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Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.

The study aims to provide a critical review of the extent to which digital technologies are likely to replace human labour, the exponential rise in the amount of work to be done and how far distinctively human skills are future-proofed and therefore likely to be in short supply. It reviews the evidence for a permanent switch to home and remote working enabled by emerging technologies. It assesses the business, digital and labour strategies of work organisations and the promise and challenges from a dominant trend towards a digitally enabled flexible labour model.

A critical review of 1020 plus case studies and the extant literature was carried out.

The relationship between emerging technologies and work is widely misunderstood, and there are major qualifiers to the idea of an overwhelming tsunami of technology drastically reducing headcounts globally. Distinctive human skills remain valuable, the amount of work to be done is increasing exponentially and automation is becoming more a coping than a labour replacement mechanism. Moves to a hybrid digitalised flexible labour model are promising but not if short-term, and if the challenges they represent are not managed well.

The main limitation is that we are making projections into the future, though we are drawing on a lot of different sources and evidence and past data projected into the future.

The problem is not labour displacement but large skills shortages that will slow down the speed of technology adoption. Skills development is vital, as is the taking of long-term perspectives towards the management of hybrid, flexible working based on human-machine interactions.

Organisations need to revitalise their training and development and labour management models. Governments and intermediary institutions need to manage transition states if the skills required to gain economic growth are to be available, and to ensure that large labour pools do not get bypassed from not having requisite skills.

The study offers a more subtle and complex perspective on the emerging evidence about the future of technology and work.

To elaborate the picture of credibility assessment by examining how participants of online discussion evaluate the informational credibility of conspiracy theories.

Descriptive quantitative analysis and qualitative content analysis of 2,663 posts submitted to seven Reddit threads discussing a conspiracy operation, that is, the damage of the Nord Stream gas pipelines in September 2022. It was examined how the participants of online discussion assess the credibility of information constitutive of conspiracy theories speculating about (1) suspected actors responsible for the damage, (2) their motives and (3) the ways in which the damage was made. The credibility assessments focussed on diverse sources offering information about the above three factors.

The participants assessed the credibility of information by drawing on four main criteria: plausibility of arguments, honesty in argumentation, similarity to one's beliefs and provision of evidence. Most assessments were negative and indicated doubt about the informational believability of conspiracy theories about the damage. Of the information sources referred to in the discussion, the posts submitted by fellow participants, television programmes and statements provided by governmental organizations were judged most critically, due to implausible argumentation and advocacy of biased views.

As the study focuses on a sample of posts dealing with conspiracy theories about a particular event, the findings cannot be generalized to concern the informational credibility conspiracy narratives.

The study pioneers by providing an in-depth analysis of the nature of credibility assessments by focussing on information constitutive of conspiracy theories.

This paper aims to consider the relationship between urban events and urban public space, asking whether cities have enough space for events and whether events have enough space in cities.

Policy analysis surrounding events and festivals in the Netherlands is used to understand the dynamics of urban events, supported by content analysis of policy documents. A vignette of event space struggles in Amsterdam illustrates the contradictions of the event/space relationship.

The research identifies a policy shift in the Netherlands towards urban events from expansive, festivalisation strategies to defensive, NIMBYist policies. It exposes contradictions between protecting space as a living resource and the exploitation of space for regenerative purposes. Three future scenarios for urban events are outlined: conflict and competition, growth and harmony and digitalisation and virtualisation.

Develops scenarios for the future relationship between events and urban space.

Provides an analysis of the recursive spatial implications of the growth of the events sector for cities and the growth of cities for events.

We study five exogenous shocks: climate, violence, price hikes, spoilage and the COVID-19 lockdown. We analyze the association between these shocks and trader characteristics, reflecting trader vulnerability.

Using primary survey data on 1,100 Nigerian maize traders for 2021 (controlling for shocks in 2017), we use probit models to estimate the probabilities of experiencing climate, violence, disease and cost shocks associated with trader characteristics (gender, size and region) and to estimate the probability of vulnerability (experiencing severe impacts).

