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.
Legal and ethical questions about SLSS were raised by @cite_16 . Recently, @cite_3 performed an empirical study on law infringements in several SLSS. While the focus was not on adult content, the researchers found that around 17.9
@cite_16 @cite_3
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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. This national survey of youth, ages 10 to 17, and their caretakers has several implications for the current debate about young people and Internet pornography. Twenty five percent of youth had unwanted exposure to sexual pictures on the Internet in the past year, challenging the prevalent assumption that the problem is primarily about young people motivated to actively seek out pornography. Most youth had no negative reactions to their unwanted exposure, but one quarter said they were very or extremely upset, suggesting a priority need for more research on and interventions directed toward such negative effects. The use of filtering and blocking software was associated with a modest reduction in unwanted exposure, suggesting that it may help but is far from foolproof. Various forms of parental supervision were not associated with any reduction in exposure. The authors urge that social scientific research be undertaken to inform this highly contentious public policy controversy. This article explores the operation of warrants, connections between online and real-world identities, on deceptive behavior in computer-mediated communication. A survey of 132 participants assessed three types of warrants (the use of a real name, a photo, and the presence of real-world acquaintances) in five different media: IM, Forums, Chat, Social Networking Sites (SNS) and Email. The effect of warrants on lies about demographic information (e.g., age, gender, education, etc.), one's interests (e.g., religion, music preferences, etc.), and the seriousness of lies was assessed. Overall, deception was observed most frequently in Chat and least often in SNS and Email. The relationship between warrants and deception was negative and linear, with warrants suppressing the frequency and seriousness of deception regardless of medium, although real-world acquaintances were especially powerful in constraining deception in SNS and emails. Since the textual contents on online social media are highly unstructured, informal, and often misspelled, existing research on message-level offensive language detection cannot accurately detect offensive content. Meanwhile, user-level offensiveness detection seems a more feasible approach but it is an under researched area. To bridge this gap, we propose the Lexical Syntactic Feature (LSF) architecture to detect offensive content and identify potential offensive users in social media. We distinguish the contribution of pejoratives profanities and obscenities in determining offensive content, and introduce hand-authoring syntactic rules in identifying name-calling harassments. In particular, we incorporate a user's writing style, structure and specific cyber bullying content as features to predict the user's potentiality to send out offensive content. Results from experiments showed that our LSF framework performed significantly better than existing methods in offensive content detection. It achieves precision of 98.24 and recall of 94.34 in sentence offensive detection, as well as precision of 77.9 and recall of 77.8 in user offensive detection. Meanwhile, the processing speed of LSF is approximately 10msec per sentence, suggesting the potential for effective deployment in social media.