News Classification Research Articles

Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News

The creation and spreading of fake information can be carried out very easily through the internet community. This pervasive escalation of fake news and rumors has an extremely adverse effect on the nation and society. Detecting fake news on the social web is an emerging topic in research today. In this research, the authors review various characteristics of fake news and identify research gaps. In this research, the fake news dataset is modeled and tokenized by applying term frequency and inverse document frequency (TFIDF). Several machine-learning classification approaches are used to compute evaluation metrics. The authors proposed hybridizing SVMs and RF classification algorithms for improved accuracy, precision, recall, and F1-score. The authors also show the comparative analysis of different types of news categories using various machine-learning models and compare the performance of the hybrid RFSVM. Comparative studies of hybrid RFSVM with different algorithms such as Random Forest (RF), naïve Bayes (NB), SVMs, and XGBoost have shown better results of around 8% to 16% in terms of accuracy, precision, recall, and F1-score.

Online Writeup Categorization using a Machine Learning Strategy

A variety of online sources produces a huge amount of daily news; thus, it is important to categorize the news items to make the information accessible to consumers easily and quickly scraping is used to gather existing news items from news websites and then categorize them automatically using a variety of classification algorithms. As a result, news categorization is a technique for discovering untracked news themes and providing specific recommendations based on the user's historical interests. The BBC News dataset, which includes articles from five different categories including Business, Entertainment, Politics, Sport, and Technology, is used in this task to discuss various steps in news classification and implement a few algorithmic approaches such as Naive Bayes, Binary Classifier, SVM, Perceptron, and SGD. In the study, results from several classification algorithms are examined, and their accuracy is measured.

Text-image multimodal fusion model for enhanced fake news detection.

In the era of rapid internet expansion and technological progress, discerning real from fake news poses a growing challenge, exposing users to potential misinformation. The existing literature primarily focuses on analyzing individual features in fake news, overlooking multimodal feature fusion recognition. Compared to single-modal approaches, multimodal fusion allows for a more comprehensive and enriched capture of information from different data modalities (such as text and images), thereby improving the performance and effectiveness of the model. This study proposes a model using multimodal fusion to identify fake news, aiming to curb misinformation. The framework integrates textual and visual information, using early fusion, joint fusion and late fusion strategies to combine them. The proposed framework processes textual and visual information through data cleaning and feature extraction before classification. Fake news classification is accomplished through a model, achieving accuracy of 85% and 90% in the Gossipcop and Fakeddit datasets, with F1-scores of 90% and 88%, showcasing its performance. The study presents outcomes across different training periods, demonstrating the effectiveness of multimodal fusion in combining text and image recognition for combating fake news. This research contributes significantly to addressing the critical issue of misinformation, emphasizing a comprehensive approach for detection accuracy enhancement.

Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM

Fake news spreads quickly and is challenging to stop due to the ease of accessing and sharing information online. Deep learning techniques are a method that can be used to identify fake news quickly and accurately. The types of neural networks commonly utilized in deep learning architectures include Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), which can perform well when managing the task of classifying fake news, according to several pertinent studies. Regarding handling instances of Indonesian fake news classification, this study compares how well the CNN and LSTM models perform. However, given that Indonesian is a low-resource language with scant documentation, it is challenging to build an adequate data set. At the same time, the CNN and LSTM classification models require significant training data. We proposed a transfer learning method by combining two classification models with a pre-trained IndoBERT language model. 1340 news text data were used, including 643 actual news texts from CNN Indonesia, Liputan6, and Detik and 697 fake news texts from TurnBackHoax. As a result, the performance of the combination of the LSTM classification model with IndoBERT outperformed that of the CNN classification model with IndoBERT, which only produced an accuracy of 92.91%, down by 6%, and was able to produce an accuracy of up to 97.76%, an increase of 4.8% from before. Furthermore, the results show that the LSTM classification model outperforms the CNN classification model in capturing the representation created by IndoBERT. Additionally, these insights may serve as a basis for future research on identifying fake news in Indonesia, helping to improve methods for combatting misinformation in Indonesia.

