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The purpose of this paper is to explore the factors influencing the encountering information adoption of virtual live streaming from the perspective of the immersion experience. In addition, the paper aims to provide new theoretical perspectives and analytical frameworks for virtual live information behavior.

Based on a review of relevant literature and theories, a model of the encountering information adoption of virtual live streaming users is constructed. In order to complete the empirical study, two experiments and questionnaires have been designed to investigate the relationship between high and low immersion experiences. A total of 1,332 valid survey samples were collected and analyzed, utilizing the structural equation model. In order to delineate the regimes, Gradient Boosted Regression Tree (GBRT) and Lasso regression were further utilized.

The research findings indicate that users' immersion experience in virtual live streaming has a positive effect on perceived usefulness, trust, and commitment. Furthermore, perceived usefulness and trust have a positive effect on users' emotional arousal and enhance the content experience, while commitment has a negative effect on the content experience. The emotional arousal and content experience of users contribute to their encountering information adoption. The effect of immersion experience on encountering information adoption is partially mediated by perceived usefulness, trust, commitment, emotional arousal, and content experience. The relationship between content experience and encountering information adoption is moderated by digital literacy to a significant extent. In the context of virtual live streaming, the factors influencing users' encountering information adoption can be divided into three distinct regimes. The most significant factors affecting encounter information adoption are trust and commitment, which are located in the first regime. Emotional arousal and digital literacy are situated in the third regime, with the least significant influence on encountering information adoption.

This study constructs a model of virtual live streaming users' encountering information adoption and explores the formation mechanism of encountering information adoption from the perspective of immersion experience, which provides a new perspective for further understanding the influence of virtual live-streaming users' encountering information adoption.

The paper examines the content moderation practices and related public disclosures of the World's most popular social media organizations (SMOs). It seeks to understand how content moderation operates as a process of accountability to shape and inform how users (inter)act on social media and how SMOs account for these practices.

Content analysis of the content moderation practices for selected SMOs was conducted using a range of publicly available data. Drawing on seminal accountability studies and the concepts of hierarchical and holistic accountability, the authors investigate the design and appearance of the systems of accountability that seek to guide how users create and share content on social media.

The paper unpacks the four-stage process of content moderation enacted by the World's largest SMOs. The findings suggest that while social media accountability may allow SMOs to control the content shared on their platforms, it may struggle to condition user behavior. This argument is built around the limitations the authors found in the way performance expectations are communicated to users, the nature of the dialogue that manifests between SMOs and users who are “held to account”, and the metrics drawn upon to determine the effectiveness of SMOs content moderation activities.

This is the first paper to examine the content moderation practices of the World's largest SMOs. Doing so extends understanding of the forms of accountability that function in the digital space. Crucial future research opportunities are highlighted to provoke and guide debate in this research area of escalating importance.

This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs.

This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets.

Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics.

This study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data.

This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc.

This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.

A social media user behavior model is presented as a function of different user types, i.e. light and heavy users. The users’ behaviors are analyzed in terms of knowledge creation, framing and targeting.

Data consisting of 160,000 tweets by nearly 40,000 twitter users in the city of Newark (NJ, USA) were collected during the year 2014. An analysis was conducted to examine the hypothesis that different user types exhibit distinct behaviors driven from different motivations.

There are three important findings of this study. First, light users reuse existing content more often, while heavy and automated users create original content more often. Light users also use more sentiments than the heavy and automated users. Second, automated users frame more than heavy users, who frame more than light users. Third, light users tend to target a specific audience, while heavy and automated users broadcast to a general audience.

Decision-makers can use this study to improve communication with their customers (the public) and allocate resources more effectively for better public services. For example, they can better identify subsets of users and then share and track specialized content to these subsets more effectively.

Despite the broad interest, there is insufficient research on many aspects of social media use, and very limited empirical research examining the relevance and impact of social media within the public sector. The social media user behavior model was established as a framework that can provide explanations for different social media knowledge behaviors exhibited by various subsets of users, in an e-government context.

The manuscript aims to introduce the managerial practice of content recycling – that is, a firm's recycling of its posts on social media platforms. I define and distinguish the phenomenon from related ones and offer propositions for future research to test empirically.

Review of the practitioner literature, in-situ observations with content managers, and a survey of content managers and Facebook users.

Managers recycle their posts to recoup the costs of content. Under some conditions, recycled content may yield more benefits than costs.

I define the phenomenon of content recycling and differentiate it from related terms. I offer propositions for future research.

I inform managers of the benefits and costs of recycling content and conditions under which benefits may override costs.

The research is novel and helps develop a common managerial practice.

The integration of social media and e-commerce has resulted in a rising phenomenon among individual content providers (ICPs), who used to offer free content, to provide consumers with paid content, such as online courses, Q&As or consultations. Despite the prevalence of ICPs’ content monetization, empirical research has rarely studied its underlying mechanism. This paper examines how the characteristics of free content contributed by ICPs on social media platforms influence their paid content sales, focusing on the perspective of human brand.

The empirical setting is an online knowledge exchange platform, where users are allowed to provide free content (e.g. answers) on the social media platform and launch paid content (e.g. lectures) on the e-commerce platform. A machine learning technique is employed to construct measures for the characteristics of free content, and fixed-effects estimation is presented to confirm which factors have a significant influence on the sales of paid content.

