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This research explores the impacts of content-generating devices (mobile phones versus personal computers) and content features (social content and achievement content) on consumer engagement with marketer-generated content (MGC) on social media. It also examines these factors' interaction effects on consumer engagement.

The study analyzed MGC that 210 companies had posted to Sina Weibo over three years, testing the study’s proposed model with negative binomial regression analysis.

The study's results show that MGC generated via mobile phones attracts more consumer engagement than MGC generated via personal computers. MGC with more social features attracts more consumer engagement, whereas MGC with more achievement features reduces consumer engagement. The authors also found that MGC with more social features generated via mobile phones and MGC with more achievement features generated via personal computers lead to more consumer engagement due to the congruency of the construal level of psychological distance.

This research enriches the literature by exploring the effects of content-generating devices and content features on consumer engagement in the MGC context, which extends the research on consumer engagement with social media from the context of user-generated content to the MGC.

Social shopping platforms have flourished by using multiple social shopping features, yet little is known about how the combination of these features affects purchase intention, particularly in terms of the product itself. The purpose of the paper is to draw on the concept of social shopping feature richness, adopting a formative approach on the survey used, and endeavors to reveal the concept's impact on consumers' buying intention from a product perspective.

Building on mental accounting and signaling theories, a theoretical model is proposed and empirically evaluated with 356 samples collected using a questionnaire survey.

The results suggest that social shopping feature richness promotes consumers' consumption by providing information signals to satisfy acquisition utility and transaction utility. Specifically, social shopping feature richness enhances perceived product quality, while decreasing negative perceptions regarding price. Moreover, perceived product quality and perceived price significantly influence buying intention through the mechanism of perceived value.

The authors' study highlights the role of the combination of functionally diverse social shopping features on product sales for social shopping platforms.

As convenience and anonymity, people with mental illness are increasingly willing to communicate and share information through social media platforms to receive emotional and spiritual support. The purpose of this paper is to identify the degree of depression based on people's behavioral patterns and discussion content on the Internet.

Based on the previous studies on depression, the severity of depression is divided into four categories: no significant depressive symptoms, mild MDD, moderate MDD and severe MDD, and defined each of them. Next, in order to automatically identify the severity, the authors proposed social media digital cues to identify the severity of depression, which include textual lexical features, depressive language features and social behavioral features. Finally, the authors evaluate a system that is developed based on social media digital cues in the experiment using social media data.

The social media digital cues including textual lexical features, depressive language features and social behavioral features (F1, F2 and F3) is the relatively best one to classify four different levels of depression.

This paper innovatively proposes a social media data-based framework (SMDF) to identify and predict different degrees of depression through social media digital cues and evaluates the accuracy of the detection through social media data, providing useful attempts for the identification and intervention of depression.

Dashang refers to a reward given voluntarily to street performers in return for their performance. Some social media platforms have created a way to integrate this as a function, referred to as the dashang feature, to allow users to reward live performers online as well. Over the last few years, this function has become extremely popular among social media users, as it recreates the nostalgic experience of watching street performances. Platforms now consider it indispensable, as it has become a source of substantial revenue (commission on rewards earned by performers). However, not all users reward performers. For each user who pays, there are many more who lurk on the platform. This study examines the reasons for these differences using the Big Five personality perspective and justice theory.

We develop an empirical model using the Big Five theory and justice theory and test it using empirical data collected through a survey of WeChat users.

The results indicate that distributive justice, interpersonal justice and informational justice are essential factors in relation to social media users' use of the dashang feature. It is also found that personality type affects these three factors.

This study makes three key contributions. First, it examines the factors that influence users' voluntary use of the dashang feature using the lenses of the Big Five theory and justice theory. Second, this study extends previous results on perceived justice to examine use of the dashang feature in social media. Third, this study applies these theories to the study of consumer behavior by exploring the role of user characteristics in social media use.

The purpose of this study is to reveal the role of individual–social–technology fit in online social network (OSN) value development. The social software features (e.g. communication and interaction), social features (e.g. privacy and trust) and individual features (e.g. sense of belonging and self-disclosure) are considered fitting forms to describe the OSN value. Implications and suggestions are addressed.

The literature review on social software, the social and individual characteristics and the research gap with respect to OSN value is presented. The research arguments are then hypothesized, and research model used to describe the proposed role is examined empirically. The research targeted mobile phone users as the subjects, and the extent of the activities of these users on OSN for both work and studies. A salient investigation explores the moderation effect of gender. The research results are obtained, and the findings are revealed on the basis of 468 social software users.

The significant effect of individual–social–technology fit on OSN value development is presented through the satisfaction of both participation and sharing information, and knowledge about this fit is verified. The interplay of social software, social and individual features contributes significantly to individual–social–technology fit development, implying that OSN value development is not a single issue. OSN value development should be considered concurrently with technological, personal and social issues.

The empirical study confirms that fitness analysis produces a systematic outcome, in which all elements (e.g. social, technology and individual) are required to cooperate with one another to maximize the OSN value. An individual adopts online channels to communicate with others; thus, the benefits may be a multidimensional issue instead of only a single information service issue. They also consider building an equal social relationship to be important, as it enables diverse propositions, maintains acceptable privacy and behaves on faith to enhance the fit of technology features and individual features to value development. The subjects also likely accepted the fact that emotion generation is important for the advantage of fit of technology features and social features, thereby likely benefitting OSN value development.

