Journal articles: 'News classification'

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Blackman, Michael. "Classification News." World Patent Information 33, no. 3 (September 2011): 294. http://dx.doi.org/10.1016/j.wpi.2011.04.010.

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Юдина, И., I. Yudina, Д. Косяков, D. Kosyakov, Е. Базылева, E. Bazyleva, З. Вахрамеева, Z. Vahrameeva, О. Федотова, and O. Fedotova. "On the Classification of Scientific News Information." Scientific Research and Development. Modern Communication Studies 7, no. 5 (September 25, 2018): 16–21. http://dx.doi.org/10.12737/article_5b9f9bf14d1cc7.47427505.

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The present research aims to study modern approaches to the classification of news information in general, and of science news in particular. Based on the analysis of Russian scientific publications and information systems, several classifications used to systematize news (mostly universal) are described. The article proposes the author’s classification of scientific news as an element of modern scientific communication system. This classification can potentially be used as another tool for assessing the effectiveness of scientific institution research results.

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Ahmad, Malik Shahzad, and Muhammad Azhar Bhatti. "News Location Classification." iRASD Journal of Computer Science and Information Technology 2, no. 1 (December 31, 2021): 52–62. http://dx.doi.org/10.52131/jcsit.2021.0201.0010.

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Every day there are a lot of things that happen around the world. There are various ways to record every event that is occurring around the world, such as news, blogs, and articles. Over the past few years, there are multiple news available on every event that has occurred. It adds to the size of information that is available for human beings to consume. People are, moving from paper-based newspapers to digital newspapers to get their daily feed of news and digitization has a role to play in this behaviour. These days every person is preoccupied with a lot of work, online and offline, as mentioned earlier the amount of information is being increased with every passing day. For this reason, people are only interested in news that match their interests. A large amount of data in the form of text is available online, hence its classification based on its hidden features can lead to the better recommendation of news to individuals. In this research work, we have used focus area and temporal features to classify news using a Convolutional Neural Network (CNN). The results of the proposed methodology in the form of precision, accuracy, recall, and F1-Score show that these features indeed can be used for recommender systems.

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Blackman, Michael. "News on classification." World Patent Information 35, no. 3 (September 2013): 250–51. http://dx.doi.org/10.1016/j.wpi.2013.05.005.

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Blackman, Michael. "News on classification." World Patent Information 35, no. 4 (December 2013): 328–29. http://dx.doi.org/10.1016/j.wpi.2013.07.004.

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Nagy, Kitti, and Jozef Kapusta. "Improving fake news classification using dependency grammar." PLOS ONE 16, no. 9 (September 14, 2021): e0256940. http://dx.doi.org/10.1371/journal.pone.0256940.

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Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentences of the dataset and based on them the importance of each word within the sentence was determined. This information about the importance of words in sentences was utilized to create the input vectors for classifications. The paper aims to find out whether it is possible to use the dependency grammar to improve the classification of fake news. We compared these methods with the TfIdf method. The results show that it is possible to use the dependency grammar information with acceptable accuracy for the classification of fake news. An important finding is that the dependency grammar can improve existing techniques. We have improved the traditional TfIdf technique in our experiment.

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Demirsoz, Orhan, and Rifat Ozcan. "Classification of news-related tweets." Journal of Information Science 43, no. 4 (June 1, 2016): 509–24. http://dx.doi.org/10.1177/0165551516653082.

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It is important to obtain public opinion about a news article. Microblogs such as Twitter are popular and an important medium for people to share ideas. An important portion of tweets are related to news or events. Our aim is to find tweets about newspaper reports and measure the popularity of these reports on Twitter. However, it is a challenging task to match informal and very short tweets with formal news reports. In this study, we formulate this problem as a supervised classification task. We propose to form a training set using tweets containing a link to the news and the content of the same news article. We preprocess tweets by removing unnecessary words and symbols and apply stemming by means of morphological analysers. We apply binary classifiers and anomaly detection to this task. We also propose a textual similarity-based approach. We observed that preprocessing of tweets increases accuracy. The textual similarity method obtains results with the highest recognition rate. Success increases in some cases when report text is used with tweets containing a link to the news report within the training set of classification studies. We propose that this study, which is made directly in consideration of tweet texts that measure the trends of national newspaper reports on social media, has a higher significance when compared to Twitter analyses made by using a hashtag. Given the limited number of scientific studies on Turkish tweets, this study makes a contribution to the literature.

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Dunne, Edward, and Klaus Hulek. "Mathematics Subject Classification 2020." EMS Newsletter 2020-3, no. 115 (March 3, 2020): 5–6. http://dx.doi.org/10.4171/news/115/2.

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MAHAJAN, SHWETA. "News Classification Using Machine Learning." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 5 (May 31, 2021): 23–27. http://dx.doi.org/10.17762/ijritcc.v9i5.5464.

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There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorithm with some word vectorizing techniques in order to get best result. The results which we got that have been compared on different parameters like Precision, Recall, F1 Score, accuracy for performance improvement.

