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As user-generated content (UGC) is entering the news cycle alongside content captured by news professionals, it is important to detect misleading content as early as possible and avoid disseminating it. The purpose of this paper is to present an annotated dataset of 380 user-generated videos (UGVs), 200 debunked and 180 verified, along with 5,195 near-duplicate reposted versions of them, and a set of automatic verification experiments aimed to serve as a baseline for future comparisons.

The dataset was formed using a systematic process combining text search and near-duplicate video retrieval, followed by manual annotation using a set of journalism-inspired guidelines. Following the formation of the dataset, the automatic verification step was carried out using machine learning over a set of well-established features.

Analysis of the dataset shows distinctive patterns in the spread of verified vs debunked videos, and the application of state-of-the-art machine learning models shows that the dataset poses a particularly challenging problem to automatic methods.

Practical limitations constrained the current collection to three platforms: YouTube, Facebook and Twitter. Furthermore, there exists a wealth of information that can be drawn from the dataset analysis, which goes beyond the constraints of a single paper. Extension to other platforms and further analysis will be the object of subsequent research.

The dataset analysis indicates directions for future automatic video verification algorithms, and the dataset itself provides a challenging benchmark.

Having a carefully collected and labelled dataset of debunked and verified videos is an important resource both for developing effective disinformation-countering tools and for supporting media literacy activities.

Besides its importance as a unique benchmark for research in automatic verification, the analysis also allows a glimpse into the dissemination patterns of UGC, and possible telltale differences between fake and real content.

The purpose of this paper is to develop a software-library in the R programming language that implements the concepts of the interpretive coordinate, interpretive axis and interpretive plane. This allows for the automatic and reliable interpretation of results from the multiple correspondence analysis (MCA) as previously proposed and published. Consequently, the users can seamlessly apply these concepts to their data, both via R commands and a corresponding graphical interface.

Within the context of this study, and through extensive literature review, the advantages of developing software using the Shiny library were examined. This library allows for the development of full-stack applications for R users without the need for knowledge of the corresponding technologies required for the development of complex applications. Additionally, the structural components of a Shiny application were presented, leading ultimately to the proposed software application.

Software utilizing the Shiny library enables nonexpert developers to rapidly develop specialized applications, either to present or to assist in the understanding of objects or concepts that are scientifically intriguing and complex. Specifically, with this proposed application, the users can promptly and effectively apply the scientific concepts addressed in this study to their data. Additionally, they can dynamically generate charts and reports that are readily available for download and sharing.

The proposed package is an implementation of the fundamental concepts of the exploratory MCA method. In the next step, discoveries from the geometric data analysis will be added as features to provide more comprehensive information to the users.

The practical implications of this work include the dissemination of the method’s use to a broader audience. Additionally, the decision to implement it with open-source code will result in the integration of the package’s functions by other third-party user packages.

The proposed software introduces the initial implementation of concepts such as interpretive coordination, the interpretive axis and the interpretive plane. This package aims to broaden and simplify the application of these concepts to benefit stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study.

The purpose of this paper is to create an automatic interpretation of the results of the method of multiple correspondence analysis (MCA) for categorical variables, so that the nonexpert user can immediately and safely interpret the results, which concern, as the authors know, the categories of variables that strongly interact and determine the trends of the subject under investigation.

This study is a novel theoretical approach to interpreting the results of the MCA method. The classical interpretation of MCA results is based on three indicators: the projection (F) of the category points of the variables in factorial axes, the point contribution to axis creation (CTR) and the correlation (COR) of a point with an axis. The synthetic use of the aforementioned indicators is arduous, particularly for nonexpert users, and frequently results in misinterpretations. The current study has achieved a synthesis of the aforementioned indicators, so that the interpretation of the results is based on a new indicator, as correspondingly on an index, the well-known method principal component analysis (PCA) for continuous variables is based.

Two (2) concepts were proposed in the new theoretical approach. The interpretative axis corresponding to the classical factorial axis and the interpretative plane corresponding to the factorial plane that as it will be seen offer clear and safe interpretative results in MCA.

It is obvious that in the development of the proposed automatic interpretation of the MCA results, the authors do not have in the interpretative axes the actual projections of the points as is the case in the original factorial axes, but this is not of interest to the simple user who is only interested in being able to distinguish the categories of variables that determine the interpretation of the most pronounced trends of the phenomenon being examined.

The results of this research can have positive implications for the dissemination of MCA as a method and its use as an integrated exploratory data analysis approach.

Interpreting the MCA results presents difficulties for the nonexpert user and sometimes lead to misinterpretations. The interpretative difficulty persists in the MCA's other interpretative proposals. The proposed method of interpreting the MCA results clearly and accurately allows for the interpretation of its results and thus contributes to the dissemination of the MCA as an integrated method of categorical data analysis and exploration.

The purpose of this paper is to investigate the effect of atmospheric cues encountered in the complex retail environment of pharmacies and, in addition, to test the boundary conditions for the formation of experience-rich emotions. Following this rationale, the research succeeds to partially explain patronage decisions and behavior in pharmacies.

A quantitative methodology was used, using a structured questionnaire. Data were collected using a multistage sampling approach of days of the week and times of each day, seeking responses from customers exiting three, aesthetically diverse types of pharmacies in the wider area of Athens, Greece. Structural equation modeling techniques were used to test the measurement using SmartPLS3 statistical software.

The results primarily confirm the multidimensionality and complexity of atmospherics in pharmacies and the consequent effect on customers’ emotional state. Emotions, amplified by repeat visitation, have both a direct and a mediated effect on customer loyalty while emotional attachment acts as the mediator.

