The impact of live chat, streamer presence, and suspense on brand recognition in social live streaming services

Social live streaming services (SLSSs) are social media, which combine Live-TV with elements of Social Networking Services (SNSs). In social media and thus also in SLSSs, the so-called influencer and micro-celebrities play an important role, but to what extend are SLSSs’ streamers motivated by fame or financial gain? We conducted a content analysis in order to investigate SLSSs’ streamers (n = 7,667) on Periscope, Ustream and YouNow in respect to their general characteristics and streaming motivation being fame and financial gain. We have developed a research model referring to the platform used by the streamers, their gender, origin, age and streamed content (general characteristics), as well as the motivational aspects. Streamers of Ustream are mostly motivated by financial gain, whereas YouNow broadcasters seek to be famous. Considering the streamers age, older generations (Gen X, Silver Surfers) aspire after financial gain. With progressing age the motivation to become a star decreases. Mostly streamed content by streamers motivated by money is entertainment media. For streamers wanting to become a star chatting and making music are the preferred content categories.

The interest in social media and in particular the social live streaming services (SLSSs) is increasing, as can be observed by the growing number of users in different age groups. However, the social live streaming services have not been satisfactory investigated yet. Therefore, knowledge gaps in this subject area are still present. This study focuses on the use of SLSSs in terms of content, motivation, and gender depending on the age of the streamer. A research team has been assembled for this purpose. 4,937 streams were analyzed for content, motivation, age and gender on three different platforms in three different countries. Dependencies of content and motivation regarding the streamers age could be determined. The results indicate that older age groups are more likely to share information and therefore broadcast content related to information. It could be observed that younger users are more likely to film their lifestyle on this medium. The genders differ for the age groups significantly. So, it can be assumed that a correlation between age and the content, motivation, and genders on social live streaming services is given.

Social live streaming services (SLSSs) are synchronous social media, which combine Live-TV with elements of Social Networking Services (SNSs) including a backchannel from the viewer to the streamer and among the viewers. Important research questions are: Why do people in their roles as producers, consumers and participants use SLSSs? What are their motives? How do they look for gratifications, and how will they obtain them? The aim of this article is to develop a heuristic theoretical model for the scientific description, analysis and explanation of users’ information behavior on SLSSs in order to gain better understanding of the communication patterns in real-time social media. Our theoretical framework makes use of the classical Lasswell formula of communication, the Uses and Gratifications theory of media usage as well as the Self-Determination theory. Albeit we constructed the model for understanding user behavior on SLSSs it is (with small changes) suitable for all kinds of social media.

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.

Viewing sports online together? Psychological consequences on social live streaming service usage

Social Live Streaming Services (SLSSs) have emerged as a novel distribution channel for mediated sports, necessitating an exploration of their impacts on viewers’ flow experiences. This study integrates flow theory and the limited capacity model to assess the extent to which interactive elements, specifically live chat and streamer presence, contribute to viewers’ flow experiences under different suspense levels. The findings revealed that the live chat positively affected viewers’ flow, while streamer presence had a negative impact. Moreover, under low suspense conditions, both informational and emotional messages were positively correlated with flow, whereas only emotional messages exhibited a positive impact on flow under high suspense conditions. Notably, the negative impact of streamer presence on flow remained consistent regardless of suspense conditions. This study expands existing knowledge on flow within the context of SLSS and provides insights for practitioners seeking to strategically leverage SLSSs as an effective distribution channel for mediated sports.

Social Live Streaming Services (SLSSs) are a new and exciting area of Social Networking Services (SNSs), with Periscope, Ustream, and YouNow representing some of the most commonly used international services. SLSSs offer the opportunity to examine the human-computer interaction between the streamers and their medium, the live stream, as well as the streamers’ information behavior. To get a better understanding on the information behavior on SLSSs and who produces which kind of live streams we conducted a systematic observation of live streams (N = 7,667) in a time-span of four weeks. We implemented a content analysis and investigated if differences between gender and the produced content, as well as the motivation of a person can be observed. Furthermore, the content was analyzed by country (U.S., Germany, and Japan) as well as by the service (Periscope, Ustream, and YouNow) to gain insight into the question if the streamed content depends on the applied services or the cultural background.

BackgroundSeeking COVID-19 information promotes individuals to adopt preventive behaviors, including wearing a mask, social distancing, staying away from risky places, and washing hands. This study aims to investigate which information and sources individuals relied on in seeking COVID-19 information and further examine their roles in individuals’ adoption of preventive behaviors.MethodsThrough a statistical analysis of 1027 valid responses from citizens in different Chinese cities in 2022 to the self-designed items in an online survey, this study identified individuals’ preferred information sources and content on COVID-19. Regarding the information sources and content, the study used multiple regression analysis to examine their associations with individuals’ preventive behaviors, and further applied fuzzy-set qualitative comparative analysis (fsQCA) to explore their configurations that increase the likelihood of individuals adopting preventive behaviors.ResultsIndividuals preferred information about the newest prevention and control policies, precautions and treatment, and symptoms from the sources of workplace and community, social media, and social live streaming services. Additionally, individuals’ preventive behaviors were positively related to the workplace and community (β = 0.202, p <.001), social live streaming services (β = 0.089, p <.01), government department websites (β = 0.079, p <.05), television (β = 0.073, p <.05), and online news media (β = 0.069, p <.05), but were negatively associated with newspapers (β=-0.087, p <.05). Regarding information content, precautions and treatments (β = 0.211, p <.001), the newest prevention and control policies (β = 0.173, p <.001), symptoms (β = 0.152, p <.001), and official rumor-dispelling information (β = 0.082, p <.05) had a positive relationship with individuals’ preventive behaviors. In addition, fsQCA results presented eight configurations that promote individuals to adopt preventive behaviors. The total coverage and solution consistency values were 0.869 and 0.987, respectively. Furthermore, COVID-19 information content, the sources of social media and interpersonal sources, and official news media played an essential role in increasing the likelihood of individuals adopting preventive behaviors.ConclusionsOur findings demonstrated that individuals seek various COVID-19 information from multiple sources. The direct and degree of association of information sources and content with individuals’ preventive behaviors vary from source to source and from content to content. Information sources and content could combinatorially promote individuals to adopt preventive behaviors through several configurations.