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Majeed, Fiaz, Muhammad Waqas Asif, Muhammad Awais Hassan, Syed Ali Abbas, and M. Ikramullah Lali. "Social Media News Classification in Healthcare Communication." Journal of Medical Imaging and Health Informatics 9, no. 6 (August 1, 2019): 1215–23. http://dx.doi.org/10.1166/jmihi.2019.2735.

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The trend of news transmission is rapidly shifting from electronic media to social media. Currently, news channels in general, while health news channels specifically send health related news on social media sites. These news are beneficial for the patients, medical professionals and the general public. A lot of health related data is available on the social media that may be used to extract significant information and present several predictions from it to assist physicians, patients and healthcare organizations for decision making. However, A little research is found on health news data using machine learning approaches, thus in this paper, we have proposed a framework for the data collection, modeling, and visualization of the health related patterns. For the analysis, the tweets of 13 news channels are collected from the Twitter. The dataset holds approximately 28k tweets available under 280 hashtags. Furthermore, a comprehensive set of experiments are performed to extract patterns from the data. A comparative analysis is carried among the baseline method and four classification algorithms which include Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (J48). For the evaluation of the results, the standard measures accuracy, precision, recall and f-measure have been used. The results of the study are encouraging and better than the other studies of such kind.

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Hassan, Awring Falah. "Analysis of BBC News by Applying Classification Algorithms." Journal of Advanced Research in Dynamical and Control Systems 12, no. 1 (February 13, 2020): 148–52. http://dx.doi.org/10.5373/jardcs/v12i1/20201023.

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Dilrukshi, Inoshika, and Kasun de Zoysa. "A Feature Selection Method for Twitter News Classification." International Journal of Machine Learning and Computing 4, no. 4 (2014): 365–70. http://dx.doi.org/10.7763/ijmlc.2014.v4.438.

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Madhuravani, K., Narsin Vamshika, Bachu Akhila, Varikuppala Praveen Kumar, and Singireddy Vaarshik Reddy. "Fake News Classification Model Using Machine Learning." YMER Digital 21, no. 05 (May 16, 2022): 786–95. http://dx.doi.org/10.37896/ymer21.05/90.

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The World Wide Web's introduction, as well as the rapid adoption of social media platforms (such as Facebook and Twitter), paved the way for unprecedented levels of knowledge distribution in human history. Certain entities, however, take advantage of such platforms, sometimes for monetary gain and sometimes for the purpose of promoting prejudiced viewpoints, changing mindsets, and disseminating satire or ridiculousness. This phenomenon is known as fake news. Because the spread of fake news can have serious consequences, such as election rigging and widening political divides, developing methods to detect fake news content is critical. A computerised classification system is used to classify a wide range of things. Fake news is a term used to describe this occurrence. Given the serious consequences of fake news, such as swaying elections and widening political divides, developing methods for detecting fake news content is critical. It is difficult to automate the classification of a text article as misinformation or disinformation. We suggest employing a machine learning ensemble approach for automatic classification in this paper. Our research looks into various textual properties such as word counts, term frequency, and inverse document frequency that can be used to distinguish between fake and real content. We train a Passive-Aggressive Classifier and evaluate its performance on real-world datasets using these properties. The datasets will be used in part to train and part to test the classifiers. It is possible to determine whether the news is fake or not by testing the different features of the datasets on the classifier.

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Kaur, Gurmeet, and Karan Bajaj. "News Classification using Neural Networks." Communications on Applied Electronics 5, no. 1 (May 24, 2016): 42–45. http://dx.doi.org/10.5120/cae2016652224.

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Ghosh, Souvick, and Chirag Shah. "Towards automatic fake news classification." Proceedings of the Association for Information Science and Technology 55, no. 1 (January 2018): 805–7. http://dx.doi.org/10.1002/pra2.2018.14505501125.

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Jain, Rachna, Deepak Kumar Jain, Dharana, and Nitika Sharma. "Fake News Classification: A Quantitative Research Description." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 1 (January 31, 2022): 1–17. http://dx.doi.org/10.1145/3447650.

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Social media can render content circulating to reach millions with a knack to influence people, despite the questionable authencity of the facts. Internet sources are the most convenient and easy approach to obtain any information these days. Fake news has become the topic of interest for academicians and the rest of society. This kind of propaganda has the power to influence the general perception, offering political groups the ability to control the results of democratic affairs such as elections. Automatic identification of fake news has emerged as one of the significant problems due to the high risks involved. It is challenging in a way because of the complexity levels of accurately interpreting the data. An extensive search has already been performed on English language news data. Our work presents a comparative analysis of fake news classifiers on the low resource Bengali language ‘ban fake news’ dataset from Kaggle. The analysis presented compares deep learning techniques such as LSTM (Long short-term Memory) and BiLSTM (Bi-directional Long short-term Memory) and machine learning methods like Naive Bayes, Passive Aggressive Classifier (PAC), and Random Forest. The comparison has been drawn based on classification metrics such as accuracy, precision, recall, and F1 score. The deep learning method BiLSTM shows 55.92% accuracy while Random Forest, in contrast, has outperformed all the other methods with an accuracy of 62.37%. The work presented in this paper sets a basis for researchers to select the optimum classifiers for their approach towards fake news detection.

