the popular live content by spontaneous and personalized broadcasters, but also because they are deliberately designed to be the innovative social networking service (SNS) platforms. The latter is a very important aspect that distinguishes MPL from the traditional livestreaming services. In this paper, we study the social networking of a large scale MPL service “Inke” (with more than 200 million registered users, 15 million daily active users) in China. By analyzing the dataset we crawl and the features of Inke app, we show that the social media stickiness of Inke comes from three aspects: the follower-followee model, the virtual-gift-based incentive mechanism, and the multi-perspective interactivity between broadcasters and viewers. First, Inke introduces the follower-followee model rather than the traditional broadcaster-viewer model, and every user in Inke can be a broadcaster. This makes MPL have some different patterns from both the traditional livestreaming serives and SNS platforms. Second, Inke use virtual gift giving and user ranking as its incentive mechanism. Our measurement results show that this mechanism can indeed enhance user stickiness. Furthermore, Inke incorporates a variety of features during broadcasting to strengthen interactivity. The insight we gain in this paper has important implications for both existing and future designs. With the advances in personal computing devices and the prevalence of broadband network and wireless mobile network accesses, end-users are no longer pure content consumers, but contributors, too. In today's crowdsourced streaming systems, numerous broadcasters lively stream their video content, e.g., live events or online game scenes, to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are geo-distributed globally and highly heterogeneous in terms of the generated video quality and the network system configurations. The scalability and heterogeneity challenges therefore lie on both broadcasters and the viewers, which call for massive transcoding, and two critical issues: 1) choosing video representation set that maximizes viewer satisfaction and 2) allocating computational resources that minimize operational costs, must be systematically optimized in the global scale. In this paper, we present a generic framework utilizing the powerful and elastic cloud computing services for crowdsourced live streaming with heterogeneous broadcasters and viewers. We jointly consider the viewer satisfaction and the service availability pricing of geo-distributed cloud resources for transcoding. We develop an optimal scheduler for allocating cloud instances with no regional constraints. We then extend the solution to accommodate regional constraints, and discuss a series of practical enhancements, including popularity forecasting, initialization latency, and viewer feedbacks. Our solutions have been evaluated under diverse networks and cloud system configurations as well as parameter settings. The trace-driven simulation confirms the superiority of our design, while our Planetlab-based experiment offers further practical hints toward real-world migration.