Traders are prone to experiencing more than one shock, which increases the intensity of the shocks. Price shocks are often accompanied by violence, climate and COVID-19 shocks. The poorer northern region is disproportionately affected by shocks. Northern traders experience more price shocks while Southern traders are more affected by violence shocks given their dependence on long supply chains from the north for their maize. Female traders are more likely to experience violent events than men who tend to be more exposed to climate shocks.

The data only permit analysis of the general degree of impact of a shock rather than quantifying lost income.

This paper is the first to analyze the incidence of multiple shocks on grain traders and the unequal distribution of negative impacts. It is the first such in Africa based on a large sample of grain traders from a primary survey.

Social media has made people better informed but also easier to manipulate. By using literature review and observing social media, the authors found a problem about echo chamber effect. The purpose of this paper is to know how the echo chamber affects the people who consume political news and the role of media diversity in it.

To conduct this study, the authors used a structured questionnaire on the Qualtrics platform to collect data from 183 participants. The authors collected data using a simple random technique. This study is based on the cross-sectional survey; the data collection period is from October to November 2023. The authors used the SPSS software to analyze the relationships between the variables and test the hypothesis.

This study found that, echo chamber is not affected by media diversity. Because of increased political interest, people will be less influenced by echo chambers. In addition, demographic factors affect political interest. People use search engines and social media sites instead of political websites when it comes to the consumption of political news online. People like to communicate with individuals who hold conflicting political views.

Researchers have not yet been able to gain a clear understanding of whether users are in an echo chamber or not and how they are interacting in that environment. Research on this topic is still going on from different perspectives. This study helped to clarify whether or not more media consumption will affect echo chambers. The possibility of being trapped in an echo chamber exists whether we use a single medium or a variety of media. The novelty of this study lies in the use of the echo chamber scale to investigate a thorough understanding of this word through the use of many factors.

In 2022, the International Network for Quality Assurance (QA) Agencies in Higher Education (INQAAHE) published the new guidelines by adding three QA modules in response to the changing higher education landscape. The paper aims to investigate the transformative focus of quality assurance in higher education globally as well as Asian response to three new QA modules according to the INQAAHE ISGs.

The research conducted a quantitative approach for data collection. An on-line survey was conducted to perceive QA practices, perceptions toward new emerging QA modules and challenges encountered. In total, there were 26 responses from 18 territories with 22 QA agencies. A total of 13 out of them have a national qualifications framework in place.

Three are three major findings in the study. First, national policy and criteria and standards in distance education have been developed in the majority of Asian nations. Second, non-signatories of the Tokyo Convention had a higher proportion of having related policies, regulations and criteria in CBHE and distance education. Third, national policies and regulations; and lack of professional staff are two common challenges implementing QA in new types of providers.

The findings are of value for policymakers, QA agencies and universities to advocate the new QA model as a systematic approach in response to changing higher education landscape in the post pandemic era.

The study aims to determine whether a relationship exists between the potential significance of corporate controversies for stakeholders and how organisations respond to them in their annual and sustainability reports.

This paper employs content analysis on annual and sustainability reports of 48 listed companies from the Refinitiv database. The logit regression was used to estimate the model.

The study revealed that the main factors increasing the probability of a controversial issue being addressed in a corporate report are the controversy’s potential significance, companies’ financial performance and lawsuits.

Our study has three major limitations. These are a relatively small sample of companies and reports, focusing on disclosures made in corporate reports and omitting other channels of communication, for example, social media, and a certain amount of subjectivity in the process of coding information.

Former studies show that corporations face a serious risk of their hypocritical strategies becoming too evident for stakeholder groups. Our findings suggest that the risk is already materialising and may undermine the idea of CSR and sustainability reporting.

Our research focuses on high-profile adverse incidents widely reported in the media, the omission of which from corporate reports seems to constitute a particular case of organised hypocrite. It also demonstrates that companies use an impression management strategy to defuse adverse publicity and that major controversies cause minor ones to be omitted from their reports.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.