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Zhu, Yunlong. "Research on News Text Classification Based on Deep Learning Convolutional Neural Network." Wireless Communications and Mobile Computing 2021 (December 8, 2021): 1–6. http://dx.doi.org/10.1155/2021/1508150.

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Aiming at the problems of low classification accuracy and low efficiency of existing news text classification methods, a new method of news text classification based on deep learning convolutional neural network is proposed. Determine the weight of the news text data through the VSM (Viable System Model) vector space model, calculate the information gain of mutual information, and determine the characteristics of the news text data; on this basis, use the hash algorithm to encode the news text data to calculate any news. The spacing between the text data realizes the feature preprocessing of the news text data; this article analyzes the basic structure of the deep learning convolutional neural network, uses the convolutional layer in the convolutional neural network to determine the change value of the convolution kernel, trains the news text data, builds a news text classifier of deep learning convolutional neural network, and completes news text classification. The experimental results show that the deep learning convolutional neural network can improve the accuracy and speed of news text classification, which is feasible.

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Utari, Muhammad Ichwan, and Henny Medyawati. "CLASSIFICATION OF NEWS TYPES BY IMPLEMENTING ENHANCED CONFIX STRIPPING STEMMER." International Journal of Engineering Technologies and Management Research 6, no. 5 (March 25, 2020): 135–41. http://dx.doi.org/10.29121/ijetmr.v6.i5.2019.380.

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News has become a community need in the world. Managing a lot of news articles is not easy and takes a long time. Indonesia has various types of media platforms that display news, one of which is an online news portal. Automation systems that are capable of managing and grouping Indonesian language news articles are needed. This study designed and built a web-based application to classify types of Indonesian language news articles by implementing the Enhanced Confix Stripping Stemmer algorithm. The categories used in the system are entertainment, lifestyle, sports, technology, and economics. The data used is secondary data quoted from 2 online news portals in Indonesia. The system development method used is Rapid Application Development. The data used for testing amounts to 30 news. The average results obtained from the system accuracy test are 63%. This shows that the system performance for the classification of news types is good. The number of words in a news article is very influential during the classification process.

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Sun, Ningfeng, and Chengye Du. "News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model." Complexity 2021 (June 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/8064579.

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This paper uses the database as the data source, using bibliometrics and visual analysis methods, to statistically analyze the relevant documents published in the field of text classification in the past ten years, to clarify the development context and research status of the text classification field, and to predict the research in the field of text classification priorities and research frontiers. Based on the in-depth study of the background, research status, related theories, and developments of online news text classification, this article analyzes the annual publication trend, subject distribution, journal distribution, institution distribution, author distribution, highly cited literature analysis, and research hotspots. Forefront and other aspects clarify the development context and research status of the text classification field and provide a theoretical reference for the further development of the text classification field. Then, on the basis of systematic research on text classification, deep learning, and news text classification theories, a deep learning-based network news text classification model is constructed, and the function of each module is introduced in detail, which will help the future news text classification of application and improvement provide theoretical basis. On the basis of the predecessors, this article separately studied and improved the neural network model based on the convolutional neural network, cyclic neural network, and attention mechanism and merged the three models into one model, which can obtain local associated features and contextual features and highlight the role of keywords. Finally, experiments are used to verify the effectiveness of the model proposed in this paper and compared with traditional text classification to prove the superiority of the network news text classification based on deep learning proposed in this paper. This article aims to study the internal connection between news comments and the number of votes received by news comments, and through the proposed model, the number of votes for news comments can be predicted.

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Zhang, Menghan. "Applications of Deep Learning in News Text Classification." Scientific Programming 2021 (August 5, 2021): 1–9. http://dx.doi.org/10.1155/2021/6095354.

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The advancement in technology is taking place with an accelerating pace across the globe. With the increasing expansion and technological advancement, a vast volume of text data are generated everyday, in the form of social media platform, websites, company data, healthcare data, and news. Indeed, it is a difficult task to extract intriguing patterns from the text data, such as opinions, summaries, and facts, having varying length. Because of the problems of the length of text data and the difficulty of feature value extraction in news, this paper proposes a news text classification method based on the combination of deep learning (DL) algorithms. In order to classify the text data, the earlier approaches use a single word vector to express text information and only the information of the relationship between words were considered, but the relationship between words and categories was ignored which indeed is an important factor for the classification of news text. This paper follows the idea of a customized algorithm which is the combination of DL algorithms such as CNN, LSTM, and MLP and proposes a customized DCLSTM-MLP model for the classification of news text data. The proposed model is expressed in parallel with word vector and word dispersion. The relationship among words is represented by the word vector as an input of the CNN module, and the relationship between words and categories is represented by a discrete vector as an input of the MLP module in order to realize comprehensive learning of spatial feature information, time-series feature information, and relationship between words and categories of news text. To check the stability and performance of the proposed method, multiple experiments were performed. The experimental results showed that the proposed method solves the problems of text length, difficulty of feature extraction in the news text, and classification of news text in an effective way and attained better accuracy, recall rate, and comprehensive value as compared to the other models.

