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Social media has become a vital part of any institute’s marketing plan. Social networks benefit businesses by allowing them to interact with their clients, grow brand exposure through offers and promotions and find new leads. It also offers vital information concerning the general emotions and sentiments directly connected to the welfare and security of the online community involved with the brand. Big organizations can make use of their social media data to generate planned and operational decisions. This paper aims to look into the conversion of sentiments and emotions over time.

In this work, a model called sentiment urgency emotion detection (SUED) from previous work will be applied on tweets from two different periods of time, one before the start of the COVID-19 pandemic and the other after it started to monitor the conversion of sentiments and emotions over time. The model has been trained to improve its accuracy and F1 score so that the precision and percentage of correctly predicted texts is high. This model will be tuned to improve results (Soussan and Trovati, 2020a; Soussan and Trovati, 2020b) and will be applied on a general business Twitter account of one of the largest chains of supermarkets in the UK to be able to see what sentiments and emotions can be detected and how urgent they are.

This will show the effect of COVID-19 pandemic on the conversions of the sentiments, emotions and urgencies of the tweets.

Sentiments will be compared between the two periods to evaluate how sentiments and emotions vary over time taking into consideration the COVID-19 as an affective factor. In addition, SUED will be tuned to enhance results and the knowledge that is mined when turning data into decisions is crucial because it will aid stakeholders handling the institute to evaluate the topics and issues that were mostly emphasized.

The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.

The detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.

The paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.

The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

This strategy significantly reduces the computational overhead and storage overhead required when using the kernel density estimation method to calculate the abnormal evaluation value of the test sample.

To effectively deal with the security threats of botnets to the home and personal Internet of Things (IoT), especially for the objective problem of insufficient resources for anomaly detection in the home environment, a novel kernel density estimation-based federated learning-based lightweight Internet of Things anomaly traffic detection based on nuclear density estimation (KDE-LIATD) method. First, the KDE-LIATD method uses Gaussian kernel density estimation method to estimate every normal sample in the training set. The eigenvalue probability density function of the dimensional feature and the corresponding probability density; then, a feature selection algorithm based on kernel density estimation, obtained features that make outstanding contributions to anomaly detection, thereby reducing the feature dimension while improving the accuracy of anomaly detection; finally, the anomaly evaluation value of the test sample is calculated by the cubic spine interpolation method and anomaly detection is performed.

The simulation experiment results show that the proposed KDE-LIATD method is relatively strong in the detection of abnormal traffic for heterogeneous IoT devices.

With its robustness and compatibility, it can effectively detect abnormal traffic of household and personal IoT botnets.

The patient journey from detection of an eye problem by optometrists to assessment and treatment by ophthalmologists can be streamlined by direct referral from optometrist to hospital eye service (HES). This requires locally agreed guidelines and training and feedback for optometrists to ensure high diagnostic competence. The purpose of this paper is to evaluate the quality of the West Suffolk Direct Referral Scheme, one of the first direct referral schemes in the UK to include all ophthalmic sub‐specialties.

Two‐cycle audit of existing practice, including all new patients referred by optometrists and seen at West Suffolk Hospital during a three‐month period in 2003 and a seven‐week period in 2006. Three interventions: direct referral clinics for urgent patients; introduced in 2003; six‐monthly training sessions for optometrists; and regular, prompt feedback via letter about individual consultation outcome. Prospective data collection via proforma in both cycles; additional retrospective data collection in the second cycle. Diagnostic accuracy, perception of urgency and request of subspecialty clinic were evaluated.

The direct referral scheme streamlines the patient journey, and patients with acute problems have fast access to HES. In total, 99 per cent of referrals are appropriate. Diagnostic competence is high (87 per cent), and has improved with tighter communication between HES and optometrists. Agreement is less for referral urgency (75 per cent) and choice of subspecialty clinic (74 per cent).

The West Suffolk Direct Referral Scheme provides an efficient service of high quality. Good communications and continued feedback between community‐ and hospital‐based eye care services improve standards and facilitate efficient use of resources.

The increasing speed and volume of attacks against networked systems highlights the need to automate the intrusion response process. This paper proposes a means by which such automation may be achieved, and presents details of a practical implementation.

The paper outlines the architecture of a flexible and intelligent automated response system that is able to adapt response decisions according to the context in which a detected incident has occurred. The discussion presents details of a prototype implementation that has been used to evaluate the concept in practice, and demonstrates the feasibility of assessing contextual factors associated with detected incidents.

A series of worked examples are presented to show how the same incident occurring in different contexts will trigger different decisions from the response system.

The paper contributes towards the domain of intrusion response, and proposes an approach that would enable automation of the response process to be more acceptable to security administrators.