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.

The newly emergent social live streaming services (SLSSs) provide the sport consumers with a synchronised and more interactive viewing experience. In order to help the sport SLSSs firms understanding and engaging with the viewers effectively, this research aims to classify the sports SLSS viewers based on their engagement behaviour, and identify the perceived value and value contribution of each group of viewers. Firstly, 52,545 sports SLSSs viewers' viewing duration time is predicted by a feedforward neural network. Second, the predicted viewing duration time and other extracted viewer behavioural data (number of messages, number of virtual gifts, and value of virtual gifts) are analysed through two-step clustering in SPSS, and classified viewers into four types. Semi-structured interviews were then conducted to understand how each type of viewer co-creates value. The results identified four groups of viewers, namely content consumers, super co-creators, co-creators, and tourists, and identified their distinct value co-creations and perceived value. This study sheds light on combining engagement behaviour and value co-creation literature to classify the sports viewers in the context of SLSSs. This understanding assists the decision-making processes of marketers and operators to promote viewers' co-creation effectively.

Social computing has transformed the content industry. Content providers have changed from providing merely content to offering social content experiences that allow users not only to passively consume content but also to actively participate. As the latest development, social live streaming services (SLSS) have emerged that enable so-called co-interactive behaviour, i.e., the live interplay between users while co-experiencing content. Yet, it remains unclear how co-interactive behaviour affects the monetisation of social live content. Based on a data set of 868,940 users from 59 gaming channels of the freemium-based SLSS Twitch, we empirically show that co-interactive behaviour has a stronger effect on users’ willingness to subscribe (WTS) than passive and active behaviour. Since we find that a user’s reciprocated interactions also directly increase other users’ WTS, our results call for dismantling the current dichotomy of user participation and social influences in IS research. We suggest a social-experiential perspective that integrates social influences as an inherent part of users’ participation experiences. This novel interactive second-person perspective contributes to research on user engagement and value capture on social content platforms, and it allows SLSS providers to obtain a more precise picture of user value to better develop or adapt their monetisation strategies.

Gamification is seen as an important factor for people to use different services, this also applies to social live streaming services (SLSSs). In China, there are around 200 SLSSs available, and the most successful of them apply a wide range of gamification elements. The general SLSS YouNow, which is mostly used by the younger generation, is a SLSS with the most applied gamification elements outside of China. However, it remains unclear if and to what extend gamification elements motivate female and male streamers differently. We empirically investigate this question by applying a survey with 94 streamers (female N = 48; male N = 46). The results indicate that YouNow is seen favorable by the streamers, but female streamers have a more positive view of it. Furthermore, male and female streamers are inclined to spend real money to further their motives. Overall, female streamers are more motivated by gamification elements than male streamers. Female streamers prefer Levels, the Progress Bar, Badges; male streamers favor Coins, Gifts, and Levels.

Gamification is seen as an important factor for people to use different services, this also applies to social live streaming services (SLSSs). In China, there are around 200 SLSSs available, and the most successful of them apply a wide range of gamification elements. The general SLSS YouNow, which is mostly used by the younger generation, is a SLSS with the most applied gamification elements outside of China. However, it remains unclear if and to what extend gamification elements motivate female and male streamers differently. We empirically investigate this question by applying a survey with 94 streamers (female N = 48; male N = 46). The results indicate that YouNow is seen favorable by the streamers, but female streamers have a more positive view of it. Furthermore, male and female streamers are inclined to spend real money to further their motives. Overall, female streamers are more motivated by gamification elements than male streamers. Female streamers prefer Levels, the Progress Bar, Badges; male streamers favor Coins, Gifts, and Levels.

Social live video streaming has been a major Internet phenomenon with the rise of platforms like Facebook-Live, Youtube-Live, and Twich. New features tailored by these platforms provide unprecedented opportunities for viewers to engage and for broadcasters to gain tangible rewards through user donation. In this article, we conduct an in-depth analysis of user donations, a distinctive feature and prominent phenomenon in social live streaming, which however we have very limited understandings with. Based on a publicly available (anonymized) dataset with detailed information on over 4 million donation relationships that cover over 2 million users and worth in total over 200 million US dollars, we quantitatively reveal donation disparity and dynamics of donation relationships. Among other results, we find that repeated donation relationships largely exist and the strength significantly increases with the repetition level. Finally, we adopt machine-learned classifiers to accurately predict future donations. Our findings shed lights on the user retention problem and the design of social live video streaming services.

In recent years, the digitization of our everyday life has been an increasingly important issue for researchers and society alike. Digital platforms with online communities have flourished and have helped users enhance their work and social lives (Tiwana et al., 2010; Bharadwaj et al., 2013). Innovative technologies and digital communities have not only introduced new methods of communicating, they have also heavily influenced our social interaction habits (Barrett et al., 2016; Baden-Fuller and Haefliger, 2013).