The empirical results show that the quality, diversity and expertness of free content have a significant positive impact on the sales of the ICP-paid content, with the brand popularity of ICP playing a mediating role.

This study is the first attempt to demystify the relationship between content contribution and ICPs’ content monetization from the perspective of human brand. The findings validate the effectiveness of the “Selling by Contribution” strategy and provide valuable insights for ICPs and social media platforms.

Today, individuals use social media to express their opinions and feelings, which offers a living laboratory to researchers in various fields, such as management, innovation, technology development, environment and marketing. It is therefore necessary to understand how the language used in user-generated content and the emotions conveyed by the content affect responses from other social media users.

In this study, almost 700,000 posts from Twitter (as well as Facebook, Instagram and forums in the appendix) are used to test a conceptual model grounded in signaling theory to explain how the language of user-generated content on social media influences how other users respond to that communication.

Extending developments in linguistics, this study shows that users react negatively to content that uses self-inclusive language. This study also shows how emotional content characteristics moderate this relationship. The additional information provided indicates that while most of the findings are replicated, some results differ across social media platforms, which deserves users' attention.

This article extends research on Internet behavior and social media use by providing insights into how the relationship between self-inclusive language and emotions affects user responses to user-generated content. Furthermore, this study provides actionable guidance for researchers interested in capturing phenomena through the social media landscape.

This paper aims to identify, evaluate and integrate the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. To achieve this aim, the authors use systematic literature review (SLR) as a powerful method to collect and critically analyze the research papers. Also, the authors discuss the selected recommender systems and its main techniques, as well as their benefits and drawbacks in general.

In this paper, the SLR method is utilized with the aim of identifying, evaluating and integrating the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. Also, the authors discussed recommender system and its techniques in general without a specific domain.

The major developments in categories of recommender systems are reviewed, and new challenges are outlined. Furthermore, insights on the identification of open issues and guidelines for future research are provided. Also, this paper presents the systematical analysis of the recommender system literature from 2005. The authors identified 536 papers, which were reduced to 51 primary studies through the paper selection process.

This survey will directly support academics and practical professionals in their understanding of developments in recommender systems and its techniques.

This paper aims to look at shifts in internet-related content and services economies, from audience labour economies to Web 2.0 user-generated content, and the emerging model of user computing power utilisation, powered by blockchain technologies. The authors look at and test three models of user computing power utilisation based on distributed computing (Coinhive, Cryptotab and Gridcoin) two of which use cryptocurrency mining through distributed pool mining techniques, while the third is based on distributed computing of calculations for scientific research. The three models promise benefits to their users, which the authors discuss throughout the paper, studying how they interplay with the three levels of the digital divide.

The goal of this article is twofold as follows: first to discuss how using the mining hype may reduce digital inequalities, and secondly to demonstrate how these services offer a new business model based on value rewarding in exchange for computational power, which would allow more online opportunities for people, and thus reduce digital inequalities. Finally, this contribution discusses and proposes a method for a fair revenue model for content and online service providers that uses user device computing resources or computational power, rather than their data and attention. The method is represented by a model that allows for consensual use of user computing resources in exchange for accessing content and using software tools and services, acting essentially as an alternative online business model.

Allowing users to convert their devices’ computational power into value, whether through access to services or content or receiving cryptocurrency and payments in return for providing services or content or direct computational powers, contributes to bridging digital divides, even at fairly small levels. Secondly, the advent of blockchain technologies is shifting power relations between end-users and content developers and service providers and is a necessity for the decentralisation of internet and internet services.

The article studies the effect of services that rely on distributed computing and mining on digital inequalities, by looking at three different case studies – Coinhive, Gridcoin and Cryptotab – that promise to provide value in return for using computing resources. The article discusses how these services may reduce digital inequalities by affecting the three levels of the digital divide, namely, access to information and communication technologies (ICTs) (first level), skills and motivations in using ICTs (second level) and capacities in using ICTs to get concrete benefits (third level).

Social media have attracted attention as an information channel for content generated in heterogeneous internet services. Focusing on social media platforms, the purpose of this paper is to examine the factors behind social transmission with content crossover from other services through hypertext link (URL). The authors investigate the effects of source influence and peer referrals on diffusion outcome and address their variations in the case of content crossover.

The authors use a Poisson regression model due to the discrete nature of the dependent variable. The authors conduct an empirical study using 233 million real transaction data generated by 1,203,196 Korean users of Twitter.

Source influence and peer referral have a positive impact on cascade size in the content dissemination process. In the case of content crossover, the impact of source influence decreases. However, the impact of peer referrals increases in the process of external content dissemination.

The authors demonstrate source and peer effects on content diffusion and that these effects vary when shared content is linked from an external service by a URL.

The findings indicate that firms that wish to diffuse information through social media or enter the social media with new services to provide new ways of creating and sharing content should understand the nature of the social transmission process.

Given the growing popularity of social media, particularly SNSs with online social networks as information channels, the authors first consider online social transmission as a user-driven diffusion process. Based on social factors in the diffusion process, the authors derive source and peer effects on the social transmission process.