Social Mirrors as Social Signals: Transforming Audio into Graphics

A social mirror is a specific type of visualization for group interaction. Three examples of social mirrors emphasize different motivations for visualizing vocal conversation: the power of visualization to influence conversation in real time, the addition of anonymous input into group visualization, and idea formation over time. Social visualizations are visualizations about people, for people. A social mirror is a type of social visualization with three particular qualities. First, it's a third-person visualization in which people can see information about themselves within information about other people. In a sense, we can perceive "us and them." Every participant sees the same visualization. Second, subtle perceivable changes of any participant appear in the visualization as they occur in near real time. This can include capturing and visualizing subtle social behaviors such as laughing and coughing. Because of this near real-time feature, people can quickly alter their behavior-and hence the visualization-so that others perceive them as they'd like to be perceived in this public mirror. Third, social mirrors allow for exploring group patterns and behavior through real-time experimentation, replay, annotation, and reconfiguration.     

College students’ academic motivation,media engagement and fear of missing out

The concerns about the consequences of mental problems related to use of social media among university students have recently raised consciousness about a relatively new phenomenon termed Fear of Missing Out (FoMO). Drawing on the self-determination theory and on the assumption that low levels of basic need satisfaction may relate to FoMO and social media engagement, the aim of the present research was to examine for the first time possible links between FoMO, social media engagement, and three motivational constructs: Intrinsic, extrinsic and amotivation for learning. Data were gathered from 296 undergraduate students by using the following scales: Social Media Engagement (SME), Fear of Missing Out (FoMOs) and Academic Motivation. The SME is a new scale, specifically designed for this study to measure the extent to which students used social media in the classroom. This scale includes three categories: Social engagement, news information engagement and commercial information engagement. Path analysis results indicated that the positive links between social media engagement and two motivational factors: Extrinsic and amotivation for learning are more likely to be mediated by FoMO. Interpretation of these results, their congruence within the context of the theoretical frameworks and practical implications are discussed.     

Getting acquainted through social network sites: Testing a model of online uncertainty reduction and social attraction

The first aim of this study was to examine which uncertainty reduction strategies members of social network sites used to gain information about a person who they had recently met online. The second aim was to investigate whether and how these uncertainty reduction strategies resulted in social attraction. Drawing on a survey of 704 members of a social network site, we found that respondents had used active, passive, and interactive strategies to reduce uncertainty about their new acquaintance. Interactive strategies were most effective in reducing uncertainty about the target person. Respondents’ level of uncertainty about the acquaintance mediated the relationships between the use of interactive uncertainty strategies and perceived similarity on the one hand and social attraction on the other. Finally, respondents’ perceived valence of the obtained information about the acquaintance moderated the relationship between the level of uncertainty and social attraction.     

Social Comparison,Goal Contagion,and Adoption of Innovative Information Technology

When adopting a new, innovative information technology (IT), most institutions tend to lack knowledge about it initially. However, they often adopt new ITs despite their illiteracy. This behavior is difficult to explain using rational IT adoption theories. Focusing on the organizational decision-making process behind adoption of innovative IT, we examine the strategy by which some companies compensate for their lack of knowledge: collection of information from other companies that have successfully adopted IT previously. We develop a new IT adoption model for which goal contagion theory and social comparison theory are combined to interpret the circumstances under which organizations tend to adopt new, innovative IT despite limited knowledge about it. Big data, cloud services, and smart mobile systems are considered as examples of innovative IT in the empirical study.     

Leveraging social media networks for classification

Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors?? latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes.     

Applying social learning analytics to message boards in online distance learning: A case study

Social learning analytics introduces tools and methods that help improving the learning process by providing useful information about the actors and their activity in the learning system. This study examines the relation between SNA parameters and student outcomes, between network parameters and global course performance, and it shows how visualizations of social learning analytics can help observing the visible and invisible interactions occurring in online distance education.The findings from our empirical study show that future research should further investigate whether there are conditions under which social network parameters are reliable predictors of academic performance, but also advises against relying exclusively in social network parameters for predictive purposes. The findings also show that data visualization is a useful tool for social learning analytics, and how it may provide additional information about actors and their behaviors for decision making in online distance learning.     

