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The creation and dissemination of fake news can have severe consequences for a company’s brand. Researchers, policymakers and practitioners are eagerly searching for solutions to get us out of the “fake news crisis”. Here, one approach is to use automated tools, such as artificial intelligence (AI) algorithms, to support managers in identifying fake news. The study in this paper demonstrates how AI with its ability to analyze vast amounts of unstructured data, can help us tell apart fake and real news content. Using an AI application, this study examines if and how the emotional appeal, i.e., sentiment valence and strength of specific emotions, in fake news content differs from that in real news content. This is important to understand, as messages with a strong emotional appeal can influence how content is consumed, processed and shared by consumers.

The study analyzes a data set of 150 real and fake news articles using an AI application, to test for differences in the emotional appeal in the titles and the text body between fake news and real news content.

The results suggest that titles are a strong differentiator on emotions between fake and real news and that fake news titles are substantially more negative than real news titles. In addition, the results reveal that the text body of fake news is substantially higher in displaying specific negative emotions, such as disgust and anger, and lower in displaying positive emotions, such as joy.

This is the first empirical study that examines the emotional appeal of fake and real news content with respect to the prevalence and strength of specific emotion dimensions, thus adding to the literature on fake news identification and marketing communications. In addition, this paper provides marketing communications professionals with a practical approach to identify fake news using AI.

The optimal thickness distribution of a two‐span continuous beam is determined with the objectives of minimizing the maximum stress, maximizing the fundamental frequency and frequency separation between adjacent frequencies. The self‐weight of the beam is included in the computations. The multiobjective design problem is solved by using the concept of Pareto optimality. The beam thickness is approximated by constant splines. The stress distribution and the frequencies are determined by the finite element method. The optimization of the beam is carried out by the feasible direction method and by employing a quadratic approximation of the thickness function. Numerical results are given for two‐objective design problems. Optimal trade‐off curves, thickness distributions and stress distributions of optimally designed beams are presented in graphical form. The effects of self‐weight and different design objectives on the thickness distribution are investigated.

The optimal design of a laminated cylindrical shell is obtained with the objectives defined as the maximisation of the axial and torsional buckling loads. The ply angle is taken as the design variable. The symbolic computational software package MATHEMATICA is used in the implementation and solution of the problem. This approach simplifies the computational procedure as well as the implementation of the analysis/optimisation routine. Results are given illustrating the dependence of the optimal fiber angle on the cylinder length and radius. It is shown that a general purpose computer algebra system like MATHEMATICA is well suited to solve small boundary value problems such as structural design optimisation involving composite materials.

The purpose of this paper is to predict the mechanical behavior of a piezoelectric nanoplate under shear stability by taking electric voltage into account in thermal environment.

Simplified first-order shear deformation theory has been used as a displacement field. Modified couple stress theory has been applied for considering small-size effects. An analytical solution has been taken into account for various boundary conditions.

The length scale impact on the results of any boundary conditions increases with an increase in l parameter. The effect of external electric voltage on the critical shear load is more than room temperature effects. With increasing aspect ratio the critical shear load decreases and external electric voltage becomes more impressive. By considering piezoelectric nanoplates, it is proved that the temperature rise cannot become a sensitive factor on the buckling behavior. The length scale parameter has more effect for more flexible boundary conditions than others. By considering nanosize, the consideration has led to much bigger critical load vs macro plate.

In the current paper for the first time the simplified first-order shear deformation theory is used for obtaining governing equations by using nonlinear strains for shear buckling of a piezoelectric nanoplate. The couple stress theory for the first time is applied on the nonlinear first-order shear deformation theory. For the first time, the thermal environment effects are considered on shear stability of a piezoelectric nanoplate.

Cantilever plates subject to axial flow can lose stability by flutter and properties such as viscoelasticity and laminar friction affect dynamic stability. The purpose of the present study is to investigate the dynamic stability of viscoelastic cantilever plates subject to axial flow by using the differential quadrature method.

Equation of motion of the viscoelastic plate is derived by implementing Kelvin-Voigt model of viscoelasticity and applying inverse Laplace transformation. The differential quadrature method is employed to discretize the equation of motion and the boundary conditions leading to a generalized eigenvalue problem. The solution is verified using the existing results in the literature and numerical results are given for critical flow velocities

It is observed that higher aspect ratios lead to imaginary part of third frequency becoming negative and causing single-mode flutter instability. It was found that flutter instability does not occur at low aspect ratios. Moreover the friction coefficient is found to affect the magnitude of critical flow velocity, however, its effect on the stability behaviour is minor.

The effects of various problem parameters on the dynamic stability of a viscoelastic plate subject to axial flow were established. It was shown that laminar friction coefficient of the flowing fluid increases the critical fluid velocity and higher aspect ratios lead to single-mode flutter instability. The effect of increasing damping of viscoelastic material on the flutter instability was quantified and it was found that increasing viscoelasticity can lead to divergence instability.

The purpose of this paper is to present the optimal design of a simply supported variable curvature laminated angle-ply composite panel under uniaxial compression. The objective is to maximize the failure load which is defined as the minimum of the buckling load and the first-ply failure load.