The OSN does not only add new values to the society but also brings new effects on social development, especially in terms of social cognition from virtual community formation, development and creation. Although existing studies in the literature present the important aspects and antecedents linked significantly to OSN value development, these studies also insufficiently discuss the effect of fit of these facets on OSN value development. This exploratory study mainly aims to propose and examine the individual–social–technology fit model through an empirical investigation. The main argument of the study is that when a positive and healthy virtual society is developed through social software, the individual and social characteristics, as well as the social software features, should be defined with a suitable fit to promote the social networking value.

The purpose of this paper is to report on a comparative and analytical examination of ten social tagging systems' interfaces and their features and functionalities. The specific objective of the study was to examine the ways in which the user interfaces of social tagging systems encourage and provide users with features to assign, explore, browse and make use of tags during their interaction with social tagging sites.

The user interface features and functionalities of ten social tagging sites (six social bookmarking and four social media sharing sites) are examined. A categorisation of tag‐related features is developed for analysis. The sites are selected based on such criteria as popularity, variety of site type, and inclusion of tagging features and content type.

The findings of this study show that there is an emerging interface design paradigm with respect to social tagging sites that reflects a particular focus on exploratory search and browsing features and services. Some of the key areas discussed are: user tagging features; exploratory and tag browsing features; and interface layout.

The findings of this study of the user interface features of social tagging sites provide a comprehensive picture of the possible and potential features that can be incorporated into new social tagging systems. Based on the evidence found in the examined social tagging interfaces, recommendations are made on the design of tag posting, tag use, tag browsing, tag lists and tag clouds. The design recommendations offer ideas for the development of more sophisticated exploratory and interactive user interfaces for social tagging systems.

This is the first paper that reports on a comparative and exploratory examination of social tagging user interface features and functionalities.

Despite the significance of online communication and interactions, previous research has not systematically compared all features on a single platform from the users' perspective. This study aims to fill this gap by extensively reviewing the current literature on social media affordances and proposes and tests a feature-centric and affordance-based conceptualization of social media platforms (SMPs) between users, features, the audience and content.

This research surveys users on Facebook, one of the largest SMPs, and asks them to assess 20 features of Facebook on six relational affordances between users, features, audience and content. The data in this study were collected on Amazon Mechanical Turk (MTurk) with participants from the US Correspondence analysis was employed to examine the relationship between affordances and the ties among affordances, features and outcomes.

Results of the study indicate that users perceive features differently, and employing features as the unit of analysis captures users' interactions effectively. The findings support the presence of user-oriented affordances, such as presentation flexibility, association and content association. These three affordances can be summarized in two higher-level ones: self-expression and connection (SEC) and persona-linked content (PLC). Our findings of the two dimensions, SEC and PLC, highlight the importance of targets and their connections in understanding social media interactions' dynamic nature.

By proposing to shift the focus from platforms to features, this study suggests that companies should focus on understanding the features they use for their users to interact with their brand, rather than merely ensuring that their company is omnipresent on all platforms. This study underlines the need to focus on features that will help managers influence interpersonal and user-brand communications and interactions on social media.

This research is the first to put features at the center of its investigation and quantitatively examine the relationship between social media features and affordances in a social media context. In all, this research provides a new unit of analysis that is more suitable for researchers to build a robust conceptual foundation for affordances. We believe that conceptualizing audience and content as outcomes, distinguishing it from features and creating connections between them as affordances is the unique aspect of our conceptualization.

The purpose of this study is to develop and test a theoretical model that accounts for an individual's we-intention to continue playing a mobile multiplayer game.

Drawing on habit-intention and habit formation theories, this study conceptualizes social play habit as a determinant of the we-intention to continue playing and identifies its antecedents. The proposed model was tested through a survey of 277 players of Honor of Kings, a popular mobile multiplayer game.

The results indicate that developing social play habit is critical to the formation of a we-intention to continue playing in the context of mobile multiplayer games. The results also suggest that technological (social features embedded in the game) and individual (desire for co-play and privacy concerns) factors jointly influence social play habit.

This study contributes to the literature on we-intention by conceptualizing social play habit and verifying its role in facilitating a shared intention to continue playing mobile multiplayer games. Our work responds to the call for understanding the mechanism by which multiple people form a shared intention to continue using an information technology at a collective level. Our findings provide significant insights into the design of information technologies for collaboration.

This study is among the first to extend the literature on gaming habits by considering other players' involvement. Specifically, our study shifts researchers' attention from gaming habits characterized by individual properties to social gaming habits characterized by communal properties.

The proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the collective Internet agenda. Unfortunately, as bot creators use more sophisticated approaches to avoid being discovered, it has become increasingly difficult to distinguish social bots from legitimate users. Therefore, this paper proposes a novel social bot detection mechanism to adapt to new and different kinds of bots.

This paper proposes a research framework to enhance the generalization of social bot detection from two dimensions: feature extraction and detection approaches. First, 36 features are extracted from four views for social bot detection. Then, this paper analyzes the feature contribution in different kinds of social bots, and the features with stronger generalization are proposed. Finally, this paper introduces outlier detection approaches to enhance the ever-changing social bot detection.

The experimental results show that the more important features can be more effectively generalized to different social bot detection tasks. Compared with the traditional binary-class classifier, the proposed outlier detection approaches can better adapt to the ever-changing social bots with a performance of 89.23 per cent measured using the F1 score.

Based on the visual interpretation of the feature contribution, the features with stronger generalization in different detection tasks are found. The outlier detection approaches are first introduced to enhance the detection of ever-changing social bots.