The effect of role conflict on self‐disclosure in social network sites: An integrated perspective of boundary regulation and dual process model

As people increasingly integrate social network sites (SNSs) into their daily lives, their social connections on SNSs become more diverse, including their friends, co‐workers, and relatives. In such a context, users may receive different role expectations from their various social circles and experience role conflict in their usage of SNSs. Furthermore, previous literature suggests that people may not always make privacy‐related decisions through effortful and deliberate information processing. Drawing on the perspective of boundary regulation and dual process theories, this study clarifies the consequences of role conflict on SNSs. A theoretical model is developed to examine the effect of role conflict regarding privacy risk and perceived control, which, in turn, impact self‐disclosure behaviour, as well as how this process is moderated by high‐ versus low‐effort processing. The results of 4 experiments provide strong support for our model. The implications for theory and practice are discussed.     

Ambiguity in IT adaptation: making sense of First Class in a social work setting

This paper explores how people make sense of the ambiguity caused by newly introduced information technology (IT) in organizations. A better understanding of these sense‐making processes might provide some basis for improving the way in which information technology is adapted in organizations. On the basis of an interpretive case study, the paper identifies how certain attention structures facilitated and restricted the meaningful construction of IT in a particular organizational context – social work. In this context, the previous coincidence of IT introductions with increasing administrative workload had considerable implications for the way in which a communications technology (First Class) was adapted. On a general level, it is concluded that making sense of ambiguity is an important ingredient in successful IT adaptation.     

Increasing Attributional Certainty via Social Media: Learning About Others One Bit at a Time

This study presents a novel examination of changes in attributions about individuals via information obtained from online sources within the context of hiring decisions. An experiment had 127 participants examine dossiers collected about a job applicant, in some conditions containing either positively or negatively valenced information about the applicant, obtained from the Internet. Results indicated online information significantly increases attributional certainty and positively valenced online information led to more favorable impressions of the applicant's perceived fit and employability. Surprisingly, results also violate assumptions of the increased magnitude of attributions due to negative information. Findings are discussed with respect to uncertainty reduction strategies, the negativity effect of online information, and implications for job seekers and employers.     

Delineating the dimensions of social support on social networking sites and their effects: A comparative model

Social networking sites (SNSs) allow users to connect with each other by overcoming geographical and temporal boundaries and thus empower people to search for social support from online. Social support has been considered a key social value that online users can obtain from SNSs. However, few studies have systematically investigated social support in such a context. Motivated to address this gap, we have developed an advanced and theoretical framework to delineate social support on SNSs by clearly revealing the dimensions of online social support on SNSs and examining their effects on users' commitment and SNS continuance. Further, we introduce gender as a key moderator and explain in theory how differently men and women perceive the importance of the dimensions in evaluating online social support over SNSs. Our research results indicate that the identified three dimensions (informational support, emotional support, and network management) are important components of the online social support on SNSs, which is positively associated with commitment and continuance. In addition, the weight of each dimension in the evaluation of online social support varies by gender. This study is among the very first to explore online social support in the context of SNS and its effects, and has rich theoretical and practical implications.     

BFF: A tool for eliciting tie strength and user communities in social networking services

The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users’ relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.     

The effects of SNS communication: How expressing and receiving information predict MERS-preventive behavioral intentions in South Korea

Individuals use social network sites (SNSs) as an effective tool for communicating relevant information with others during the outbreak of infectious diseases. However, little is known about the underlying mechanism through which communicative behaviors influence preventive behaviors. Thus, in the context of Middle East respiratory syndrome (MERS) in South Korea, this study investigated how two communicative behaviors (message expression and reception) in SNSs affected the communicators’ intentions to engage in MERS-preventive behaviors. Using data collected from a nationally representative panel survey of 1000 Korean adults aged 19 or older, we examined a theoretical expression and reception effects model. Results support the presence of effects from expressing and receiving MERS-related information via SNSs and their underlying mechanism during South Korea’s MERS outbreak. Public health officials and communication professionals should actively use SNS communication in coping with public health crisis caused by emerging infectious diseases.     

Ambiguity in the Operator's Sense

Ambiguity is a central problem for operators working in dynamic, high‐risk environments. Operators must decide on a course of action before knowing with certainty how the system will respond. Though ambiguity is expected, how it presents is unexpected. We reduce ambiguity when we can give meaning to information and events through use of context, responsiveness of the system, and when we can view events from a higher level. Preparation and training for routine operations helps identify potential ambiguities before events begin. The experienced operator engages the situation relying on real‐time experience to learn what works through action, thus further reducing ambiguity.     

