Science Communication and Online Social Networks: Challenges and Opportunities

Online consumer social networks have become an important marketing channel for product-related information diffusion in the technological landscape. This paper aims to identify the factors that drive opinion diffusion behavior in consumers on online social networks. To empirically test the theoretically driven hypotheses, the study uses firsthand survey responses from 614 respondents based in two major cities in China: Shenzhen, an eastern city, and Xi’an, a western city. A survey of online shopping consumers shows that both network heterogeneity and network influence have a positive impact on consumers’ level of online opinion leadership and online opinion seeking behavior. However, network acquaintance has an insignificant impact on the level of opinion diffusion. Furthermore, online opinion diffusion has a positive impact on behavioral consequence, which has two dimensions: online forwarding and online communication. An online opinion leader is more likely to forward and communicate information than an online opinion seeker. Multiple-group analysis indicates that gender and city moderate the effect of opinion diffusion. Theoretically, this paper makes several contributions to marketing literature, particularly from a social networking perspective. The results from this study can help online retailers and social network service providers utilize consumer networks to improve online opinion diffusion. Within social networking literature, this is a unique attempt to explore the factors driving opinion diffusion behavior in consumers’ online social networks.

BackgroundThe US Centers for Disease Control and Prevention have identified a lack of encouragement, support, or companionship from family and friends as a major barrier to physical activity. To overcome this barrier, online social networks are now actively leveraging principles of companion social support in novel ways.ObjectiveThe aim was to evaluate the functionality, features, and usability of existing online social networks which seek to increase physical activity and fitness among users by connecting them to physical activity partners, not just online, but also face-to-face.MethodsIn September 2012, we used 3 major databases to identify the website addresses for relevant online social networks. We conducted a Google search using 8 unique keyword combinations: the common keyword “find” coupled with 1 of 4 prefix terms “health,” “fitness,” “workout,” or “physical” coupled with 1 of 2 stem terms “activity partners” or “activity buddies.” We also searched 2 prominent technology start-up news sites, TechCrunch and Y Combinator, using 2 unique keyword combinations: the common keyword “find” coupled with 1 of 2 stem terms “activity partners” and “activity buddies.” Sites were defined as online social health activity networks if they had the ability to (1) actively find physical activity partners or activities for the user, (2) offer dynamic, real-time tracking or sharing of social activities, and (3) provide virtual profiles to users. We excluded from our analysis sites that were not Web-based, publicly available, in English, or free.ResultsOf the 360 initial search results, we identified 13 websites that met our complete criteria of an online social health activity network. Features such as physical activity creation (13/13, 100%) and private messaging (12/13, 92%) appeared almost universally among these websites. However, integration with Web 2.0 technologies such as Facebook and Twitter (9/13, 69%) and the option of direct event joining (8/13, 62%) were not as universally present. Largely absent were more sophisticated features that would enable greater usability, such as interactive engagement prompts (3/13, 23%) and system-created best fit activities (3/13, 23%).ConclusionsSeveral major online social networks that connect users to physical activity partners currently exist and use standardized features to achieve their goals. Future research is needed to better understand how users utilize these features and how helpful they truly are.

Even though online social network services have become enormously popular among general public, there is a laxity of empirical investigations on the individual's level in this domain. This paper examines the impact of personality factors such as extraversion, self-esteem, opinion seeking and opinion leadership on brand communication and online social behaviors. Our results show that gender and extroversion predict online social network size and time spent online; that opinion seekers spend more time online and have larger networks relative to opinion leaders; and that opinion leaders are more likely to communicate their brand use online. We also find the mediating role of opinion leadership and opinion seeking in explaining the impact of general personality traits on online brand communication and social networking. Directions for future research are provided and some practical implications are discussed.

