Issue Of Trustworthiness Research Articles

Review on Social Media and Digital Security

The emerging social media with inherent capabilities seems to be gaining edge over comprehensiveness, diversity and wisdom, nevertheless its security and trustworthiness issues have also become increasingly serious, which need to be addressed urgently. The available studies mainly aim at both social media content and user security, including model, protocol, mechanism and algorithm. Unfortunately, there is a lack of investigating on effective and efficient evaluations and measurements for security and trustworthiness of various social media tools, platforms and applications, thus has effect on their further improvement and evolution. To address the challenge, this paper firstly made a survey on the state-of-the-art of social media networks security and trustworthiness particularly for the increasingly growing sophistication and variety of attacks as well as related intelligence applications. And then, we highlighted a new direction on evaluating and measuring those fundamental and underlying platforms, meanwhile proposing a hierarchical architecture for crowd evaluations based on signaling theory and crowd computing, which is essential for social media ecosystem. Finally, we conclude our work with several open issues and cutting-edge challenges.

Review on Social Media and Digital Security

The emerging social media with inherent capabilities seems to be gaining edge over comprehensiveness, diversity and wisdom, nevertheless its security and trustworthiness issues have also become increasingly serious, which need to be addressed urgently. The available studies mainly aim at both social media content and user security, including model, protocol, mechanism and algorithm. Unfortunately, there is a lack of investigating on effective and efficient evaluations and measurements for security and trustworthiness of various social media tools, platforms and applications, thus has effect on their further improvement and evolution. To address the challenge, this paper firstly made a survey on the state-of-the-art of social media networks security and trustworthiness particularly for the increasingly growing sophistication and variety of attacks as well as related intelligence applications. And then, we highlighted a new direction on evaluating and measuring those fundamental and underlying platforms, meanwhile proposing a hierarchical architecture for crowd evaluations based on signaling theory and crowd computing, which is essential for social media ecosystem. Finally, we conclude our work with several open issues and cutting-edge challenges.

Case Study Approach to Conducting and Designing Qualitative Investigation for Business Researchers

Research into social problems, problems of deviation, of control and of crisis and the like, the general subject matter to which social problems are devoted is still mainly feasible through methods which yield qualitative data. Indeed, a method of analyzing qualitative interview data can be outlined as a stage-by-stage process. This research offers a resource guide for qualitative researchers in social science investigations, discussing data collection techniques, data analysis, reporting, and the issues of validity, reliability, and ethics adopting the qualitative context. This study further provides an overview of important concepts (manifest and latent content, unit of analysis, meaning unit, condensation, abstraction, content area, code, category and theme) related to qualitative content analysis. The research would serve as a resource guide for qualitative researchers in the field of business and the social sciences, discussing data collection techniques, data analysis, reporting, and the issues of trustworthiness, dependability, credibility and ethics. Keywords: Case Studies, Manual Analysis, In-depth Interviews, Qualitative Research, Research Methodology.

A logic for biassed information diffusion by paranoid agents in social networks

Abstract Information transmission in social networks is riddled with issues of reliability and trustworthiness. One of the main sources of disinformation can be traced back to agents—human or artificial—whose political or cultural agenda is guided by conspiracy theories. Modelling and understanding the behaviour of such agents within social networks is therefore crucial to approach the disinformation problem. In the present paper, we formulate the logic (un)SecureNDsim*, equipped with a proof-theory and a relational semantics in which negative trust relations are defined formalizing the attitude of paranoid agents, i.e. agents distrusting any information originating from the authority and thereby spreading what can be characterized as the content of conspiracy theories. The logic is implemented in a multi-agent simulation aimed at analysing the effects of conspiracy theorists in networks of agents. In particular, we analyse consensus reaching scenarios and the ability of paranoid agents to induce the spread of potentially false information.

Design and implementation of hybrid consensus mechanism for IoT based healthcare system security.

