Employees’ proactiveness on enterprise social media and social consequences: An integrated perspective of social network and social exchange theories

This study delves into how employee proactiveness on enterprise social media can influence social outcomes at work, both positively and negatively. Drawing on social network and social exchange theories, we develop a model to understand how virtual proactiveness is perceived by leaders and coworkers. Using a round-robin design, we collected data from 281 respondents in 56 teams across three waves. The results show that affiliative and challenging proactiveness positively relate to leader-member and team-member exchanges. However, challenging proactiveness negatively affects team-member exchange, potentially leading to coworker envy. These findings shed light on the intricate social dynamics in remote working environments.     

Detecting and tagging users’ social circles in social media

As a media and communication platform, microblog becomes more popular around the world. Most users follow a large number of celebrities and public medias on microblog; however, these celebrities do not necessarily follow all their fans. Such one-way relationship abounds in ego network and is displayed by the forms of users’ followees and followers, which make it difficult to identify users’ real friends who are contained in merged lists of followees and followers. The aim of this paper is to propose a general algorithm for detecting users’ real friends in social media and dividing them into different social circles automatically according to the closeness of their relationships. Then we analyze these social circles and detect social attributes of these social circles. To verify the effectiveness of the proposed algorithm, we build a microblog application which displays algorithm results of social circles for users and enables users to adjust proposed results according to her/his real social circles. We demonstrate that our algorithm is superior to the traditional clustering method in terms of F value and mean average precision. Furthermore, our method of tagging social attributes of social circles gets high performance by NDCG (normalized discounted cumulative gain).     

SocialSearch + : enriching social network with web evidences

This paper introduces the problem of searching for social network accounts, e.g., Twitter accounts, with the rich information available on the Web, e.g., people names, attributes, and relationships to other people. For this purpose, we need to map Twitter accounts with Web entities. However, existing solutions building upon naive textual matching inevitably suffer low precision due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nicknames). To overcome these limitations, we leverage “relational” evidences extracted from the Web corpus. We consider two types of evidence resources—First, web-scale entity relationship graphs, extracted from name co-occurrences crawled from the Web. This co-occurrence relationship can be interpreted as an “implicit” counterpart of Twitter follower relationships. Second, web-scale relational repositories, such as Freebase with complementary strength. Using both textual and relational features obtained from these resources, we learn a ranking function aggregating these features for the accurate ordering of candidate matches. Another key contribution of this paper is to formulate confidence scoring as a separate problem from relevance ranking. A baseline approach is to use the relevance of the top match itself as the confidence score. In contrast, we train a separate classifier, using not only the top relevance score but also various statistical features extracted from the relevance scores of all candidates, and empirically validate that our approach outperforms the baseline approach. We evaluate our proposed system using real-life internet-scale entity-relationship and social network graphs.     

Is the social use of media for seeking connectedness or for avoiding social isolation? Mechanisms underlying media use and subjective well-being

This study provides a resolution for two contrasting hypotheses around media use, the augmentation and the displacement hypotheses. To do this, we conducted an online survey of 300 Korean adults examining the relationships among the social use of media, face-to-face communication, social isolation, connectedness, and subjective well-being. The results indicate that connectedness, not avoiding social isolation, mediates the effects of the social use of media on subjective well-being. On the other hand, both connectedness and avoiding social isolation mediate the effects of face-to-face communication on subjective well-being. These results suggest that the social use of media is limited to seeking connectedness to others, whereas face-to-face communication can facilitate avoiding social isolation as well as seeking connectedness, which can explain why the two contrasting hypothesis, the augmentation and the displacement hypotheses, can be right. In the domain of seeking connectedness, media can augment face-to-face communication. On the other hand, in the domain of avoiding social isolation, media may displace face-to-face communication.     

How demanding is social media: Understanding social media diets as a function of perceived costs and benefits – A rational actor perspective

Using the rational actor perspective as a guiding frame, this exploratory study examined individuals’ social media diet (i.e., amount, frequency, and duration of use) as a function of task load and expected goal attainment. Surveys were distributed (N = 337) focusing on Twitter and Facebook usage for informational and relational purposes, respectfully. Increased task load – conceptualized as a cognitive cost – directly negatively influenced Twitter use but only indirectly influenced Facebook use as a function of perceived benefits. Across conditions, perceived self-efficacy was negatively associated with perceived task load and positively associated with goal attainment, and goal attainment was a significant correlate of increased social media usage. Interpreted, we see that a transparent technology such as Facebook has no cognitive costs associated with its use, while an opaque technology such as Twitter seems to have a salient cognitive cost element. Further, we found that older users of Facebook were more likely to judge the channel as more cognitively demanding and themselves as having lower self-efficacy in using it. Finally, results indicated that for both Facebook and Twitter, males perceived both channels as more cognitively demanding than females. Theoretical and practical explanations and applications for these findings are presented.     

Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection

With the rise of social web, there has also been a great concern about the quality of user-generated content on social media sites (SMSs). Deceptive comments harm users’ trust in online social media and cause financial loss to firms. Previous studies use various features and classification algorithms to detect and filter social spam on several social media platforms. However, to the best of our knowledge, previous studies have not exploited both probabilistic topic modeling and incremental learning to detect social spam on SMSs. Thus, the main contribution of this paper is design of a novel detection methodology that combines topic- and user-based features to improve the effectiveness of social spam detection. The proposed methodology exploits a probabilistic generative model, namely the labeled latent Dirichlet allocation (L-LDA), for mining the latent semantics from user-generated comments, and an incremental learning approach for tackling the changing feature space. An experiment based on a large dataset extracted from YouTube demonstrates the effectiveness of our proposed methodology, which achieves an average accuracy of 91.17 % in social spam detection. Our statistical analysis reveals that topic-based features significantly improve social spam detection, which has significant implications for business practice.     

Enterprise social media and cyber-slacking: A Kahn’s model perspective

This study investigates the associations between enterprise social media (ESM) use, psychological conditions, and cyber-slacking in the workplace and examines the mediating role of psychological conditions between ESM use and cyber-slacking. The study utilizes Kahn’s framework to develop a research model and investigate various associations, and it employs a cross-sectional design, testing the developed research model with 277 ESM users from Chinese enterprises. The study results suggest that ESM use stimulates various psychological conditions that discourage cyber-slacking behaviors and significantly mediate the associations between ESM use and cyber-slacking.     

DRAGON-Lab—Next generation internet technology experiment platform

Testbed technology is very important in the development of the Internet. Similar to the present internet, next generation internet also starts from testbed. There are two kinds of testbeds, testbed networks like CNGI-CERNET2, Internet2, Geant; testbed systems like PlanetLab, NS2. DRAGON-Lab can be viewed as both testbed network and testbed system. DRAGON-Lab is an independent AS （autonomous system） and connected to multiple real networks. On the other hand, DRAGON-Lab integrates many resources of its own, partners＇ and internet＇s, so as to provide open service. DRAGON-Lab has a large scale, provides open service, supports remote visualized experiments and programmable experiments. More details will be introduced in this paper.     

Semantic ambient media—an introduction

The medium is the message! And the message was literacy, media democracy and music charts. Mostly one single distinguishable medium such as TV, the Web, the radio, or books transmitted the message. Now in the age of ubiquitous and pervasive computing, where information flows through a plethora of distributed interlinked media—what is the message ambient media will tell us? What does semantic mean in this context? Which experiences will it open to us? What is content in the age of ambient media? Ambient media are embedded throughout the natural environment of the consumer—in his home, in his car, in restaurants, and on his mobile device. Predominant sample services are smart wallpapers in homes, location based services, RFID based entertainment services for children, or intelligent homes. The goal of this article is to define semantic ambient media and discuss the contributions to the Semantic Ambient Media Experience (SAME) workshop, which was held in conjunction with the ACM Multimedia conference in Vancouver in 2008. The results of the workshop can be found on: www.ambientmediaassociation.org.     

How is the Semantic Web evolving? A dynamic social network perspective

Finding how the Semantic Web has evolved can help understand the status of Semantic Web community and predict the diffusion of the Semantic Web. One of the promising applications of the Semantic Web is the representation of personal profiles using Friend of a Friend (FOAF). A key characteristic of such social networks is their continual change. However, extant analyses of social networks on the Semantic Web are essentially static in that the information about the change of social networks is neglected. To address the limitations, we analyzed the dynamics of a large-scale real-world social network in this paper. Social network ties were extracted from both within and between FOAF documents. The former was based on knows relations between persons, and the latter was based on revision relations. We found that the social network evolves in a speckled fashion, which is highly distributed. The network went through rapid increase in size at an early stage and became stabilized later. By examining the changes of structural properties over time, we identified the evolution patterns of social networks on the Semantic Web and provided evidence for the growth and sustainability of the Semantic Web community.     