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Pelicon, Andraž, Marko Pranjić, Dragana Miljković, Blaž Škrlj, and Senja Pollak. "Zero-Shot Learning for Cross-Lingual News Sentiment Classification." Applied Sciences 10, no. 17 (August 29, 2020): 5993. http://dx.doi.org/10.3390/app10175993.

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In this paper, we address the task of zero-shot cross-lingual news sentiment classification. Given the annotated dataset of positive, neutral, and negative news in Slovene, the aim is to develop a news classification system that assigns the sentiment category not only to Slovene news, but to news in another language without any training data required. Our system is based on the multilingual BERTmodel, while we test different approaches for handling long documents and propose a novel technique for sentiment enrichment of the BERT model as an intermediate training step. With the proposed approach, we achieve state-of-the-art performance on the sentiment analysis task on Slovenian news. We evaluate the zero-shot cross-lingual capabilities of our system on a novel news sentiment test set in Croatian. The results show that the cross-lingual approach also largely outperforms the majority classifier, as well as all settings without sentiment enrichment in pre-training.

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Ali Ramdhani, Muhammad, Dian Sa’adillah Maylawati, and Teddy Mantoro. "Indonesian news classification using convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 2 (August 1, 2020): 1000. http://dx.doi.org/10.11591/ijeecs.v19.i2.pp1000-1009.

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<span>Every language has unique characteristics, structures, and grammar. Thus, different styles will have different processes and result in processed in Natural Language Processing (NLP) research area. In the current NLP research area, Data Mining (DM) or Machine Learning (ML) technique is popular, especially for Deep Learning (DL) method. This research aims to classify text data in the Indonesian language using Convolutional Neural Network (CNN) as one of the DL algorithms. The CNN algorithm used modified following the Indonesian language characteristics. Thereby, in the text pre-processing phase, stopword removal and stemming are particularly suitable for the Indonesian language. The experiment conducted using 472 Indonesian News text data from various sources with four categories: ‘hiburan’ (entertainment), ‘olahraga’ (sport), ‘tajuk utama’ (headline news), and ‘teknologi’ (technology). Based on the experiment and evaluation using 377 training data and 95 testing data, producing five models with ten epoch for each model, CNN has the best percentage of accuracy around 90,74% and loss value around 29,05% for 300 hidden layers in classifying the Indonesian News data.</span>

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Srivastava, Anurag. "Fake News Classification Using Outliner Detection and Trend Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3452–54. http://dx.doi.org/10.22214/ijraset.2021.37123.

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In modern era fake news is one of the major causes for disrupted social harmony, impact of fake news can lead to various unforeseen situations and thus affect the society as a whole. This paper proposed the use of anomaly detection and trend analysis for detecting fake news.

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AHMED, Kashif, Mubashir ALI, Shehzad KHALID, and Muhammad KAMRAN. "Framework for Urdu News Headlines Classification." Journal of Applied Computer Science & Mathematics 10, no. 1 (2016): 17–21. http://dx.doi.org/10.4316/jacsm.201601002.

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Santos, António Paulo, Carlos Ramos, and Nuno C. Marques. "Sentiment Classification of Portuguese News Headlines." International Journal of Software Engineering and Its Applications 9, no. 9 (September 30, 2015): 9–18. http://dx.doi.org/10.14257/ijseia.2015.9.9.02.

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Rijvordt, Wouter, Frederik Hogenboom, and Flavius Frasincar. "Ontology-Driven News Classification with Aethalides." Journal of Web Engineering 18, no. 7 (2019): 627–54. http://dx.doi.org/10.13052/jwe1540-9589.1873.

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ASHWINI S., MANE. "NEWS VIDEO CONCEPT BASED EVENT CLASSIFICATION." i-manager’s Journal on Image Processing 4, no. 3 (2017): 22. http://dx.doi.org/10.26634/jip.4.3.13924.

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Singh, Amritpal, and Sunil Kumar Chhillar. "News Category Classification Using Distinctive Bag of Words and ANN Classifier." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 311. http://dx.doi.org/10.23956/ijermt.v6i6.288.

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Category classification, for news, is a multi-label text classification problem. The goal is to assign one or more categories to a news article. A standard technique in multi-label text classification is to use a set of binary classifiers. For each category, a classifier is used to give a “yes” or “no” answer on if the category should be assigned to a text. Some of the standard algorithms for text classification that are used for binary classifiers include Naive Bayesian Classifiers, Support Vector Machines, artificial neural networks etc. In this distinctive bag of words have been used as feature set based on high frequency word tokens found in individual category of news. The algorithm presented in this work is based on a keyword extraction algorithm that is capable of dealing with English language in which different news categories i.e. Business, entertainment, politics, sports etc. has been considered. Intra-class news classification has been carried out in which Cricket and Football in sports category has been selected to verify the performance of the algorithm. Experimental results shows high classification rate in describing category of a news document.