How to keep members using the information in a computer-supported social network

Computer-supported social networks have become a significant channel for people to interact and exchange information. The success of computer-supported social networks depends on the extent to which members will stay and continue participating. Many computer-supported social networks however suffer from the problem of retaining members. Drawing from theories of user satisfaction and information adoption, we develop a model to examine how computer-supported social networks encourage members to continue participating and using the information in the network. The theoretical model is validated through an online survey of 240 users of a Bulletin Board System established by a local university in China. The results reveal that individuals will continue to use the information in a computer-supported social network when they are satisfied with their prior usage, and when they perceive that the information in the network is useful. The results also suggest that individuals’ perceived information usefulness and satisfaction are determined by information quality and source credibility in the context of computer-supported social networks. Theoretical and practical implications about computer-supported social networks are discussed.     

Providing Adaptive Health Updates Across the Personal Social Network

This article presents research conducted to establish how information is shared across the personal social network in the sensitive context of a health crisis. We worked with parents of very sick babies who were cared for in a hospital's Neonatal Unit (NNU). Through a combination of interviews, a focus group, and surveys, we developed a user model of the information that parents wanted to share, and how they adapted this information to individual recipients. We then developed a prototype software tool which created adaptive updates for members of the parents' social network. The updates contained summaries of large volumes of complex medical data about the baby, nonmedical information about the parents, and practical information about the hospital. Updates were automatically adapted to individual members of parents' social networks, based on our user model. The tool was evaluated in a large NNU in the United Kingdom with parents of babies who were currently being cared for in the unit. We found that parents adapted the information that they shared about themselves and their babies based on the emotional proximity of their network members. They gave most detail to those who were emotionally closest to them and least to those who were less close. Parents also adapted information content to the recipient's tendency to worry and empathize. Two adaptive strategies were deployed by parents, (a) benign deceit—not telling the whole truth—and (b) promotion of empathetic members of the social network to a higher level of emotional proximity, so that they were given more information. We generated a number of directions for future work, and issues to consider around designing adaptive mediated communications systems for sensitive contexts. These include the potential to generalize our model to other medical contexts and considerations to apply when deliberately designing deceit into adaptive systems.     

User Resistance to Information System Implementations: A Dual-Mode Processing Perspective

This article examines the attitudes that can cause users to resist information system implementations using an established theory from social and cognitive psychology: the Elaboration Likelihood Model. It is argued that users who do not think deeply about systems represent a key blockage and that their attitudes are based largely on heuristics and peripheral influences. The results of a wide-ranging study are presented in which 28 heuristics and peripheral influences that commonly affect user attitudes are identified.     

Queer identity online: Informal learning and teaching experiences of LGBTQ individuals on social media

In this study, we explored how social media, particularly social networking sites, serve as informal learning environments for lesbian, gay, bisexual, transgender, questioning, and otherwise-identified (LGBTQ) individuals during formative stages of their evolving LGBTQ identity. We conducted semi-structured interviews (N = 33) probing LGBTQ individuals’ use of social media and identified three educational uses tied to online information seeking: traditional learning (e.g., information seeking about LGBTQ-related issues), social learning (e.g., observing role models or other LGBTQ individuals’ behavior and experiences), and experiential learning (e.g., experimenting with online dating sites and dating apps). These experiences were especially common during the coming out process. Participants also reported a fourth educational function, teaching (e.g., sharing information with others about their experiences as an LGBTQ individual). Teaching was more common among individuals who were out and those with less common identities (e.g., asexual and transgender). Several affordances of social media, including visibility, association, persistence, anonymity, and interactivity enabled these learning experiences.     

Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions

Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.     

Entropy based evaluation of net structures – deployed in Social Network Analysis

The analysis of Social Networks widely is based on their respective graphical structures. Centrality measures of actors usually consider their position in a graph. Sometimes graphs are an insufficient medium to represent social structures. In this paper the authors propose a new framework with the purpose to analyze social fabric correctly: information theory. A set of conditionals forms the knowledge base about the network’s structure. Once the knowledge base is adapted under maximum entropy or minimum cross-entropy a new form of analysis is available. The construction of knowledge bases and their analyses are realized in the expert system shell SPIRIT.     

Improving quality and quantity of contributions: Two models for promoting knowledge exchange with shared databases

Shared databases are used for knowledge exchange in groups. Whether a person is willing to contribute knowledge to a shared database presents a social dilemma: Each group member saves time and energy by not contributing any information to the database and by using the database only to retrieve information which was contributed by others. But if all people use this strategy, then the database will be empty and, hence, useless for every group member. Based on theoretical approaches, two models for fostering the information-sharing behavior of database users are presented. One for enhancing the quality of database contents, and one for enhancing the quantity of those contents. The models take into account the following factors: the kinds of rewards the participants obtain for contributing information, the individual costs associated with this contribution, the prospective metaknowledge about the importance of one’s own information to the others, and the retrospective metaknowledge about how much others contributed to and retrieved from the database. These factors enhance the quantity of database contents as well as their quality.     