What motivates health information exchange in social media? The roles of the social cognitive theory and perceived interactivity

Social media has become an attractive platform for users to exchange health information with others. However, little research has been done to identify the determinants of health information exchange. By integrating aspects of the social cognitive theory and perceived interactivity, this study proposes a research model to investigate the antecedents of health information exchange in social media. Data collected from Facebook users with health information exchange experience were used to examine the proposed model. The results demonstrate that human-to-human interaction, human-to-information interaction, outcome expectation of health self-management competence, and outcome expectation of social relationships have a significant impact on health information exchange behavior. The results also reveal that human-to-human interaction exerts a significant influence on the outcome expectation of health self-management competence and the outcome expectation of social relationships, while human-to-information interaction has a positive effect on the outcome expectation of health self-management competence. The implications for the theory and practice and future research directions are discussed.     

Learning beyond books—strategies for ambient media to improve libraries and collaboration spaces as interfaces for social learning

With the advent of digital media and online information resources, public libraries as physical destinations for information access are being increasingly challenged. As a response, many libraries follow the trend of removing bookshelves in order to provide more floorspace for social interaction and collaboration. Such spaces follow a Commons 2.0 model: they are designed to support collaborative work and social learning. The acquisition of skills and knowledge is facilitated as a result of being surrounded by and interacting with a community of likeminded others. Based on the results of a case study on a Commons 2.0 library space, this paper describes several issues of collaboration and social learning in public library settings. Acknowledging the significance of the architectural characteristics of the physical space, we discuss opportunities for ambient media to better reflect the social attributes of the library as a place; i.e. amplify the sense of other co-present library visitors and provide opportunities for shared encounters and conversations, which would remain invisible otherwise. We present the design of a user check-in system for improving the library as a physical destination for social learning, sharing, and inspiration for and by the community.     

Understanding online consumers’ purchase intentions: a contribution from social network theory

There is a dynamic and interconnected international setting shaped by the power of the Internet and social media. To gain more consumers, understand their behaviours and needs, and maintain closest relationships with them, businesses should understand how consumers behave in social media and how they vary in their purchase intentions. In the scope of the study, we integrate the social network theory and the theory of planned behaviour to analyse online consumers’ purchase intentions and to investigate their structural positions by analysing their friendships in social networks. We target Twitter users to conduct analysis due to Twitter's popularity in use, market penetration, and opportunity to work with open-source data. This study contributes to a better theoretical understanding of online consumers’ purchase intentions by integrating multiple theoretical perspectives. It expands the literature by considering both online consumers’ friendship network in Twitter and their individual online purchasing intentions. The study also guides e-marketers to design proper strategies for potential and current consumers and target the right sets of people in the social networks.     

Name it as you like it? Keeping pace with social media something

Social media has emerged over the last decade and consequently has accompanying information systems (IS) research. In this paper, however, we argue that researchers cannot keep pace with the rapid changes this topic engenders. This claim is substantiated by a database built from 28,283 articles published in either top IS journals, i.e., ELMA, BISE, and all AIS basket journals, or IS conference proceedings, i.e., ICIS, ECIS, AMCIS, WI, HICSS, and PACIS. We derive a set of 13 keywords that have been used to describe what we call the ‘social media phenomenon’. The results are transformed into a four-phase model and a taxonomical categorization, which provides an exhaustive overview of social media terms and trends and reflects the main research issue facing academia: The fact that the versatile social media phenomenon in its current phase is dominated by just two terms—“social media” and “social network”—or imprecise “such-as-Facebook” analogies.     

When does personalization work on social media? a posteriori segmentation of consumers

The aim of this research is to find a segment of consumers of fashion products based on their personal visions of personalization of shoppable ads on mobile social media. To meet this objective, three operational objectives are defined. First, a theoretical model is evaluated based on the stimulus-organism-response framework (S–O–R). This examines, with a PLS-SEM approach, how the stimulation of personalization will affect consumers' internal cognitive state (perceived usefulness) and consequently generates a behavioral response (intention to buy). Second, we look for fashion consumer segments based on their perception of personalization through prediction-oriented segmentation (PLS-POS). Third, the segments are explained based on three constructs that were considered important in fashion consumption through mobile social networks: purchase intention, concern for privacy, and perception of trend. The inclusion of personalization and the perception of usefulness of advertisements can greatly help the intention to purchase clothing to be understood. The application of a posterior segmentation helps to better understand the different types of users exposed to shoppable ads on mobile social networks and their relationship with the purchase intention, concern for privacy and trend. While the measures and scales were tested in a context of mobile clothing trade, the methodology can be applied to other types of products or services.