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Yazdani, Sepideh Foroozan, Masrah Azrifah Azmi Murad, Nurfadhlina Mohd Sharef, Yashwant Prasad Singh, and Ahmed Razman Abdul Latiff. "Sentiment Classification of Financial News Using Statistical Features." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 03 (February 2017): 1750006. http://dx.doi.org/10.1142/s0218001417500069.

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Sentiment classification of financial news deals with the identification of positive and negative news so that they can be applied in decision support systems for stock trend predictions. This paper explores several types of feature spaces as different data spaces for sentiment classification of the news article. Experiments are conducted using [Formula: see text]-gram models unigram, bigram and the combination of unigram and bigram as feature extraction with traditional feature weighting methods (binary, term frequency (TF), and term frequency-document frequency (TF-IDF)), while document frequency (DF) was used in order to generate feature spaces with different dimensions to evaluate [Formula: see text]-gram models and traditional feature weighting methods. We performed some experiments to measure the classification accuracy of support vector machine (SVM) with two kernel methods of Linear and Gaussian radial basis function (RBF). We concluded that feature selection and feature weighting methods can have a substantial role in sentiment classification. Furthermore, the results showed that the proposed work which combined unigram and bigram along with TF-IDF feature weighting method and optimized RBF kernel SVM produced high classification accuracy in financial news classification.

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Aprilius, William. "Klasifikasi Artikel Berita Online Sederhana dengan Menggunakan Struktur Kategori Wikipedia." Jurnal ULTIMA Computing 6, no. 1 (June 1, 2014): 14–17. http://dx.doi.org/10.31937/sk.v6i1.290.

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The growth of information and communication technology makes the electronic news portal as a source of information. It makes the increasing numbers of online news articles that need to be classified. The classification is done to facilitate the users to access news. This paper proposes a simple method of classification of online news articles into categories. This method uses Wikipedia Bahasa Indonesia as a source of external knowledge and consists of 6 steps. In general, this method works by exploiting the structure of categories in Wikipedia, then check for the existence of entities of a news article in Wikipedia articles. This paper is an early stage of the research to be conducted and the proposed method has not been implemented. This makes the researchers have not been able to draw conclusions with regard to the method proposed. Index Terms—news classification, text classification, Wikipedia

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Xu, Xiao, LiJuan Wang, RuFan Liu, and TianYu Xu. "Deep learning based news text classification software design." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012067. http://dx.doi.org/10.1088/1742-6596/2031/1/012067.

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Abstract New technologies such as artificial intelligence have developed at a rapid pace in recent years and are increasingly being used in the process of managing news in bulk. The development of deep learning has facilitated unprecedented progress in the field of computing and has opened our eyes to the possibility of using AI for news text classification. In this paper, based on the system requirements analysis, we describe the process of functional modules arising from the requirements analysis, design the internal details of functional modules, including algorithms and detailed principles, and finally obtain a prototype of news text classification software, which results in the pre-design expectations. The research in this paper makes the system development work more concrete, while providing software users, software developers, and analysts and testers with a unified and comprehensive understanding of the system’s functional implementation.

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Selakovic, Marko, Anna Tarabasz, and Monica Gallant. "Typology of Business-Related Fake News Online: A Literature Review." GATR Journal of Management and Marketing Review 5, no. 4 (December 22, 2020): 234–43. http://dx.doi.org/10.35609/jmmr.2020.5.4(5).

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Objective – This review paper discusses the emergence of scholarly articles related to the typology and classification of fake news and offers solutions for identified gaps, such as unstandardized terminology and unstandardized typology in the field of fake news-related research. Typology of fake news is a critical topic nowadays: recently emerged fake news needs to be categorized and analyzed in a structured manner in order to respond appropriately. Methodology/Technique – Based on the systematic review of literature identified in scientific databases, different typologies of fake news have been identified and a new typology of business-related fake news online has been proposed. New typology of business-related fake news online is based on factors such as level of facticity, intention to deceive and financial motivation. Findings and novelty – Content analysis of 326 articles containing terms related to the typology of fake news and classification of fake news indicates that the term “typology of fake news” is predominantly used in management, marketing and communications research, while the term “classification of fake news” is predominantly used in the information technology research. The content analysis also indicates the recent emergence of the topic of typology and classification of fake news in academic research, revealing that all articles related to these topics have been published on or after 2016. In addition to the contribution by presenting comprehensive typology of business-related fake news online, this paper also provides recommendations for future research and improvements related to the typology of fake news, emphasizing business-related fake news and fake news spread in the digital space. Type of Paper: Review JEL Classification: M31, M39. Keywords: Fake News; Crisis Communications; Online Communications; Digital Marketing; Management Research; Marketing Research Reference to this paper should be made as follows: Selakovic, M; Tarabasz, A; Gallant, M. (2020). Typology of Business-Related Fake News Online: A Literature Review, J. Mgt. Mkt. Review 5(4) 234 – 243. https://doi.org/10.35609/jmmr.2020.5.4(5)

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Buzea, Marius Cristian, Stefan Trausan-Matu, and Traian Rebedea. "Automatic Fake News Detection for Romanian Online News." Information 13, no. 3 (March 14, 2022): 151. http://dx.doi.org/10.3390/info13030151.