Open Challenges in Relationship-Based Privacy Mechanisms for Social Network Services

Social networking services (SNSs) such as Facebook and Twitter have experienced explosive growth during the few past years. Millions of users have created their profiles on these services because they experience great benefits in terms of friendship. SNSs can help people to maintain their friendships, organize their social lives, start new friendships, and meet others who share their hobbies and interests. However, all these benefits can be eclipsed by the privacy hazards that affect people in SNSs. People expose intimate information about their lives on SNSs, and this information affects the way others think about them. It is crucial that users be able to control how their information is distributed through the SNSs and decide who can access it. This article presents a list of privacy threats that can affect SNS users and what requirements privacy mechanisms should fulfill to prevent this threats. Then the article reviews current approaches and analyzes to what extent they cover the requirements.     

The Impact of Cross-Addiction on Information Sharing Behaviors on Social Networking Sites

ABSTRACT

There is a rise in the interest among researchers to understand how addiction to social networking sites (SNSs) influences the use of SNS for information sharing. Much less attention has been placed on the interactive effect of multiple addictions (i.e., cross-addiction) on information sharing behaviors on SNSs. In this study, we examine the interaction effect of SNS addiction and alcohol use on two characteristic sharing behaviors on SNSs, self-promotion, and peer promotion of alcohol use. This study contributes to theory and practice as it builds a model that integrates social cognitive theory and normative social influence to explain mechanisms through which cross-addiction shapes information sharing behaviors on SNSs. The proposed model is estimated using data from a sample of college students in a North American university. The results support the theorized relationships between cross-addiction and SNS use to promote addiction-related activities. Implications of the findings for theory and practice are discussed.     

H.266合并模式候选决策的研究与改进

针对H.266中基于历史块的运动矢量预测（HMVP）技术在换行更新列表过程中无法参考之前运动信息,以及HMVP候选列表在加入Merge列表过程中存在繁琐的去冗余过程的问题,提出一种改进的H.266合并模式候选决策算法。通过临时存储一个HMVP候选列表来解决原有HMVP列表在换行清空时存在的无法参考之前运动信息的不足,从而更好地预测运动信息。通过Merge候选列表中原有的空时域候选数量来确定需要进行去冗余的HMVP候选数量,以简化去冗余过程。实验结果表明,与H.266标准算法相比,该算法在随机接入的配置下,测试图像序列的亮度分量能够平均节省0.46%的码率,编码总时间平均缩短4.09%,帧间预测时间平均缩短10.87%,能够提高编码性能同时降低编码复杂度。     

Early detection of COTS component functional suitability

The adoption of COTS-based development brings with it many challenges about the identification and finding of candidate components for reuse. Particularly, the first stage in the identification of COTS candidates is commonly carried out by dealing with unstructured information on the Web, which makes the evaluation process highly costly when applying complex evaluation criteria. To facilitate this process, our proposal introduces an early measurement procedure for suitability of COTS candidates. Considering that filtering is about a first-stage selection, functionality evaluation might drive the analysis, proceeding with the evaluation of other properties only on the pre-selected candidates. In this way, a few candidates are fully evaluated making in principle the whole process more cost-effective. In this paper, we illustrate how functional measures at an initial state are calculated for an E-payment case study.     

An Empirical Study Evaluating Social Networking Continuance and Success

Social networking site (SNS) use decisions have led to major economic and social transformations worldwide. While many organizations seek to use SNSs from a strategic perspective to reach their customer, it is important to understand what makes SNSs successful in order to use them for competitive purposes. The current research evaluates the influence of the social capital theory on SNS success measures. A model was developed and empirically tested using two data samples to ensure valid and reliable results for success of SNSs. The results display the importance of social capital in SNS success followed by practitioner and academic implications.     

The effect of searching and surfing on recognition of destination images on Facebook pages

Social networking sites (SNSs) are playing an increasingly important role as information sources for tourists. Among the various types of SNSs, Facebook has become the leading site in terms of number of users and volume of content. Despite the proliferation of Facebook, however, there has been little research on its users’ cognitive processes as they acquire visual images from photos. Particularly in the context of tourism and hospitality, one largely ignored area that warrants examination is individuals’ recognition accuracy when acquiring visual information via Facebook. Based on LC4MP, this study examines users’ ability to encode tourism information acquired from Facebook by differentiating the two types of information-search processing conditions, surfing and searching. After employing a visual-recognition test, the findings of this study indicated that participants recognized the photos they acquired from searching more accurately than the photos they acquired from surfing on destination Facebook pages. Further discussion and implications are provided in the text.     