     

Are heavy users of computer games and social media more computer literate?

Adolescents spend a substantial part of their leisure time with playing games and using social media such as Facebook. The present paper examines the link between adolescents' computer and Internet activities and computer literacy (defined as the ability to work with a computer efficiently). A cross-sectional study with N = 200 adolescents, aged 17 on average, was conducted. Hierarchical regression analyses showed that an increase in time spent playing games on a PC/Mac was related to higher scores on practical and theoretical computer knowledge. Moreover, practical computer knowledge was higher for adolescents who liked playing shooters, fantasy games, or Facebook-games. Frequency of social media use was associated with higher scores in practical computer knowledge. A bootstrap analysis indicated that this relationship was mediated by a decrease in computer anxiety, not by more positive attitudes toward the computer. Gender yielded substantial main effects on the media literacy variables but the associations between the computer and Internet activities and the literacy aspects were similar for boys and girls. This study is expected to encourage future longitudinal research on adolescents' incidental learning during leisure time computer and Internet activities.     

Neural network’s selection of color in UI design of social software

Neural Computing and Applications - In recent years, the design of social software UI has become a design research focus in the field of design. Color affects many factors in UI design. However,...     

Towards understanding the gamification upon users’ scores in a location-based social network

Online social platform, such as Wikipedia and Foursquare, has been increasingly exploded due to not only various useful services provided but also social gaming mechanisms that can keep users actively engaged. For example, users are awarded ”virtual goods” like badges and points when they contribute to the community in the network by voluntarily sharing ideas and other information. In this paper, we aim to examine the effectiveness of a social gamification mechanism, named user scores, designed in Foursquare which is one of most popular location-based social networks. A user’s score in Foursquare is an aggregate measure based on recent check-in activities of the user, which reflects a snapshot summary of the user’s temporal and spatial behaviors. Whenever a user checks in to a venue, a list of scores of the user’s friends are visible to the user via a ”leaderboard” which ranks these users’ scores in a descending order. Given a pair of friends who participate in a score competition in such a gimification mechanism, we identify if one user’s scores have significant influence on the other user’s scores by utilizing the Granger Causality Test. To understand what types of users and what types of friends tend to participate in the score competition (i.e., their check-ins are more likely driven by such a gamification mechanism), we extract users’ features (e.g. user’s degree) as well as the features of pairs of friends (e.g., number of common friends, score similarity and ranking difference) to examine whether these features have correlations with those pairs of users who are identified as being involved in the score game. The identified influence on user scores has the important implication on applications including friend and venue recommendations in location-based social networks.     

Characterizing users’ check-in activities using their scores in a location-based social network

Analysis of users’ check-ins in location-based social networks (LBSNs, also called GeoSocial Networks), such as Foursquare and Yelp, is essential to understand users’ mobility patterns and behaviors. However, most empirical results of users’ mobility patterns reported in the current literature are based on users’ sampled and nonconsecutive public check-ins. Additionally, such analyses take no account of the noise or false information in the dataset, such as dishonest check-ins created by users. These empirical results may be biased and hence may bring side effects to LBSN services, such as friend and venue recommendations. Foursquare, one of the most popular LBSNs, provides a feature called a user’s score. A user’s score is an aggregate measure computed by the system based on more accurate and complete check-ins of the user. It reflects a snapshot of the user’s temporal and spatial patterns from his/her check-ins. For example, a high user score indicates that the user checked in at many venues regularly or s/he visited a number of new venues. In this paper, we show how a user’s score can be used as an alternative way to investigate the user’s mobility patterns. We first characterize a set of properties from the time series of a user’s consecutive weekly scores. Based on these properties, we identify different types of users by clustering users’ common check-in patterns using non-negative matrix factorization (NMF). We then analyze the correlations between the social features of user clusters and users’ check-in patterns. We present several interesting findings. For example, users with high scores (more mobile) tend to have more friends (more social). Our empirical results demonstrate how to uncover interesting spatio-temporal patterns by utilizing the aggregate measures released by a LBSN service.