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This paper proposes a supervised machine learning system to detect fake news in online sources published in Romanian. Additionally, this work presents a comparison of the obtained results by using recurrent neural networks based on long short-term memory and gated recurrent unit cells, a convolutional neural network, and a Bidirectional Encoder Representations from Transformers (BERT) model, namely RoBERT, a pre-trained Romanian BERT model. The deep learning architectures are compared with the results achieved by two classical classification algorithms: Naïve Bayes and Support Vector Machine. The proposed approach is based on a Romanian news corpus containing 25,841 true news items and 13,064 fake news items. The best result is over 98.20%, achieved by the convolutional neural network, which outperforms the standard classification methods and the BERT models. Moreover, based on irony detection and sentiment analysis systems, additional details are revealed about the irony phenomenon and sentiment analysis field which are used to tackle fake news challenges.

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Han, Xiao, Jing Peng, Tailai Peng, Rui Chen, Boyuan Hou, Xinran Xie, and Zhe Cui. "The Status and Trend of Chinese News Forecast Based on Graph Convolutional Network Pooling Algorithm." Applied Sciences 12, no. 2 (January 17, 2022): 900. http://dx.doi.org/10.3390/app12020900.

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It is always a hot issue in the intelligence analysis field to predict the trend of news description by pre-trained language models and graph neural networks. However, there are several problems in the existing research: (1) there are few Chinese data sets on this subject in academia and industry; and (2) using the existing pre-trained language models and graph classification algorithms cannot achieve satisfactory results. The method described in this paper can better solve these problems. (1) We built a Chinese news database predicted by more than 9000 annotated news time trends, filling the gaps in this database. (2) We designed an improved method based on the pre-trained language model and graph neural networks pooling algorithm. In the graph pooling algorithm, the Graph U-Nets Pooling method and self-attention are combined, which can better solve the analysis of the problem of forecasting the development trend of news events. The experimental results show that the effect of this method compared with the baseline graph classification algorithm is improved, and it also solves the shortcomings of the pre-trained language model that cannot handle very long texts. Therefore, it can be concluded that our research has strong processing capabilities for analyzing and predicting the development trend of Chinese news events.

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Pogul, Gopi, Sankei Rohokhale, Priya More, and Pallavi Chavan. "Ensemble approach for fake news classification using machine learning." ITM Web of Conferences 44 (2022): 03017. http://dx.doi.org/10.1051/itmconf/20224403017.

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During the covid 19 outbreak, fake news has grown highly, affecting people’s mental and physical health. There is a wide range of solutions for fake news classification which are machine learning-based proposed models. Research shows that the existing proposed models have less accuracy, and they are only text-based models. In our research paper, we are focused on different algorithms, and we are comparing these algorithms in our proposed model in this research paper. We are considering the title author and text in the proposed model. Based on our experiments, Logistic Regression has high accuracy, recall, and precision score values. This research paper suggests using a logistic regression model to classify fake news.

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Nur Ghaniaviyanto Ramadhan. "Indonesian Online News Topics Classification using Word2Vec and K-Nearest Neighbor." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 6 (December 30, 2021): 1083–89. http://dx.doi.org/10.29207/resti.v5i6.3547.

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News is information disseminated by newspapers, radio, television, the internet, and other media. According to the survey results, there are many news titles from various topics spread on the internet. This of course makes newsreaders have difficulty when they want to find the desired news topic to read. These problems can be solved by grouping or so-called classification. The classification process is carried out of course by using a computerized process. This study aims to classify several news topics in Indonesian language using the KNN classification model and word2vec to convert words into vectors which aim to facilitate the classification process. The use of KNN in this study also determines the optimal K value to be used. In addition to using the classification model, this study also uses a word embedding-based model, namely word2vec. The results obtained using the word2vec and KNN models have an accuracy of 89.2% with a value of K=7. The word2vec and KNN models are also superior to the support vector machine, logistic regression, and random forest classification models.

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Gao, Yiping. "News Video Classification Model Based on ResNet-2 and Transfer Learning." Security and Communication Networks 2021 (December 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/5865200.

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A large amount of useful information is included in the news video, and how to classify the news video information has become an important research topic in the field of multimedia technology. News videos are enormously informative, and employing manual classification methods is too time-consuming and vulnerable to subjective judgment. Therefore, developing an automated news video analysis and retrieval method becomes one of the most important research contents in the current multimedia information system. Therefore, this paper proposes a news video classification model based on ResNet-2 and transfer learning. First, a model-based transfer method was adopted to transfer the commonality knowledge of the pretrained model of the Inception-ResNet-v2 network on ImageNet, and a news video classification model was constructed. Then, a momentum update rule is introduced on the basis of the Adam algorithm, and an improved gradient descent method is proposed in order to obtain an optimal solution of the local minima of the function in the learning process. The experimental results show that the improved Adam algorithm can iteratively update the network weights through the adaptive learning rate to reach the fastest convergence. Compared with other convolutional neural network models, the modified Inception-ResNet-v2 network model achieves 91.47% classification accuracy for common news video datasets.