Nowadays, blockchain is emerging as a worthwhile technology for managing sensitive data in electronic healthcare system. It plays an important role in healthcare, medical research and insurance sectors. The consensus algorithms used in blockchain technology for the selection of a new block, provide high-level of security to IoT devices. However, the reliability problem still exists. In the present paper, a blockchain based hybrid consensus mechanism (HCM) is implemented in electronic healthcare system (EHS) to overcome trustworthiness issues. The aim of the proposed HCM is to maintain reputation module on the basis of the block activities. In this context, EHS is designed in four layers namely: system layer, inter-network layer, blockchain layer, and cloud layer. Moreover, HCM consists of five algorithms for creation, validation, fork handling (if any), Merkle tree construction and reward/punishment module respectively. Dev C++ software platform is used for the simulation of the above mentioned HCM algorithms excluding Merkle tree construction which is simulated using conditional contrast high itemset tree. It is observed that all the blocks use same methodology to be the part of a new blockchain network. Moreover, the CPU and memory consumption in the implementation of HCM is always below two percent and about fifty percent respectively as shown in the latency graph. The basic security goal (confidentiality, integrity and availability) is guaranteed with the help of the height of the Merkel tree as well. The performances of the proposed HCM and Proof of X-repute (PoXR) blockchain consensus algorithms are compared with respect to various parameters such as final difficulty, reward and punishment provisions etc. HCM shows superior performance as the final earned reputation is calculated for each block along with the reward and punishment modules during the deployment of blockchain network. In addition, a simple concept of Merkle-tree is opted for providing reward and punishment rather than a set of complex mathematical equations as used in PoXR consensus algorithm.

Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier

Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy.

Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane

Rheumatoid arthritis (RA) is a systemic autoimmune disease that preferably affects small joints. As the well-timed diagnosis of the disease is essential for the treatment of the patient, several works have been conducted in the field of deep learning to develop fast and accurate automatic methods for RA diagnosis. These works mainly focus on medical images as they use X-ray and ultrasound images as input for their models. In this study, we review the conducted works and compare the methods that use deep learning with the procedure that is commonly followed by a medical doctor for the RA diagnosis. The results show that 93% of the works use only image modalities as input for the models as distinct from the medical procedure where more patient medical data are taken into account. Moreover, only 15% of the works use direct explainability methods, meaning that the efforts for solving the trustworthiness issue of deep learning models were limited. In this context, this work reveals the gap between the deep learning approaches and the medical doctors’ practices traditionally applied and brings to light the weaknesses of the current deep learning technology to be integrated into a trustworthy context inside the existed medical infrastructures.

Sociality and the Need for the Epistemology of Social Relatedness: IJSS Editorial Statement

As our inaugural statement, this article conceptualises sociality, which forms the focus of the "Interdisciplinary Journal of Sociality Studies". We reinvigorated its “scope and focus” for the cognisance of our potential authors. We also explicated, step by step, our fundamental editorial process, including the issue of originality, trustworthiness and misconduct, ethical considerations, citation and referencing style, disclosure and conflict of interest, permissions and acknowledgement, as part of our quality production process. The article also presents the academic background of all our inaugural editorial team to display the potential of IJSS towards the production of quality knowledge on sociality studies. Based on this exploration, we conclude that IJSS accommodates any scholarly articles that solve social-related problems with an iota of sociological and emancipatory tendency involving humans and their relationships with but not limited to environment, objects and subjects. Therefore, we recommend that all potential authors ensure that the author guidelines' instructions are strictly adhered to while preparing for submission.

Conducting Online Healthcare Research With Open-Ended Questions in the Age of COVID-19: A Critical Review.

Worldwide, there is a remarkable increase in Internet use, with a current penetration rate of 62%. This widespread Internet use and the global coronavirus disease (COVID-19) preventive measures provide opportunities for data collection using the Internet in healthcare research. No recent studies have been conducted regarding the methodological issues of asynchronous Internet research that employed opened-ended questions to explore providers' and patients' experiences. This study utilizes prior research to explore methodological issues that affect online research using open-ended questions to obtain health data. The electronic databases searched were PubMed, CINAHL, and full-text Ovid. Reference lists and the Journal of Medical Internet Research were manually searched. The search strategy was based on the PRISMA flow diagram. Articles published between January 2003 and May 2020 were searched. Inclusion criteria were asynchronous online researcher-led studies that used open-ended questions to explore healthcare issues. Methodological issues were extracted from the selected studies. The evidence suggests that factors such as technical/website study/survey design issues, smartphone study applications, use of reminders, incentives, overrecruiting participants, using a combination of asynchronous and synchronous methods, trustworthiness, ethical and security issues affect the quality of data obtained in online health research. Asynchronous online research methods with open-ended questions could be used to collect high-quality data from patients, healthcare providers, and other participants in self-isolation, quarantine, and in diverse locations. However, researchers should be aware of the identified methodological issues. Future research could explore methodological issues and data quality in combined asynchronous and synchronous data collection methods.