Online Social Networks (OSNs), such as Facebook and Twitter, are popular platforms that enable users to interact and socialize through their networked devices. However, the social nature of such applications forces users to share a great amount of personal data with other users and the OSN service providers, including pictures, location check-ins, etc. Even though some OSNs offer configurable privacy controls that limit access to shared data, users might misconfigure these controls due to their complexity or lack of clear instructions. Furthermore, the fact that OSN service providers have full access over the data stored on their servers is an alarming thought, especially for users who are conscious about their privacy. For example, OSNs might share such data with third parties, data mine them for targeted advertisements, collect statistics, etc. As a result, data and communication privacy over OSNs is a popular topic in the data privacy research community. Existing solutions include cryptographic mechanisms [1], trusted third parties [2], external dictionaries [3], and steganographic techniques [4]. Nevertheless, none of the aforementioned approaches offers a comprehensive solution that (i) implements fine-grained access control over encrypted data and (ii) works seamlessly over existing OSN platforms. To this end, we will design and implement a flexible and user-friendly system that leverages encryption-based access control and allows users to assign arbitrary decryption privileges to every data object that is posted on the OSN servers. The decryption privileges can be assigned on the finest granularity level, for example, to a hand-picked group of users. In addition, data decryption is performed automatically at the application layer, thus enhancing the overall experience for the end-user. Our cryptographic-based solution leverages hidden vector encryption (HVE)[5], which is a ciphertext policy-based access control mechanism. Under HVE, each user generates his/her own master key (one-time) that is subsequently used to generate a unique decryption key for every user with whom they share a link in the underlying social graph. Moreover, during the encryption process, the user interactively selects a list of friends and/or groups that will be granted decryption privileges for that particular data object. To distribute the decryption keys, we utilize an untrusted database server where users have to register before using our system. The server stores (i) the social relationships of the registered users, (ii) their public keys, and (iii) the HVE decryption keys assigned to each user. As the database server is untrusted, the decryption keys are stored in encrypted form, i.e., they are encrypted with the public key of the underlying user. Therefore, our solution relies on the existing public key infrastructure (PKI) to ensure the integrity and authenticity of the users’ public keys. To facilitate the deployment of our system over existing OSN platforms, we use steganographic techniques [6] to hide the encrypted data objects within randomly chosen cover images (stego images). The stego images are then uploaded to the OSN servers, and only authorized users (with the correct decryption keys) would be able to extract the embedded data. Unauthorized users will simply see the random cover images. We aim to implement our system as a Chrome-based browser extension where, after installation, the user registers with the un- trusted server and uploads/downloads the necessary decryption keys. The keys are also stored locally, in order to provide a user-friendly interface to share private information. Specifically, our system will offer a seamless decryption process, where all hidden data objects are displayed automatically while surfing the OSN platform, without any user interaction.

Online social networks (OSNs) have gained soaring popularity and are among the most popular sites on the Web. With OSNs, users around the world establish and strengthen connections by sharing thoughts, activities, photos, locations, and other personal information. However, the immense popularity of OSNs also raises significant security and privacy concerns. Storing millions of users' private information and their social connections, OSNs are susceptible to becoming the target of various attacks. In addition, user privacy will be compromised if the private data collected by OSNs are abused, inadvertently leaked, or under the control of adversaries. as a result, the tension between the value of joining OSNs and the security and privacy risks is rising.;To make OSNs more secure and privacy-preserving, our work follow a bottom-up approach. OSNs are composed of three components, the infrastructure layer, the function layer, and the user data stored on OSNs. For each component of OSNs, in this dissertation, we analyze and address a representative security/privacy issue. Starting from the infrastructure layer of OSNs, we first consider how to improve the reliability of OSN infrastructures, and we propose Fast Mencius, a crash-fault tolerant state machine replication protocol that has low latency and high throughput in wide-area networks. For the function layer of OSNs, we investigate how to prevent the functioning of OSNs from being disturbed by adversaries, and we propose SybilDefender, a centralized sybil defense scheme that can effectively detect sybil nodes by analyzing social network topologies. Finally, we study how to protect user privacy on OSNs, and we propose two schemes. MobiShare is a privacy-preserving location-sharing scheme designed for location-based OSNs (LBSNs), which supports sharing locations between both friends and strangers. LBSNSim is a trace-driven LBSN model that can generate synthetic LBSN datasets used in place of real datasets. Combining our work contributes to improving security and privacy in OSNs.

According to the literature, social relationships have a positive influence on work-related attitudes and behaviours. Taking into account that Online Social Networks (OSNs), brought about by Web 2.0, have become an international phenomena and have a considerable impact on the way people communicate and interact with each other, the chapter’s purpose is to evaluate the effect that the use of OSNs has on the worker’s attitudes and behaviours. In this way, the authors use a questionnaire to evaluate the attitudes of 157 faculty members in Higher Education Institutions (HEI). To assess the use of OSNs by faculty members, they use a binary variable. After analysing and discussing the results, the authors conclude that the use of OSNs influences the workers’ performance traits. The relations they propose in what concerns the workers’ attitudes are all empirically proven. At last, the authors describe the study limitations and suggest some perspectives for future research.

Recommender Systems for Online and Mobile Social Networks: A survey

82 Background: Online social networks (OSNs) have emerged in the past decade as potentially powerful tools for health information sharing and health behavior change. According to a recent study by the Pew Research Center, 75% of all parents are active on OSNs. Given the high level of need for childhood cancer survivorship health education and preventive health behavior change, OSNs represent key resources for parents to engage with experts and peers over survivorship health. Methods: A cross-sectional survey of 114 parents of young and adolescent ( < 13 years) childhood cancer survivors was used to examine parent OSN use in specific platforms. Recruitment was conducted through online social support groups of two childhood cancer non-profits that focus on parent support. The novel online survey investigated OSN frequency of use, history of making new friends in OSNs, and whether OSNs were “good for sharing and receiving reliable and high quality information on diet, physical activity, and other healthy behaviors.” Results: Parents reported high levels of OSN use with 80% reporting daily use. Facebook was the most commonly used OSN by a wide margin. Seventy-eight percent of parents reported using it every day. The next most used OSN was Instagram (15%). Parents reported making new friends on Facebook (86%), followed by Instagram (22%) and Twitter (10%). Among parents who have used the respective OSNs, several were endorsed as being “good” or “extremely good” for sharing survivorship-related preventive health information. Facebook was endorsed by 76% of its users, Twitter and Pinterest by 59%, Google+ by 52%, and Instagram by 40%. None of the demographic variables collected predicted parent OSN use or perception. There was a strong association between parents using OSNs daily and their reporting having made new friends on OSNs (p < .000). Conclusions: Parents of young childhood cancer survivors recruited from online support groups report high levels of OSN use and making new friends through OSNs, and find OSNs potentially good sources for sharing and receiving survivorship-related health information. OSNs in general, and Facebook in particular, represent promising avenues for childhood cancer survivorship health information dissemination.