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Lehman-Wilzig, Sam N., and Michal Seletzky. "Hard news, soft news, ‘general’ news: The necessity and utility of an intermediate classification." Journalism: Theory, Practice & Criticism 11, no. 1 (February 2010): 37–56. http://dx.doi.org/10.1177/1464884909350642.

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Kaur, Harneet, and Kiran Jyoti. "Design and Implementation of Hybrid Algorithm for e-news Classification." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 1 (December 15, 2013): 3178–86. http://dx.doi.org/10.24297/ijct.v12i1.3365.

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Data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. As the use of internet is increasing day by day and with the advancement of internet news also publish online. So to handle this bulk amount of news various data mining techniques for classification had been used. In this paper we are using an intelligent system based on Hybrid algorithm (HMM, SVM and CART) for e-news classification. An intelligent system is designed which will extract the online news and then will find out category and subcategory wise news. System involves four main stages: a) Keyword Extraction b) Implementation of Hybrid Algorithm (HMM, SVM and CART). Data have been collected for experimentation from online newspapers like The Hindu, Hindustan Times and Times of India. The experimental results are based on the news categories and sub categories such as Entertainment: Bollywood 100% and Hollywood 90%, Sports: Cricket 90%, Football 90% and Hockey 78%, Matrimonial :Hindu 100% and Muslim 80%. In this paper we also compare the result of Hybrid algorithm (HMM, SVM and CART) with individual HMM and SVM Algorithm and conclude that Hybrid algorithm (HMM, SVM and CART) gave better result than that of what HMM and SVM individually gave.

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Bauer, A. J., Anthony Nadler, and Jacob L. Nelson. "What is Fox News? Partisan Journalism, Misinformation, and the Problem of Classification." Electronic News 16, no. 1 (December 3, 2021): 18–29. http://dx.doi.org/10.1177/19312431211060426.

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Fox News is one of the most popular news sources in the United States. Yet, there are those who reject the idea that Fox should be considered a news source in the first place, claiming it should be considered something more akin to propaganda. This article uses the ambiguity surrounding Fox News’ classification as an opportunity to explore how news sources get defined and categorized within journalism research and practice. It discusses three approaches that can be utilized to understand and categorize partisan media—producer-focused, audience-focused, and critical/normative. It explores the benefits and limitations of these perspectives and the need for scholarly inquiry that transverses and synthesizes them. We argue that an increasingly variegated news landscape calls for scholars to develop a richer vocabulary for distinguishing key features of partisan news outlets and greater reflexivity in research design that acknowledges the challenges inherent in translating meaning and values between producers, audiences, and scholars.

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Haumahu, J. P., S. D. H. Permana, and Y. Yaddarabullah. "Fake news classification for Indonesian news using Extreme Gradient Boosting (XGBoost)." IOP Conference Series: Materials Science and Engineering 1098, no. 5 (March 1, 2021): 052081. http://dx.doi.org/10.1088/1757-899x/1098/5/052081.

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Liu, Yun, Peng Zhou Zhang, and Jun Peng Gong. "Research on the Classification Based on Naïve Bayes." Applied Mechanics and Materials 543-547 (March 2014): 1643–46. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1643.

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With the advent of the era of big data, people increasingly strong demand for intelligent information. How to make news industry provide more accurate and efficient service for people has become a significant issue. This paper describes how to build a predictable model about Chinese news classification using Bayesian algorithms, and discusses the performance of Naive Bayesian model in different dataset environments, where contains unbalanced data and equilibrium data.

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Linden, Johannes, Xutao Wang, Stefan Forsstrom, and Tingting Zhang. "Productify News Article Classification Model with Sagemaker." Advances in Science, Technology and Engineering Systems Journal 5, no. 2 (2020): 13–18. http://dx.doi.org/10.25046/aj050202.

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Wongso, Rini, Ferdinand Ariandy Luwinda, Brandon Christian Trisnajaya, Olivia Rusli, and Rudy. "News Article Text Classification in Indonesian Language." Procedia Computer Science 116 (2017): 137–43. http://dx.doi.org/10.1016/j.procs.2017.10.039.

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Thandar Nwet, Khin. "Machine Learning Algorithms for Myanmar News Classification." International Journal on Natural Language Computing 8, no. 4 (August 31, 2019): 17–24. http://dx.doi.org/10.5121/ijnlc.2019.8402.

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El-Barbary, O. "Arabic News Classification Using Field Association Words." Advances in Research 6, no. 1 (January 10, 2016): 1–9. http://dx.doi.org/10.9734/air/2016/18789.

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Engesser, Sven. "Towards a Classification of Participatory News Websites." Digital Journalism 2, no. 4 (October 2, 2013): 575–95. http://dx.doi.org/10.1080/21670811.2013.841367.

