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Objectives: Much research suggests that the general public relies on the popular media as a primary source of information about mental illness. We assessed the broad content of articles relating to mental illness in major Canadian newspapers over a 6-year period. We also sought to assess if such content has changed over time. Methods: We conducted a retrospective analysis of Canadian newspaper coverage from 2005 to 2010. Research assistants used a standardized guide to code 11 263 newspaper articles that mention the terms mental health, mental illness, schizophrenia, or schizophrenic. Once the articles were coded, descriptive statistics were produced for overarching themes and time trend analyses from 2005 to 2010. Results: Danger, violence, and criminality were direct themes in 40% of newspaper articles. Treatment for a mental illness was discussed in only 19% of newspaper articles, and in only 18% was recovery or rehabilitation a significant theme. Eighty-three per cent of articles coded lacked a quotation from someone with a mental illness. We did not observe any significant changes overtime from 2005 to 2010 in any domain measured. Conclusion: There is scope for more balanced, accurate, and informative coverage of mental health issues in Canada. Newspaper articles infrequently reflect the common realities of mental illness phenomenology, course, and outcome. Currently, clinicians may direct patients and family members to other resources for more comprehensive and accurate information about mental illness.

Because of the rapid growth of mobile technology, social media has become an essential platform for people to express their views and opinions. Understanding public opinion can help businesses and political institutions make strategic decisions. Considering this, sentiment analysis is critical for understanding the polarity of public opinion. Most social media analysis studies divide sentiment into three categories: positive, negative, and neutral. The proposed model is a machine-learning application of a classification problem trained on three datasets. Recently, the BERT model has demonstrated effectiveness in sentiment analysis. However, the accuracy of sentiment analysis still needs to be improved. We propose four deep learning models based on a combination of BERT with Bidirectional Long ShortTerm Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms. The study is based on pre-trained word embedding vectors that aid in the model fine-tuning process. The proposed methods are trying to enhance accuracy and check the effect of hybridizing layers of BIGRU and BILSTM on both Bert models (DistilBERT, RoBERTa) for no emoji (text sentiment classifier) and also with emoji cases. The proposed methods were compared to two pre-trained BERT models and seven other models built for the same task using classical machine learning. The proposed architectures with BiGRU layers have the best results.

This article provides evidence-based results regarding current debates on transnationalism. It draws on the content analysis of the 50 most cited (according to the major academic databases and search engines in 2020) and the 50 most recent (published or forthcoming in 2019–2020) articles and/or books on transnationalism. The study analysed the main definitions of transnationalism, identified classification criteria for transnational experience, and reviewed the concept of transnationalism in the studied articles and books. In transnationalism, a broad range of economic, sociocultural, and political cross-border activities and practices, and their various combinations, modify people’s sense of belonging to places; affect their citizenship and nationality; change their aspirations, imagination and decisions in everyday life; and influence their identity. In the studied academic literature, transnationalism was often associated with globalisation, migration, cosmopolitanism, multiculturalism, diaspora, post-migration studies, and internationalism. Transnationalism has an inner processual and in-becoming character, leading to difficulty in giving it a precise and clear theoretical definition. Many studies have shown the need for conceptual academic clarity regarding transnationalism, whether considering it from narrow or broad perspectives. Transnationalism is transformative, and powerful enough to trigger changes in contemporary societies. This article suggests a number of particularly intriguing research fields regarding transnationalism: telecommunications (ICT—Information and Communication Technology/the internet/social media), return migration (aspirations to return, and in relation to telecommunications), as well as the connection between bodies and the law (the incorporation of the body into transnational practices and in relation to the law).