The recent elevation of pharmacies from a mere convenient shopping destination of mostly emergency goods to a shopping alternative for a wide variety of well-being goods and services, necessitates the shift of focus of research from the technical expertise of the pharmacist to the enabling dimensions of the retailing environment. The paper answers this need and test the boundary conditions for the development of experience-rich emotions in retailing. The proposed model indicates the significant impact of atmospherics in customer decision-making even in the complex setting of pharmacies.

Grounded in regulatory mode theory (RMT), this study aims to investigate the impact of managers’ orientation for action (locomotion and assessment) in business-to-business positioning decision-making.

Data are collected using a scenario-based experimental design. Study 1 examines whether interest and involvement in recommending a positioning strategy depends on a manager’s regulatory mode orientation. The impact of such orientations on the likelihood of changing a recommended positioning strategy is the focus of Study 2. The moderating effects of task motivation (expected rewards resulting from a recommendation), market feedback and the line manager’s leadership style are examined.

Both assessment and locomotion are significant determinants of involvement in recommending a positioning strategy. The introduction of motivation as a moderator helps explain differences in level of interest in positioning decision-making. Locomotion, but not assessment, affects the likelihood of changing a recommended positioning strategy. Assessment amplifies the impact of locomotion, while none of the interaction effects between regulatory mode orientation and contextual factors is a significant determinant of changing a positioning strategy.

To the best of the authors’ knowledge, this is the first application of RMT on positioning decision-making. Results from two experiments provide novel insights into the predictive relevance of managers’ preference in terms of involvement with the decision-making process and the likelihood of altering positioning.

Success in export ventures has been linked to managerial capabilities. The purpose of this paper is to examine the influence of exporting motivators on managerial perceptions of UK manufacturing small and medium-sized enterprises (SMEs) by investigating the links between export motivators and decision bias (i.e. predictable behavior).

Based on the Uppsala and resource-based view perspectives (using a sample of the UK’s independent manufacturing SMEs and utilizing a survey, correlation analysis, and factor analysis), this study finds and describes the effect of the most recurrent motivators and clusters of motivators from the literature on the SMEs’ decision to export by investigating the dimensions (research, external, reactive).

This study finds that export motivators can be separated into specific dimensions leading to potential selection bias. In addition, the importance of size, knowledge of foreign markets, and unsolicited orders show an association with the perceptions of motivator stimuli toward specific dimensions (research, external, reactive).

Government policy and SME export strategy need to understand managerial perceptions and bias better in order to allocate resources efficiently toward stimulating exporting.

The literature and empirical work on the topic have been fragmented and conflicting focusing on specific motivators but not necessarily explaining the selection or origin of motivators even less on SMEs. Dimensions have not been taken into account as clusters of motivators.

A global reach in exporting has been linked to profitability. The purpose of this paper is to answer the influence of EU regulations on exporting decisions of UK manufacturing small- and medium-sized firms (SMEs) by investigating the home and host country-based motivators behind SMEs’ choice to export, and export regionally, within the EU.

Contrasting the Uppsala and resource-based view perspectives (using a sample of UK independent manufacturing SMEs and utilizing a survey, correlation analysis and factor analysis), the paper finds and describes the effect of the most recurrent motivators from the literature on the SMEs’ decision to export within the EU or not.

The paper finds that SMEs whose latest international market entry was not in the EU scored significantly higher in the factor scorings for the motivators in the external dimension than participants whose latest entry was in the EU. Several motivators show an association with the choice to export per se. The importance of regionalization to export initiation (and EU membership) within the EU is emphasized in the results.

The sample size is limited.

In the current climate, how can SMEs reduce market research costs for managers by relying solely and proactively on home country and internal advantages and motivators and being more aware of their surroundings? Managers and policymakers can direct their strategy, resources and policy more efficiently according to motivators; internal home country motivators (e.g. strengths of prices of products) direct the SME to overcome inter-regional liability of foreignness, while host country motivators (e.g. legal restrictions in the host country) direct them to regional ventures.

The theoretical and empirical work on the topic, until recently, has been fragmented and inconsistent focusing on specific motivators but not necessarily justifying the selection or origin of variables even less on SMEs.

In the last decades, the demand and use of renewable energies have been increasing. The increase in renewable energies, particularly wind energy, leads to the development and innovation of powerful wind energy converters as well as increased production requirements. Hence, a higher supporting structure is required to achieve higher wind speed with less turbulence. To date, the onshore wind towers with tubular connections are the most used. The maximum diameter of this type of tower is limited by transportation logistics. The purpose of this paper is to propose an alternative wind turbine lattice structure based on half-pipe steel connections.

In this study, a new concept of steel hybrid tower has been proposed. The focus of this work is the development of a lattice structure. Therefore, the geometry of the lattice part of the tower is assessed to decrease the number of joints and bolts. The sections used in the lattice structure are constructed in a polygonal shape. The elements are obtained by cold forming and bolted along the length. The members are connected by gusset plates and preloaded bolts. A numerical investigation of joints is carried out using the finite element (FE) software ABAQUS.

Based on the proposed study, the six “legs” solution with K braces under 45° angle and height/spread ratio of 4/1 and 5/1 provides the most suitable balance between the weight of the supporting structure, number of bolts in joints and reaction forces in the foundations, when compared with four “legs” solution.

In this investigation, the failure modes of elements and joints of an alternative wind turbine lattice structures, as well as the rotation stiffness of the joints, are determined. The FE results show good agreement with the analytical calculation proposed by EC3-1-8 standard.

Examines the fourteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.