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Ahmed, Jeelani, and Muqeem Ahmed. "ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES." IIUM Engineering Journal 22, no. 2 (July 4, 2021): 210–25. http://dx.doi.org/10.31436/iiumej.v22i2.1662.

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A massive rise in web-based online content today pushes businesses to implement new approaches and resources that might support better navigation, processing, and handling of high-dimensional data. Over the Internet, 90% of the data is unstructured, and there are several approaches through which this data can translate into useful, structured data—classification is one such approach. Classification of knowledge into a good collection of groups is significant and necessary. As the number of machine-readable documents proliferates, automatic text classification is badly needed to classify these documents. Unlabeled documents are categorized into predefined classes of labeled documents using text labeling, a supervised learning technique. This paper reviewed some existing approaches for classifying online news articles and discusses a framework for the automatic classification of online news articles. For achieving high accuracy, different classifiers were tried. Our experimental method achieved 93% accuracy using a Bayesian classifier and present in terms of confusion metrics. ABSTRAK: Peningkatan tinggi pada masa kini pada maklumat dalam talian berasaskan web menyebabkan kaedah baru dalam bisnes telah diguna pakai dan sumber sokongan seperti navigasi, proses, dan pengurusan data berdimensi-tinggi adalah perlu. 90% data di internet adalah data tidak berstruktur, dan terdapat pelbagai kaedah data ini dapat diterjemahkan kepada data berguna, lebih berstruktur — iaitu melalui kaedah klasifikasi. Klasifikasi ilmu kepada koleksi kumpulan baik adalah penting dan perlu. Seperti mana mesin-boleh baca dokumen berkembang pesat, teks klasifikasi automatik juga sangat diperlukan bagi mengklasifikasi dokumen-dokumen ini. Dokumen yang tidak dilabel dikategori sebagai pengelasan pratakrif dokumen berlabel melalui teks label, iaitu teknik pembelajaran berpenyelia. Kajian ini mengkaji semula pendekatan sedia ada bagi artikel berita dalam talian dan membincangkan rangka kerja bagi pengelasan automatik artikel berita dalam talian. Bagi menghasilkan ketepatan yang tinggi, kami menggunakan pelbagai alat klasifikasi. Kaedah eksperimen ini mempunyai ketepatan 93% menggunakan pengelas Bayesian dan data dibentangkan berdasarkan matriks kekeliruan.

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Endalie, Demeke, and Getamesay Haile. "Hybrid Feature Selection for Amharic News Document Classification." Mathematical Problems in Engineering 2021 (March 11, 2021): 1–8. http://dx.doi.org/10.1155/2021/5516262.

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Today, the amount of Amharic digital documents has grown rapidly. Because of this, automatic text classification is extremely important. Proper selection of features has a crucial role in the accuracy of classification and computational time. When the initial feature set is considerably larger, it is important to pick the right features. In this paper, we present a hybrid feature selection method, called IGCHIDF, which consists of information gain (IG), chi-square (CHI), and document frequency (DF) features’ selection methods. We evaluate the proposed feature selection method on two datasets: dataset 1 containing 9 news categories and dataset 2 containing 13 news categories. Our experimental results showed that the proposed method performs better than other methods on both datasets 1and 2. The IGCHIDF method’s classification accuracy is up to 3.96% higher than the IG method, up to 11.16% higher than CHI, and 7.3% higher than DF on dataset 2, respectively.

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hra, Chait, Dr G. M. Lingaraju, and Dr S. Jagannatha. "Automatic Web Page Classification System with Improved Accuracy." Webology 18, no. 2 (December 23, 2021): 225–42. http://dx.doi.org/10.14704/web/v18i2/web18318.

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Nowadays, the Internet contain s a wide variety of online documents, making finding useful information about a given subject impossible, as well as retrieving irrelevant pages. Web document and page recognition software is useful in a variety of fields, including news, medicine, and fitness, research, and information technology. To enhance search capability, a large number of web page classification methods have been proposed, especially for news web pages. Furthermore existing classification approaches seek to distinguish news web pages while still reducing the high dimensionality of features derived from these pages. Due to the lack of automated classification methods, this paper focuses on the classification of news web pages based on their scarcity and importance. This work will establish different models for the identification and classification of the web pages. The data sets used in this paper were collected from popular news websites. In the research work we have used BBC dataset that has five predefined categories. Initially the input source can be preprocessed and the errors can be eliminated. Then the features can be extracted depend upon the web page reviews using Term frequency-inverse document frequency vectorization. In the work 2225 documents are represented with the 15286 features, which represents the tf-idf score for different unigrams and bigrams. This type of the representation is not only used for classification task also helpful to analyze the dataset. Feature selection is done by using the chi-squared test which will be in the task of finding the terms that are most correlated with each of the categories. Then the pointed features can be selected using chi-squared test. Finally depend upon the classifier the web page can be classified. The results showed that list has obtained the highest percentage, which reflect its effectiveness on the classification of web pages.

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Journal articles on the topic 'News classification'

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