PurposeThe purpose of this paper is to investigate the effects of a particular form of sponsorship disclaimer in sponsored content by social media influencers (SMIs), namely a sponsorship compensation justification disclosure. A sponsorship compensation justification disclosure explains why influencers and brands engage in sponsorship collaborations by providing a normative reason that justifies the existence and dissemination of sponsored content.Design/methodology/approachAn experimental design was used to compare the effects of a sponsorship compensation justification disclosure made by either an influencer or the sponsoring brand, to a simple sponsorship disclosure and a no disclosure control post, on consumers’ responses to a product-review video by a YouTube influencer.FindingsThe paper offers empirical evidence that sponsorship compensation justification generates more positive consumer attitudes toward influencers receiving sponsorship compensation, and increases source and message credibility, compared to a simple sponsorship disclosure.Research limitations/implicationsThe hypotheses were tested on one YouTube video, comprising of a single product category, one SMI and one social media platform. Further studies might replicate the experiment on different product categories and on different social media platforms.Practical implicationsThis empirical study can offer brand communication managers and influencers important information on how to communicate and design sponsorship disclosures to reach-desired responses from consumers.Originality/valueThe study is the first study to empirically demonstrate the effects of this particular type of sponsorship disclosure.

Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning -based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both traffic characterization in which the network traffic is categorized into major classes (e.g., FTP and P2P) and application identification in which identifying end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. The Deep Packet framework employs two deep neural network structures, namely stacked autoencoder (SAE) and convolution neural network (CNN) in order to classify network traffic. Our experiments show that the best result is achieved when Deep Packet uses CNN as its classification model where it achieves recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.

Identification and classification of extremist-related tweets is a hot issue. Extremist gangs have been involved in using social media sites like Facebook and Twitter for propagating their ideology and recruitment of individuals. This work aims at proposing a terrorism-related content analysis framework with the focus on classifying tweets into extremist and non-extremist classes. Based on user-generated social media posts on Twitter, we develop a tweet classification system using deep learning-based sentiment analysis techniques to classify the tweets as extremist or non-extremist. The experimental results are encouraging and provide a gateway for future researchers.

Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of video, audio, text, and images. This is due to the prevalence of novel applications in recent years, such as social media, video sharing, and location based services (LBS), etc. In many multimedia applications, for example, video/image tagging and multimedia recommendation, text classification techniques have been used extensively to facilitate multimedia data processing. In this paper, we give a comprehensive review on feature selection techniques for text classification. We begin by introducing some popular representation schemes for documents, and similarity measures used in text classification. Then, we review the most popular text classifiers, including Nearest Neighbor (NN) method, Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Neural Networks. Next, we survey four feature selection models, namely the filter, wrapper, embedded and hybrid, discussing pros and cons of the state-of-the-art feature selection approaches. Finally, we conclude the paper and give a brief introduction to some interesting feature selection work that does not belong to the four models.

In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of fake news at an incredible pace and scale. In recent researches, many useful methods for fake news detection employ sequential neural networks to encode news content and social context-level information where the text sequence was analyzed in a unidirectional way. Therefore, a bidirectional training approach is a priority for modelling the relevant information of fake news that is capable of improving the classification performance with the ability to capture semantic and long-distance dependencies in sentences. In this paper, we propose a BERT-based (Bidirectional Encoder Representations from Transformers) deep learning approach (FakeBERT) by combining different parallel blocks of the single-layer deep Convolutional Neural Network (CNN) having different kernel sizes and filters with the BERT. Such a combination is useful to handle ambiguity, which is the greatest challenge to natural language understanding. Classification results demonstrate that our proposed model (FakeBERT) outperforms the existing models with an accuracy of 98.90%.

A rapid increase in brain tumor cases mandates researchers for the automation of brain tumor detection and diagnosis. Multi-tumor brain image classification became a contemporary research task due to the diverse characteristics of tumors. Recently, deep neural networks are commonly used for medical image classification to assist neurologists. Vanishing gradient problem and overfitting are the demerits of the deep networks. In this paper, we have proposed a deep network model that uses ResNet-50 and global average pooling to resolve the vanishing gradient and overfitting problems. To evaluate the efficiency of the proposed model simulation has been carried out using a three-tumor brain magnetic resonance image dataset consisting of 3064 images. Key performance metrics have used to analyze the performance of the proposed model and its competitive models. We have achieved a mean accuracy of 97.08% and 97.48% with data augmentation and without data augmentation, respectively. Our proposed model outperforms existing models in classification accuracy.