Knowledge-Based Recommendation System for Plate Waste Reduction in Latvian Schools

Food waste indicates ineffective and irresponsible consumption of resources, particularly during the food consumption stage. The aim of our research study is to optimize the catering management process at Latvian schools by reducing the amount of plate waste. The experts developed a set of recommendations aimed at improving the catering management process at schools. The recommendations developed were supported by measurable parameters, which must be monitored by school staff. The capability-driven development approach was applied to model the recommendation system. A plate waste predictive module and a large language model classifier were integrated into the system to support sustainable decision-making. The large language model classifier was trained to filter questions and recommendations. Three training methods were compared: training from scratch and finetuning by using datasets DBPedia and News Category Dataset. As a result, we present the list of recommendations based on the literature review, and the prototype of the knowledge-based recommendation system was developed to audit the school catering management process and promote sustainable school management and decision-making. The recommendation system aims to reduce plate waste due to deficiencies in the implementation of the catering process and to promote responsible food consumption at schools.

Extracting Features from Text Flows based on Semantic Similarity for Text Classification: an Approach Inspired by Audio Analysis

Text classification is a mainly investigated challenge in Natural Language Processing (NLP) research. The higher performance of a classification model depends on a representation that can extract valuable information about the texts. Aiming not to lose crucial local text information, a way to represent texts is through flows, sequences of information collected from texts. This paper proposes an approach that combines various techniques to represent texts: the representation by flows, the benefit of the word embeddings text representation associated with lexicon information via semantic similarity distances, and the extraction of features inspired by well-established audio analysis features.In order to perform text classification, this approach splits the text into sentences and calculates a semantic similarity metric to a lexicon on an embedding vector space. The sequence of semantic similarity metrics composes the text flow. Then, the method performs the extraction of twenty-five features inspired by audio analysis (named Audio-Like Features). The features adaptation from audio analysis comes from a similitude between a text flow and a digital signal, in addition to the existing relationship between text, speech, and audio. We evaluated the method in three NLP classification tasks: Fake News Detection in English, Fake News Detection in Portuguese, and Newspaper Columns versus News Classification. The approach efficacy is compared to baselines that embed semantics in text representation: the Paragraph Vector and the BERT. The objective of the experiments was to investigate if the proposed approach could compete with the baselines methods improve their efficacy when associated with them. The experimental evaluation demonstrates that the association between the proposed and the baseline methods can enhance the baseline classification efficacy in all three scenarios. In the Fake News Detection in Portuguese task, our approach surpassed the baselines and obtained the best effectiveness (PR-AUC = 0.98).

FAKE NEWS DETECTION

The Internet is one of among the greatest inventions of the world and there are millions of individuals who utilize it. These persons employ it in various ways, as listed below: There are various social networks that are available for use among such users. Often they can be just ordinaries who decide to make a post or share the news on the internet. Such platforms offer no means of confirming any users or the content they post. Therefore, some of the users attempt to indulge in active dissemination of fake news through the platforms. This fake news can aimed at an individual, a community, a company or a political party. It becomes virtually impossible for a human being to follow all the fake news. So, currently, there exists the need for automatic classification of fake news using machine learning classifiers. My description of machine learning classifiers such as passive agressive classifiers and algorithm such as K-Nearest Neighbor, Support vector machine(SVM) is used for detecting fake news is described in this systematic literature review. KEYWORD: Fake News Dissemination, Social Networks, Automatic Classification, Machine Learning Classifiers, Passive Aggressive Classifiers, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Systematic Literature Review, Challenges in Fake News Detection.

A Predictive Model for Benchmarking the Performance of Algorithms for Fake and Counterfeit News Classification in Global Networks.

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments.

Advanced Computational Methods for News Classification: A Study in Neural Networks and CNN integrated with GPT

In an era inundated with vast amounts of information, the imperative for efficient news classification is paramount. This research explores the sophisticated integration of neural networks and convolutional neural networks (CNN) with Generative Pre-trained Transformers (GPT) to enhance the precision and efficacy of news categorization. The rapid digital dissemination of news necessitates advanced computational methodologies capable of accurate classification and event prediction that include finance and economic events. Leveraging recent advancements in machine learning and natural language processing (NLP), this study utilizes large language models (LLMs) such as GPT and BERT, known for their exceptional comprehension and generation of human-like text. Over 232 days, our methodology classified 33,979 news articles into Education & Learning, Health & Medicine, and Science & Technology, with further subcategorization into 32 distinct subcategories. For evaluation, a sample of 5,000 articles was assessed using metrics such as True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), Precision, Recall, and F1-Score. In comparison with the existing studies, the proposed method achieving significantly higher with average scores of 0.986 (Precision), 0.987 (Recall), and 0.987 (F1-Score). This research offers substantial practical contributions, providing detailed insights into news source contributions, effective anomaly detection, and predictive trend analysis using neural networks. The theoretical contributions are profound, demonstrating the mathematical integration of GPT with CNNs and recurrent neural networks. This integration advances computational news classification and exemplifies how sophisticated mathematical frameworks enhance large-scale text data analysis, marking a pivotal advancement in applying advanced computational methods in real-world scenarios.