Exploring men\u2019s use of mental health support offered by an Australian Employee Assistance Program (EAP): perspectives from a focus-group study with males working in blue- and white-collar industries

BackgroundMen continue to be overrepresented in the Australian suicide statistics despite wide scale public health initiatives to improve men’s mental health literacy and to increase their help-seeking behaviour. Employee Assistance Programs (EAP) deliver free and confidential mental health support; however, their services are underutilised by men. In the absence of contemporary literature that explores end-user experiences of EAPs, we asked men from blue- and white-collar employment settings about the barriers and enablers to using EAP services and explored differences between employment settings.MethodsForty-four men participated in this qualitative study: 32 from one white-collar employer and 12 from one blue-collar employer. Two qualified mental health professionals facilitated five first-round and three second-round focus groups and one interview with white-collar workers, and two focus groups and three interviews with blue-collar workers. Data were thematically analysed using a framework approach.ResultsFour of the six main themes were barriers: no need for EAP—alternative supports; uncertainty of EAP services; scepticism and distrust of EAP; and societal and workplace cultures. Elements of enduring barriers to EAP use were contained within sub-themes. These included lack of knowledge about EAPs, issues of trustworthiness and confidentiality, and fear of stigma and career jeopardy. Enablers comprised the need for attractive, reliable messaging and proactive connections and service delivery. Differences within sub-themes for white-collar and blue-collar groups reflected the corporate nature of work and workplace culture for white-collar participants, and workers’ communication and practical problem resolution preferences for blue-collar workers.ConclusionSome elements identified in the barriers to EAP use are more entrenched than were previously estimated and these need to be a priority for action to increase confidence in EAP services by end-users. EAPs that have a visible and proactive presence in the workplace, that tailor their marketing and service delivery to different workgroups, that provide a competitive advantage to its service users, and more confidently conveys independence from its client organisations may help to increase men’s interest in accessing EAP support services. Further initiatives that reduce the stigma surrounding mental health and help-seeking both in society and the workplace are needed.

Internationalization Behavior of Biotech Firms Producing a COVID-19 Vaccine

The purpose of this research is to explore the internationalization behavior of BioTech firms producing a COVID 19 vaccine using the Born Global internationalization model as a conceptual framework. The research methodology that has been considered in this study is a qualitative, multiple-case study to gain valuable insight about the internationalization behavior of BioTech firms producing a COVID 19 vaccine, and how and why these BioTech firms internationalize. Secondary data was collected via various websites, the BioTech firms’ official websites, through corporate documents, press releases, published articles, journals, and annual reports. The data analysis disclosed four themes to answer the research questions. The findings from the qualitative analysis, suggest that BioTech firms producing a COVID-19 vaccine use the combination of multiple market entry modes and expansion strategies to internationalize. The first limitation of this research is the sample size used in the multiple-case study. Secondly, although most of the information was from the websites of the firms in this paper, other sources were also used and that could raise the issue of quality and trustworthiness. There is a call for further research to explore the internationalization process, behaviour, and patterns of BioTech firms producing a COVID-19 vaccine using different methodologies, sample size, the industry, and firms from other continents to close the gap.

The issues of information trustworthiness during the pandemic

The article analyzes the changes observed in the modern media environment, their specific features, achievements and shortcuts, and especially, during the pandemic. It discusses the role of mass media in an ongoing emergency around the world. In the modern information society, any socially important processes that also affect the safety and health of citizens are accompanied by a flow of false information. The more participants in the process and the more complex the subject area, the wider the space for manipulation and dissemination of disinformation. Such misinformation can be more dangerous than the phenomenon that generated the threat.

RELATIONSHIP BETWEEN PROSOCIAL BEHAVIOUR AND ALTRUISM

The article deals with the definition and description of the concept of altruism from its ethical and philosophical side against the background of prosocial behaviour. We point out the relation between prosocial behaviour of man and altruism based on a summary of respondents 'opinions on the issues of trustworthiness and efficiency of charitable organizations, respondents' participation in their support and the most common way of donation. Effective altruism is supposed to be a kind of helper for people who want to donate part of their money to charity organizations to improve the world, but are unable to make the right decision about whose financial contribution they are donating. Distrust of charities is becoming more and more current, leading people to stagnate charity. The lack of information on efficiency, functioning, results and a non-transparent list of sources of funding for charitable organizations has prompted us to be more interested in this area. A transparent list of the most effective charities with detailed information on the use of funds to which every person would have access could be a way to express charity and humanity more than ever before. The most ideal scenario is for all people to adopt such behaviour, but in today's world there is the opposite extreme - egoism, selfishness, individualism, materialism and lack of interest in others, which, unfortu-nately, often hide behind altruism. Society, as such, cannot do without altruistic be-haviour, so even "impure" motives can bear fruit, of course, to some extent. Altruism is most often confused with the concept of pro-sociality, as a way of helping others, without expecting the reward that can come, but forms the core not only of ethical education but of each education because it leads to more positive relationships. Living in a society where there is positive energy and people are helping each other is a fuller life.