The numerical results presented are obtained using a shear deformable degenerated shell finite element, a brief formulation of which is given. Some verification problems are solved and a convergence study is conducted in order to assess the accuracy of the element. The design procedure is presented and optimization results are given for a simply supported symmetric eight layer angle-ply panel composed of a flat and two cylindrical sections.

The influences of the stacking sequence and panel thickness on optimization are investigated and the effects of various problem parameters on the optimization procedure are discussed.

The paper shows that the load carrying capacity of thicker panels is considerably reduced when the first-ply failure constraint is taken into account.

For an enterprise, it is essential to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality characteristics of the object of communication in order to employ an effective communication strategy. An enterprise needs to obtain the personality information of target or potential customers. However, the traditional method for personality evaluation is extremely costly in terms of time and labor, and it cannot acquire customer personality information without their awareness. Therefore, the manner in which to effectively conduct automated personality predictions for a large number of objects is an important issue. The paper aims to discuss these issues.

The diverse social media that have emerged in recent years represent a digital platform on which users can publicly deliver speeches and interact with others. Thus, social media may be able to serve the needs of automated personality predictions. Based on user data of Facebook, the main social media platform around the world, this research developed a method for predicting personality types based on interaction logs.

Experimental results show that the Naïve Bayes classification algorithm combined with a feature selection algorithm produces the best performance for predicting personality types, with 70-80 percent accuracy.

In this research, the dominance, inducement, submission, and compliance (DISC) theory was used to determine personality types. Some specific limitations were encountered. As Facebook was used as the main data source, it was necessary to obtain related data via Facebook’s API (FB API). However, the data types accessible via FB API are very limited.

This research serves to build a universal model for social media interaction, and can be used to propose an efficient method for designing interaction features.

This research has developed an approach for automatically predicting the personality types of network users based on their Facebook interactions.

This paper aims to understand and document the state of the art in the anti-money laundering (AML) systems literature.

A systematic literature review (SLR) is performed using the Saudi Digital Library. The outputs published as conference proceedings, workshop proceedings, journal articles and books were all considered. The final sample size after omitting out-of-scope selections was 27 documents, which mainly span from 2015 to 2020.

The sample is discussed based on a categorization, which demarcates solutions, machine learning, data sources, evaluation methods, implementation tools, sampling techniques and regions of study.

This SLR could serve as a useful basis for researchers and salient decision-makers, who are seeking to understand the nature and extent of the currently available research into AML systems.

This chapter draws on both theoretical and empirical literature on trust and discusses the role of trust in strategic communication. It also examines the importance of trust for organizational success, the dimensions of trust and distrust and discusses quantifiable proxies to measure trust and distrust on social media. The theoretically driven dimensions of trust and distrust served as a framework to examine how Boeing and Airbus use Twitter to communicate with their stakeholders and publics. 6,926 Twitter messages were examined in the process of content analysis. The following proxies of stakeholder and publics' trust in an organization were identified for Twitter: number of followers, friends and likes; frequency of online activities; length of messages; use of hashtags, links, exclamation and questions marks; and use of specific words and phrases in messages. Two separate lists of words and phrases were created, one for proxies of trust and one for proxies of distrust. In addition, the following trust building actions that organization can engage in on Twitter were identified: listening and engaging in dialogue by following users, mentioning users in messages, replying to enquiries, providing and encouraging feedback.

Owing to the increased accessibility of internet and related technologies, more and more individuals across the globe now turn to social media for their daily dose of news rather than traditional news outlets. With the global nature of social media and hardly any checks in place on posting of content, exponential increase in spread of fake news is easy. Businesses propagate fake news to improve their economic standing and influencing consumers and demand, and individuals spread fake news for personal gains like popularity and life goals. The content of fake news is diverse in terms of topics, styles and media platforms, and fake news attempts to distort truth with diverse linguistic styles while simultaneously mocking true news. All these factors together make fake news detection an arduous task. This work tried to check the spread of disinformation on Twitter.

This study carries out fake news detection using user characteristics and tweet textual content as features. For categorizing user characteristics, this study uses the XGBoost algorithm. To classify the tweet text, this study uses various natural language processing techniques to pre-process the tweets and then apply a hybrid convolutional neural network–recurrent neural network (CNN-RNN) and state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) transformer.

This study uses a combination of machine learning and deep learning approaches for fake news detection, namely, XGBoost, hybrid CNN-RNN and BERT. The models have also been evaluated and compared with various baseline models to show that this approach effectively tackles this problem.

This study proposes a novel framework that exploits news content and social contexts to learn useful representations for predicting fake news. This model is based on a transformer architecture, which facilitates representation learning from fake news data and helps detect fake news easily. This study also carries out an investigative study on the relative importance of content and social context features for the task of detecting false news and whether absence of one of these categories of features hampers the effectiveness of the resultant system. This investigation can go a long way in aiding further research on the subject and for fake news detection in the presence of extremely noisy or unusable data.