Analysis and Classification of Fake News Using Sequential Pattern Mining

Analysis and Classification of Fake News Using Sequential Pattern Mining

Indonesian GDP movement detection using online news classification

Gross Domestic Product (GDP) stands as a pivotal indicator, offering strategic insights into economic dynamics. Recent technological advancements, particularly in real-time information dissemination through online economic news platforms, provide an accessible and alternative data source for analyzing GDP movements. This study employs online news classification to identify patterns in the movement and growth rate of Indonesia’s GDP. Utilizing a web scraping technique, we collected data for analysis. The classification models employed include transfer learning from pre-trained language model transformers, with classical machine learning methods serving as baseline models. The results indicate superior performance by the pre-trained language model transformers, achieving the highest accuracy of 0.8880 and 0.7899. In comparison, hyperparameter-tuned classical machine learning models also demonstrated commendable results, with the best accuracy reaching 0.845 and 0.7811. This research underscores the efficacy of leveraging online news classification, particularly through advanced language models. The findings contribute to a nuanced understanding of economic dynamics, aligning with the contemporary landscape of information accessibility and technological progress.

Exploring transformer models for sentiment classification: A comparison of BERT, RoBERTa, ALBERT, DistilBERT, and XLNet

AbstractTransfer learning models have proven superior to classical machine learning approaches in various text classification tasks, such as sentiment analysis, question answering, news categorization, and natural language inference. Recently, these models have shown exceptional results in natural language understanding (NLU). Advanced attention‐based language models like BERT and XLNet excel at handling complex tasks across diverse contexts. However, they encounter difficulties when applied to specific domains. Platforms like Facebook, characterized by continually evolving casual and sophisticated language, demand meticulous context analysis even from human users. The literature has proposed numerous solutions using statistical and machine learning techniques to predict the sentiment (positive or negative) of online customer reviews, but most of them rely on various business, review, and reviewer features, which leads to generalizability issues. Furthermore, there have been very few studies investigating the effectiveness of state‐of‐the‐art pre‐trained language models for sentiment classification in reviews. Therefore, this study aims to assess the effectiveness of BERT, RoBERTa, ALBERT, DistilBERT, and XLNet in sentiment classification using the Yelp reviews dataset. The models were fine‐tuned, and the results obtained with the same hyperparameters are as follows: 98.30 for RoBERTa, 98.20 for XLNet, 97.40 for BERT, 97.20 for ALBERT, and 96.00 for DistilBERT.

Examining the merits of feature-specific similarity functions in the news domain using human judgments

Online news article recommendations are typically of the ‘more like this’ type, generated by similarity functions. Across three studies, we examined the representativeness of different similarity functions for news item retrieval, by comparing them to human judgments of similarity. In Study 1 (N=401\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=401$$\\end{document}), participants assessed the overall similarity of ten randomly paired news articles on politics and compared their judgments to different feature-specific similarity functions (e.g., based on body text or images). In Study 2, we checked for domain differences in a mixed-methods survey (N=45\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=45$$\\end{document}), surfacing evidence that the effectiveness of similarity functions differs across different news categories (‘Recent Events’, ‘Sport’). In Study 3 (N=173\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=173$$\\end{document}), we improved the design of Study 1, by controlling for how news articles were matched, differentiating between dissimilar news articles and articles that were matched on a shared topic, named entities, and/or date of publication, across ‘Recent Events’ and ‘Sport’ categories. Across all studies, we found that users mostly used text-based features (e.g., body text, title) for their similarity judgments, while BodyText:TF-IDF was found to be the most representative for their judgments. Moreover, the strength of similarity judgments by humans and similarity scores by feature-specific functions was strongly affected by how news article pairs were matched. We show that humans and similarity functions are better aligned when two news articles are more alike, such as in a news recommendation scenario.