Making the Case for Unrestricted and Indiscriminate Data Collection: (a Prerequisite for the Success of Digital Economy)

Proposition: Data will not be protected, at all. In other words, there will be unrestricted and indiscriminate data collection. And, privacy will undergo fundamental change. I do acknowledge and, in fact, admit that the preceding assertions are very radical given the stance of European Union and the propounders of AI ethics in different countries of the world on the issues of data collection, data protection and privacy. Even the business corridors are no strangers to the issues. Perhaps that is why the issue of trustworthiness is being promulgated in relation to data. Regardless, I still maintain my stance, grounding it in the matrix of reality, reasoning, law, and economic structuring. Also, the companies that are in control of technology landscape are either ‘ignoring’ ethics or resorting to them to avoid concrete government regulations. After all, a peculiar thing about technological advancement is that it precedes law making. Some experts have warned that the data regulations as it acts against AI oriented enterprises, whether old or new. Needless to say, regulation of AI will finally become redundant and, hence, counterintuitive as an idea when we achieve AGI (Artificial General Intelligence). In my opinion, academic and public discourse on data protection and data privacy are a deliberate attempt at misdirection. The work being done by ethics experts is not only counterintuitive to the direction in which the technological developments are unfolding but is also a desperate attempt in skewing the said development. The paper explains that in further detail. Data collection should be unrestricted, indiscriminate, and absolute. Why do I say that? There are many reasons but, primarily, the most important of them could be put forth as follows: 1) Data is a pre-requisite for digital economy. 2) Data is already being collected in the absence of any substantial laws or discarding the laws, in essence 3) More regulations do not conform to the principles of free market economy which the advanced economy stand with, in principle. 4) Regulations increase the cost in favour of a few and to the detriment of the rest of the players.

Perceptions of Preconception Care among Pregnant Women at Masvingo General Hospital, Zimbabwe: A Qualitative Study

Background & aim: Preconception care (PCC) is the provision of biomedical, behavioral, and social health interventions for women and couples before conception occurs. Although it serves to close the gap of poor maternal health through promoting good behavior, risk assessment and management of chronic medical conditions, treatment of infections, and vaccinations, but it is still a challenge to most women of the childbearing age worldwide. The present study was performed to investigate the perceptions of women of the childbearing age regarding PCC. Methods: A descriptivequalitative design was used on a purposive sample of eight pregnant women within the age range of 15-49 years attending Masvingo general hospital, Zimbabwe for antenatal care. The sample size was determined by data saturation. The data were collected within March 2018 to April 2018 through in-depth interviews, which were conducted in private rooms while detailed notes were taken. Thematic analysis was manually performed to analyse the data. Issues of trustworthiness was considered. Results: Major themes identified were inadequate knowledge of PCC, recognition of the importance of PCC, barriers to PCC, and facilitators of PCC. Conclusion: Women acknowledged the importance of PCC. However, there was a number of barriers to the full utilization of PCC, and it was a neglected aspect of maternal-child health. There is a need to promote the awareness of PCC to enable informed decision-making regarding the issues of fertility and pregnancy planning.

Contextualizing reliability and validity in qualitative research: toward more rigorous and trustworthy qualitative social science in leisure research

Issues of trustworthiness in qualitative leisure research, often demonstrated through particular techniques of reliability and/or validity, is often either nonexistent, unsubstantial, or unexplained. Rather than prescribing what reliability and/or validity should look like, researchers should attend to the overall trustworthiness of qualitative research by more directly addressing issues associated with reliability and/or validity, as aligned with larger issues of ontological, epistemological, and paradigmatic affiliation. In reviewing contemporary qualitative research methodologies, we present a variety of reliability and validity techniques that might lead to increased trustworthiness in analysis and representation of findings. Qualitative leisure scholars are encouraged to align paradigmatic assumptions, theoretical orientations, methodological practices, analytical techniques, and representational practices to engage trustworthiness techniques that can be assessed for quality and credibility. This conceptualization offers a useful pedagogical model and supports common language of qualitative preferred practices, while providing space and openness for growth, development, improvisation, and critique.