In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a ‘community’ as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of ‘community.’ In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure.

Online social networks (OSNs) contain data about users, their relations, interests and daily activities and the great value of this data results in ever growing popularity of OSNs. There are two types of OSNs data, semantic and topological. Both can be used to support decision making processes in many applications such as in information diffusion, viral marketing and epidemiology. Online Social network analysis (OSNA) research is used to maximize the benefits gained from OSNs’ data. This paper provides a comprehensive study of OSNs and OSNA to provide analysts with the knowledge needed to analyse OSNs. OSNs’ internetworking was found to increase the wealth of the analysed data by depending on more than one OSN as the source of the analysed data. Paper proposes a generic model of OSNs’ internetworking system that an analyst can rely on. Two different data sources in OSNs were identified in our efforts to provide a thorough study of OSNs, which are the OSN User data and the OSN platform data. Additionally, we propose a classification of the OSN User data according to its analysis models for different data types to shed some light into the current used OSNA methodologies. We also highlight the different metrics and parameters that analysts can use to evaluate semantic or topologic OSN user data. Further, we present a classification of the other data types and OSN platform data that can be used to compare the capabilities of different OSNs whether separate or in a OSNs’ internetworking system. To increase analysts’ awareness about the available tools they can use, we overview some of the currently publically available OSNs’ datasets and simulation tools and identify whether they are capable of being used in semantic, topological OSNA, or both. The overview identifies that only few datasets includes both data types (semantic and topological) and there are few analysis tools that can perform analysis on both data types. Finally paper present a scenario that shows that an integration of semantic and topologic data (hybrid data) in the OSNA is beneficial.

Anxiety disorders are closely associated with an individual’s interactions, manifested in the way an individual expresses themselves and interacts with others in their social environment. However, little is explored empirically about the association of social network structure and the interactions of an individual with aspects of mental health functioning, such as anxiety. In recent years, individuals have begun to appropriate social media to self-disclose about their mental illnesses, seek support, and derive therapeutic benefits. The study examines the online social network and interaction characteristics of Twitter users who self-disclose about their anxiety disorders. We analyze a sample of 200 Twitter users and their over 200,000 posts shared on the platform, who were expert-validated to have self-disclosed about suffering from an anxiety disorder. On their data, a variety of attributes of the users’ online social networks, interactions, and social behaviors using natural language and network analysis approaches were modeled using state-of-the-art network science measures. A number of state-of-the-art supervised learning classification frameworks are built using these attributes, to identify whether an individual’s anxiety disorder status could be automatically inferred. Results show that these social network, behavior, and interaction attributes, when incorporated in a support vector machine classifier, signal an individual’s self-reported anxiety disorder status, in contrast to a demographically and activity matched control group, with 79% accuracy and 84% area under the receiver-operating characteristic curve. The work provides the first insights into the role that the social interactions and social network structure on online platforms play in characterizing an individual’s mental health experience, such as anxiety. We discuss the implications of our work in instrumenting online social platforms in ways that yield positive affordances and outcomes for individuals vulnerable to mental illnesses.

Communicating environmental sciences: Public discourse and policy development.

Online social media network has become part of human life by transforming the way users create new social relations or relate with family and friends. Online social network (OSN) has drastically increased the rate at which people interact with each other by simplifying the means of communication. However, privacy is raising a serious concern. All user generated data within the OSN system need to be protected against unauthorized friends or hackers or even against the provider of OSN. Many research works are going on to encounter the privacy issues in OSN. This paper analyses the limitations of the recent work being done in this field and proposes an efficient abstract solution to them.

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Research limitations/implications – 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. Originality/value – 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.

Why and how more and more people get involved and use social networking systems are critical topics in social network analysis (SNA). As a matter of fact, social networking systems bring online a growing number of acquaintances, for many different purposes. Both business interests and personal recreational goals are motivations for using online social networks (OSN) or other social networking systems. The participation in social networks is a phenomenon which has been studied with several theories, and SNA is useful for common business problems, e.g., launching distributed teams, retaining people with vital knowledge for the organization, improving access to knowledge and spreading ideas and innovation. Nevertheless, there are some difficulties, such as anti-social behaviors of participants, lack of incentives, organizational costs and risks. In this article, a survey of the basic features of SNA, participation theories and models are discussed, with emphasis on social capital, information spreading, motivations for participation, and anti-social behaviors of social network users.