This paper aims to investigate, using stakeholder map methodology, showing power, urgency, legitimacy and concerns of different actors, the current state of corporate social responsibility (CSR) in Switzerland. Previous research on CSR in Europe has made few attempts to identify stakeholders and their contribution to this topic.

To derive this map, publicly available documents were explored, augmented by 27 interviews with key stakeholders (consumers, media, government, trade unions, non-profit organisations [NPOs], banks, certifiers and consultants) and management of different companies (multinational enterprises [MNEs], small- and medium-sized enterprises [SMEs] and large national companies). Using MAXQDA, the quantified codes given for power, legitimacy and urgency were triangulated between self-reporting, external assessments and statements from publicly available documents and subsequently transferred into stakeholder priorities or, in other words, into positions in the map. Further, the codes given in the interviews for different CSR interests and the results from the document analysis were linked between stakeholders. The identified concerns and priorities were quantitatively analysed in regard to centrality and salience using VennMaker.

The paper identified SMEs, MNEs and cooperating NPOs as being the most significant stakeholders, in that order. CSR is, therefore, not driven primarily by regulators, market pressure or customers. Further network parameters substantiated the importance of SMEs while following an unconventionally informal and idiosyncratic CSR approach. Hence, insights into these ethics-driven, unformalised business models that pursue broader responsibility based on trust, traditional values, regional anchors and the willingness to “give something back” were formed. Examples of this strong CSR habit include democratic decisions and abolished hierarchies, handshake instead of formal contracts and transparency in all respects (e.g. performance indicators, salaries and bonuses).

In total, 27 interviews as primary data that supplements publicly available documents are clearly only indicative.

The research found an innovative, vibrant and practical CSR model that is emerging for reasons other than conventional CSR agendas that are supposed to evolve. In fact, the stakeholder map and the CSR practices may point at a very different role businesses have adopted in Switzerland. Such models offer a useful, heuristic evaluation of the contribution of formal management systems (e.g. as could be found in MNEs) in comparison to the unformalised SME business conduct.

A rarely reported and astonishing feature of many of the very radical SME practices found in this study is that their link to commercial strategies was, in most cases, not seen. However, SMEs are neither the “poor relative” nor the abridged version of CSR, but are manifesting CSR as a Swiss set of values that fits the societal culture and the visionary goals of SME owners/managers and governs how a sustainably responsible company should behave. Hence, as a new stance and argument within CSR-related research, this paper concludes that “informal” does not mean “weak”. This paper covers a myriad of management fields, e.g. CSR as strategic tool in business ethics; stakeholder and network management; decision-making; and further theoretical frameworks, such as transaction cost and social capital theory. In other words, this research closes scientific gaps by at once applying quantitative as well as qualitative methods and by merging, for the first time, network methodology with CSR and stakeholder research.

This paper aims to critically examine the lie detector test policy of the Nigeria Police Force to determine if the policy is capable of curbing corruption in the Nigerian Police Force.

The analysis took the form of a desk study, which analyzed various documents and reports such as the report by the United Nations Office on Drugs and Crime and the National Bureau of Statistics titled “Corruption in Nigeria – Bribery: Public Experience and Response,” Transparency International’s Corruption Perceptions Index 2017, the report by the International Police Science Association and the Institute for Economics and Peace.

This paper determined that the lie detector test policy of the Nigeria Police Force could achieve its desired objectives if the following recommendations are implemented: The Nigeria Police Reform Trust Fund bill should be given accelerated consideration in the Senate and House of Representatives based on its urgency and significance for the new lie detector test policy of the Nigeria Police Force. There is need for the Nigerian Police to have enough funds to conduct trainings for police personnel who are chosen as examiners for the lie detector tests. The Nigerian National Assembly will need to pass an Act to provide for the licensing of detection of deception examiners – commonly known as polygraph or lie detector operators – and regulation of that profession. The act should set forth the conditions under which persons may be admitted to practice detection of deception with a polygraph, the standards they must observe and the types of polygraph devices that they may henceforth be used lawfully. This is what was done in the State of Illinois. The Nigeria Police Force is advised to make use of two examiners for the lie detector test: one in-house examiner and one external examiner. The external examiner may be from another country in which corruption is not at a high rate, and must be someone of high integrity and professional competence. This measure may reduce the risk of bribery and corruption in the system. It will also bring more integrity and transparency into the system. The external examiner may also carry out “on the job training” with the in-house examiner while the polygraph exercise is going on. The Nigeria Police Force must make a new policy that mandates that all transactions relating to the purchase of polygraph machines must be conducted in an open and fair manner that recognizes the need for the transaction to be done directly with the seller, and not through a sales agent. This policy may help prevent a situation where a corrupt sales agent connives with a corrupt police officer to defraud the police unit. An ongoing approach to screening should be considered for specific positions, as circumstances change, or for a comprehensive review of departmental staff over a period. The Nigeria Police Force should have a policy that mandates that the lie detector test should be taken once in five years by all staff of the Nigeria Police Force. For staff in very sensitive positions, the lie detector test should be taken every three years. This will enable the lie detector policy to be more effective. Let us take, for example, a person passes the lie detector test genuinely without any influence of corruption; there is still a possibility that the person may change over time. The temptation to follow current employees to collect bribes is very high. But if the Nigeria Police Force put a policy in place that mandates every police personnel to take the lie detector test every five years starting from the first five years after recruitment, the cankerworm called corruption may be curbed effectively. Imagine if every police personnel knew that they were going to be asked by an examiner, five years after working, to confirm if they ever collected bribe during the time they served in the police force; most employees will desist from taking bribes or engaging in corrupt acts. The above measure will ensure that current employees who are chosen as examiners for the lie detector tests are fit and proper persons for the job.