QoS Prediction of Web Services Based on Reputation-Aware Network Embedding

As the emergence of numerous services with similar functions, it is very helpful to recommend personalized services for users, and urgent to accurately predict the QoS(Quality-of-Service) values of Web services. Collaborative Filtering (CF) is a commonly-used method to handle above issues. However, it faces two common issues: data sparsity problem and trustworthiness issue, which greatly reduces its prediction accuracy. To address this problem properly and systematically, we introduce the network embedding learning into the QoS prediction process and propose an improved QoS prediction method based on the reputation-aware network embedding learning. Firstly, a two-phase K-means clustering is adopted to filter untrustworthy users. Next, the reputation of trustworthy users is calculated, and an attributed user-service bipartite network is constructed between trustworthy users and services while considering the user reputation. Then the reputation-aware network embedding is adopted to learn the hidden representations of users. Finally, user-based CF is adopted to predict the unknown QoS values. The experimental results show that our method has a significant improvement in accuracy compared with other methods.

Content marketing from the perspective of PR specialists: an Estonian case study

Abstract This paper is an exploratory study to understand the content marketing practices in Estonia, a current trend that ties together journalism, communications, and advertising. Estonia is a small market where the ‘guilds’ of journalists and PR professionals are rather intertwined. Trends occurring here may provide suggestions for larger markets and future developments. A qualitative study was conducted in Spring 2018. The objective of the study was to describe the problems and potential complications arising from the reorganization of traditional areas of activity of agents operating in the field of the communications industry – specifically in the context of content marketing, and from the viewpoint of representatives of PR agencies. The article begins with putting the phenomenon of content marketing into a wider societal context – and specifically that of the Estonian media ecosystem. The possible influences of content marketing on such important realms, and such defining factors as trust (Luhmann 2000), social capital (Bourdieu 1995) the integrity, independence, and the interactions between the different fields (ibid) are discussed. The results of the survey indicate that the field of public relations is changing as a result of the forces from the other neighbouring fields, that of journalism and advertising. The paper points out that the issue of trust and trustworthiness and the origins of ‘the media’ need to be addressed in order to provide integrity and transparency.

Data Analysis Methods for Qualitative Research: Managing the Challenges of Coding, Interrater Reliability, and Thematic Analysis

The purpose of this article is to provide an overview of some of the principles of data analysis used in qualitative research such as coding, interrater reliability, and thematic analysis. I focused on the challenges that I experienced as a first-time qualitative researcher during the course of my dissertation, in the hope that how I addressed those difficulties will better prepare other investigators planning endeavors into this area of research. One of the first challenges I encountered was the dearth of information regarding the details of qualitative data analysis. While my text books explained the general philosophies of the interpretive tradition and its theoretical groundings, I found few published studies where authors actually explained the details pertaining to exactly how they arrived at their findings. Some authors even confirmed my own experience that few published studies described processes such as coding and methods to evaluate interrater reliability. Herein, I share the sources of information that I did find and the methods that I used to address these challenges. I also discuss issues of trustworthiness and how matters of objectivity and reliability can be addressed within the naturalistic paradigm.

A privacy-preserving collaborative reputation system for mobile crowdsensing

Mobile crowdsensing is an emerging technology in which participants contribute sensor readings for different sensing applications. This technology enables a broad range of sensing applications by utilizing smartphones and tablets worldwide to improve people’s quality of life. Protecting participants’ privacy and ensuring the trustworthiness of the sensor readings are conflicting objectives and key challenges in this field. Privacy issues arise from the disclosure of the participant-related context information, such as participants’ location. Trustworthiness issues arise from the open nature of sensing system because anyone can contribute data. This article proposes a privacy-preserving collaborative reputation system that preserves privacy and ensures data trustworthiness of the sensor readings for mobile crowdsensing applications. The proposed work also counters a number of possible attacks that might occur in mobile crowdsensing applications. We provide a detailed security analysis to prove the effectiveness of privacy-preserving collaborative reputation system against a number of attacks. We conduct an extensive simulation to investigate the performance of our schema. The obtained results show that the proposed schema is practical; it succeeds in identifying malicious users in most scenarios. In addition, it tolerates a large number of colluding adversaries even if their number surpass 65%. Moreover, it detects on-off attackers even if they report trusted data with high probability (0.8).