A Discovery-Based Approach to Database Ontology Design

In this paper, we introduce an approach to task-driven ontology design which is based on information discovery from database schemas. Techniques for semi-automatically discovering terms and relationships used in the information space, denoting concepts, their properties and links are proposed, which are applied in two stages. At the first stage, the focus is on the discovery of heterogeneity/ambiguity of data representations in different schemas. For this purpose, schema elements are compared according to defined comparison features and similarity coefficients are evaluated. This stage produces a set of candidates for unification into ontology concepts. At the second stage, decisions are made on which candidates to unify into concepts and on how to relate concepts by semantic links. Ontology concepts and links can be accessed according to different perspectives, so that the ontology can serve different purposes, such as, providing a search space for powerful mechanisms for concept location, setting a basis for query formulation and processing, and establishing a reference for recognizing terminological relationships between elements in different schemas.     

Quantifying explainable discrimination and removing illegal discrimination in automated decision making

Recently, the following discrimination-aware classification problem was introduced. Historical data used for supervised learning may contain discrimination, for instance, with respect to gender. The question addressed by discrimination-aware techniques is, given sensitive attribute, how to train discrimination-free classifiers on such historical data that are discriminative, with respect to the given sensitive attribute. Existing techniques that deal with this problem aim at removing all discrimination and do not take into account that part of the discrimination may be explainable by other attributes. For example, in a job application, the education level of a job candidate could be such an explainable attribute. If the data contain many highly educated male candidates and only few highly educated women, a difference in acceptance rates between woman and man does not necessarily reflect gender discrimination, as it could be explained by the different levels of education. Even though selecting on education level would result in more males being accepted, a difference with respect to such a criterion would not be considered to be undesirable, nor illegal. Current state-of-the-art techniques, however, do not take such gender-neutral explanations into account and tend to overreact and actually start reverse discriminating, as we will show in this paper. Therefore, we introduce and analyze the refined notion of conditional non-discrimination in classifier design. We show that some of the differences in decisions across the sensitive groups can be explainable and are hence tolerable. Therefore, we develop methodology for quantifying the explainable discrimination and algorithmic techniques for removing the illegal discrimination when one or more attributes are considered as explanatory. Experimental evaluation on synthetic and real-world classification datasets demonstrates that the new techniques are superior to the old ones in this new context, as they succeed in removing almost exclusively the undesirable discrimination, while leaving the explainable differences unchanged, allowing for differences in decisions as long as they are explainable.     

On the entropy for Atanassov's intuitionistic fuzzy sets: An interpretation from the perspective of amount of knowledge

Given the fact that totally unknown information cannot be expressed by Zadeh's fuzzy sets but can well be represented by Atanassov's intuitionistic fuzzy sets (A-IFSs), we provide in this paper a new idea, from the perspective of amount of knowledge, that the entropy for such information should be unique so that it may differ significantly from the entropy of a particular fuzzy element of which the membership and non-membership values are both equal to 0.5. Motivated by this idea, we present a more strict axiomatic definition of entropy on A-IFSs, and then develop a new entropy measure of A-IFSs that satisfies the refined axioms, with the aim of overcoming some drawbacks and ambiguities the existing entropy brings about and building a new entropy measurement model in the A-IFSs context to show some unique features of A-IFSs from the viewpoint of a specific purpose, notably those related to decision making. This allows us to capture the intrinsic amount of knowledge and the additional features that may be relevant when making decisions, and ultimately may be able to help us distinguish such particular cases in which the membership values are equal to the corresponding non-membership values in the A-IFSs context. Finally, a real-life example is provided for an in-depth discussion on the application of the developed models in decision making under uncertainty.     

Providing awareness,explanation and control of personalized filtering in a social networking site

Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to “the filter bubble” problem where the users can only receive information that matches their pre-stated preferences but fail to be exposed to new topics. Moreover, these SNSs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user’s usage experience and trust in the system can decline. This paper presents an interactive method to visualize the personalized filtering in SNSs. The proposed visualization helps to create awareness, explanation, and control of personalized filtering to alleviate the “filter bubble” problem and increase the users’ trust in the system. Three user evaluations are presented. The results show that users have a good understanding about the filter bubble visualization, and the visualization can increase users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing. The intuitiveness of the design is overall good, but a context sensitive help is also preferred. Moreover, the visualization can provide users with better usage experience and increase users’ trust in the system.