This paper focuses on the new lie detector test policy of the Nigeria Police Force. It does not address the other anti-corruption policies of the Nigeria Police Force.

This paper offers a critical analysis of the lie detector test policy of the Nigeria Police Force. It will provide recommendations on how the policy could be strengthened. This is the only paper to adopt this kind of approach.

The purpose of this paper is to undertake an exploratory study on mapping the investment fraud methods and tactics used by scammers against the emerging literature on scam compliance.

Qualitative interviews were conducted with victims of investment fraud supported by the engagement of specialist counsellors and allied health professionals who specialise in scam victim support (including investment fraud).

Investment fraud offending in the cases sampled exhibited a number of dominant offending traits and methodological themes. These included a strong reliance or dependency on legitimate service provisioning on the part of the fraudster and the use of key trust measures to lure the victim. The empirical data revealed the presence of a number of scam compliance influences captured in the literature, including trust, social influence and urgency, as well as others not previously documented that pave the way for further research attention.

The research only examined a sample of investment fraud victim experiences that engaged a national victim support service immediately following detection over a 24 month period.

The research found that offending relied upon the participation of trust-building signals and measures. Legitimate economy participants appear to play a dominant role in enabling investment scam activities, further creating efficiencies for criminals. The offending tended to follow a number of distinct but connected phases. Impacts were influenced by specific offending attributes, such as whether remote access was given to offenders of a victim’s device, as well as the nature of the identity credentials access.

The research has practically applied an emerging view of scam compliance influences and vulnerabilities within an investment fraud context. The study is novel in its thematic analysis of the distinct phases and tactics used by scammers.

The coronavirus (COVID-19) pandemic has led to a surge in demand for health-care facilities, medicines, vaccines and other health-care items. The purpose of this study is to investigate different facets of integrated Green Lean Six Sigma and Industry 4.0 approach in the context of COVID-19 for better healthcare management. Integrating Green Lean Six Sigma (GLSS) and Industry 4.0 (I4.0) has the potential to meet the modern demand of health-care units and also leads to improving the quality of inpatient care with better safety, hygiene and real-time diagnoses. A systematic review has been conducted to determine the tools/techniques, challenges, application areas and potential benefits for the adoption of an integrated GLSS-I4.0 approach within health-care facilities from the perspective of COVID management. Further, a conceptual framework of integrated GLSS-I4.0 has been proposed for better COVID management.

To conduct the literature review, the authors used the preferred reporting items for systematic reviews and meta-analysis and covers relevant papers from the arrival of COVID-19. Based on the systematic understanding of the different facets of the integrated GLSS-I4.0 approach and through insights of experts (academicians and health-care personnel), a conceptual framework is proposed to combat COVID-19 for better detection, prevention and cure.

The systematic review presented here provides different avenues to comprehend the different facets of the integrated GLSS-I4.0 approach in different areas of COVID health-care management. In this study, the proposed framework reveals that the Internet of Things, big data and artificial intelligence are the major constituents of I4.0 technologies that lead to better COVID management. Moreover, integration of I4.0 with GLSS aids during different stages of the COVID management, right from diagnosis, manufacture of items and inpatient and outpatient care of the affected person.

This study provides a significant knowledge database to the practitioners by understanding different tools and techniques of an integrated approach for better COVID management. Moreover, the proposed framework aids to grab day-to-day information from the affected people and ensures reduced hospital stay with better space utilization and the creation of a healthy environment around the patient. This inclusive implementation of the proposed framework will enhance knowledge base in medical areas and provides different novel prospects to combat other medical urgencies.

To the best of the authors’ knowledge, this study is the first of its kind to review different facets of the integrated GLSS-I4.0 approach with a view of the COVID health-care perspective and provides a conceptual framework.