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The objective is to develop a more effective model that simplifies and accelerates the news classification process using advanced text mining and deep learning (DL) techniques. A distributed framework utilizing Bidirectional Encoder Representations from Transformers (BERT) was developed to classify news headlines. This approach leverages various text mining and DL techniques on a distributed infrastructure, aiming to offer an alternative to traditional news classification methods.

This study focuses on the classification of distinct types of news by analyzing tweets from various news channels. It addresses the limitations of using benchmark datasets for news classification, which often result in models that are impractical for real-world applications.

The framework’s effectiveness was evaluated on a newly proposed dataset and two additional benchmark datasets from the Kaggle repository, assessing the performance of each text mining and classification method across these datasets. The results of this study demonstrate that the proposed strategy significantly outperforms other approaches in terms of accuracy and execution time. This indicates that the distributed framework, coupled with the use of BERT for text analysis, provides a robust solution for analyzing large volumes of data efficiently. The findings also highlight the value of the newly released corpus for further research in news classification and emotion classification, suggesting its potential to facilitate advancements in these areas.

This research introduces an innovative distributed framework for news classification that addresses the shortcomings of models trained on benchmark datasets. By utilizing cutting-edge techniques and a novel dataset, the study offers significant improvements in accuracy and processing speed. The release of the corpus represents a valuable contribution to the field, enabling further exploration into news and emotion classification. This work sets a new standard for the analysis of news data, offering practical implications for the development of more effective and efficient news classification systems.

This scoping review aims to explore how “food media”, “food messages” and “food content” are referred to in scholarly writing to enhance a shared understanding and comparability.

Following the PRISMA, ScR-guidelines, four scientific databases were screened on published manuscripts in academic journals, books and doctoral theses mentioning food media, content and messages within the prevalent meaning as in human communication.

Of the 376 included manuscripts, only a small minority (n = 7) provided a conclusive definition of at least one of the three earlier-mentioned concepts; 40 others elucidated some aspects of food media, messages or content; however, they emphasized different and, sometimes even, contrasting aspects. In addition, the review explores in which disciplines the manuscripts mentioning food media, messages or content occur, which methodologies are used and what target groups and media are most common.

Based on this aggregated information, a definition of food media, messages and content is proposed, aiming to enhance the comparability of diverse academic sources. This contribution invites scholars to critically reflect on the included media and content types when comparing studies on food media, messages or content.