The Identification and Influence of Social Roles in a Social Media Product Community

This research focuses on the identification of social roles and an investigation of their influence in online context. Relying on a systemic approach for role conceptualization, we investigate member's activity, shared content and position in the network within a consumer to consumer social media‐based community (SMC) around a product. This investigation led to the identification of ten core roles, based on three key elements: object of interest (product, practice, and community), main contribution type (sharing information and seeking information), individual orientation (factual, emotional). We propose an explanation about how these roles, through their positioning, participate in the community dynamics and how they contribute to the creation and diffusion of cookery as a social practice, shaping the periphery around this practice.     

Reflecting on Social Influence in Networks

In many social contexts, social influence seems to be inescapable: the behavior of others influences us to modify ours, and vice-versa. However, social psychology is full of examples of phenomena where individuals experience a discrepancy between their public behavior and their private opinion. This raises two central questions. First, how does an individual reason about the behavior of others and their private opinions in situations of social influence? And second, what are the laws of the resulting information dynamics? In this paper, we address these questions by introducing a formal framework for representing reasoning about an individual’s private opinions and public behavior under the dynamics of social influence in social networks. Moreover, we dig deeper into the involved information dynamics by modeling how individuals can learn about each other based on this reasoning. This compels us to introduce a new formal notion of reflective social influence. Finally, we initialize the work on proof theory and automated reasoning for our framework by introducing a sound and complete tableaux system for a fragment of our logic. Furthermore, this constitutes the first tableau system for the “Facebook logic” of J. Seligman, F. Liu, and P. Girard.     

Model use in sustainability policy making: An experimental study

We examine whether decision makers who use a system dynamics model more readily create policies whose outcomes match their stated priorities or are on the Pareto Frontier of achieved outcomes, and whether model users are more likely to change their priorities. Comparing model use with other decision tools, we address these questions in a role-play simulation (serious game) experiment in which participants make multi-dimensional policy recommendations to address environmental, economic, and social equity dimensions of sustainability. Participants are randomly assigned to use one of four decision support tools: (1) actively using the model or passively being briefed. The briefings cover: (2) the insights of the model; (3) general, relevant information; or (4) irrelevant information (control). We find that model-users, group (1), discovered key insights about win-win opportunities—over-performing their priorities—and most readily created policies on the Pareto Frontier of achieved outcomes. Groups (2) and (3) best matched their policy outcomes to their priorities, missing the win-win.     

Leveraging personal photos to inferring friendships in social network services

Social network services have become widely used as an important tool to share rich information; in such networks, making new friends is the most basic functionality to enable users to take advantage of their social networks. In this paper we look into personal photos as an additional source for social network analysis and analyze the potential of people tags in the photos for friend recommendations. We also propose a new compact data structure, collectively called Face Co-Occurrence Networks (FCON), which stores crucial and quantitative information about people’s appearance in photos. We discover strong associative relationships among people and recommend reliable social friends by utilizing FCON. Experimental results demonstrate the effectiveness and efficiency of our method for recommending friends in social network services.     

Social networking federation: A position paper

This paper proposes the concept and the technology of social networking federation as a paradigm where information on various social network systems can be seamlessly integrated in order to provide users a uniform and semantic view of their social connections. Such a uniformly fused social network provides a single point of access where all information with respect to one’s social networks can be queried and reasoned about. The goal of the research is to establish a foundation of integrating and assimilating information within multiple social network systems. We designed a reference model of social networking federation system in this paper, as well as some prototype application to demonstrate its paradigm. We believe our work would provide a novel vision of future online social network.     

How do people really seek information about others?: Information seeking across Internet and traditional communication channels

The present research describes the manner in which individuals use various media in the interpersonal information seeking process. Stephens' (2007) information and communication technology (ICT) succession theory was applied to an interpersonal information seeking context, and hypotheses and research questions about the channels people use to seek information about others of various relationship to the seeker were offered. Two hundred and twenty-five participants responded to a survey about this topic, and they reported a greater likelihood to seek information about less-known targets using channels where they would be unidentifiable. However, participants reported a greater likelihood to seek information about more-known targets using channels where they would be identifiable. Channels such as social networking websites were frequently reported to be useful regardless of whether the target was well known or less known. Properties of these channels and their implications for interpersonal information seeking as well as theoretical implications of these findings are discussed and directions for future research are examined.