Voice-Assisted News App using Natural Language Processing

In recent times, the advancements in Artificial Intelligence (AI) and natural language processing (NLP) have enabled the development of novel voice-assisted apps. This study introduces a cutting-edge Voice-Assisted News App that utilizes the capabilities of Alan M, a prominent AI platform that revolutionizes the way people access news. This research endeavor attempts to utilize Alan Al's capabilities to offer a personalized news experience and enhance clarity through voice interactions. Users of the Voice-Assisted News software can effortlessly request and select their preferred news topics, and the computer will generate the most relevant news updates. Through integration with diverse news sources and the application of sophisticated algorithms, the program generates an individualized news stream for every user, ensuring they receive the latest and most relevant information. Users can efficiently access story audio descriptions. The app's speech interface allows users to navigate across different news categories. The NLP algorithms developed by Alan AI offer precise understanding of user demands by ensuring smooth interaction and an authentic conversational experience

A Novel Model Combining Transformer and Bi-LSTM for News Categorization

News categorization (NC), the aim of which is to identify distinct categories of news through analyzing the contents, has acquired substantial progress since deep learning was introduced into the natural language processing (NLP) field. As a state-of-art model, transformer’s classification performance is not satisfied compared with recurrent neural network (RNN) and convolutional neural network (CNN) if it does not get pretrained. Based on the transformer model, this article proposes a novel framework that combines bidirectional long short-term memory (Bi-LSTM) network and transformer to solve this problem. In the suggested framework, the self-attention mechanism is substituted with Bi-LSTM to capture the semantic information from sentences. Meanwhile, an attention mechanism model is applied to focus on those important words and adjust their weights to solve the problem of long-distance information loss. With pooling network, the network complexity can be reduced and the main features can be highlighted by halving the dimension of the hidden state. Finally, after acquiring the hidden representation by the above structures, we utilize a contraction network to further capture the long-range associations from a text. Experiments on three large-scale corpora were performed to evaluate the suggested framework, and the results demonstrate that our model outperforms other models such as deep pyramid CNN (DPCNN), transformer.

GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques—BERT's deep contextual understanding and the generative capabilities of GPT—to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.

Development of an enhanced naive bayes algorithm for fake news classification

Development of an enhanced naive bayes algorithm for fake news classification

Using Artificial Intelligence Systems in News Verification: An Application on X

The aim of this study is to analyse the relationship between the interaction rates and the number of followers of independent news accounts broadcasting on social network platforms and the types of fake news they publish and the frequency of publishing fake news. In the study, fake news was categorised using qualitative content analysis method. In addition to this, artificial intelligence was used to check the accuracy of news content shared on social networks and to distinguish misleading information. To obtain the data, Chat GPT was utilised and an AI-powered chatbot was developed with the help of algorithms prepared by the researchers to determine the accuracy of the news. The population of the study consists of the accounts practicing social media journalism on the social networking platform X in Türkiye. The sample of the study consists of 6 accounts with the highest interaction selected by purposive sampling method among the accounts that engage in social media journalism on this platform and have the highest interaction. According to the results obtained from the research, a large proportion of the news content shared by accounts practicing social media journalism on the X platform in Türkiye consists of unverifiable news content. In the category of unverifiable news, news is mostly created in the category of “Fabricated” content.

Application of Machine Learning Techniques for Fake News Classification

ABSTRACT Fake News consists of disseminating fake news in various social and digital media such as newspapers, television networks, and the internet. Fake news is not a new phenomenon in human behavior. However, the current dissemination is very different from what happened in the past. Social networks and the contemporary world have made it possible for the spread of lies to occur quickly and even intentionally. This causes serious problems, and its impacts can be felt in the real world. The identification of fake news can be useful in several contexts and can be used, for example, as a news filter in the virtual space. Thus, the present work aims to propose and evaluate strategies for processing and applying machine learning models, to improve the performance of classifiers in the problem of identifying fake news in Brazilian news.

Leveraging textual information for social media news categorization and sentiment analysis.

The rise of social media has changed how people view connections. Machine Learning (ML)-based sentiment analysis and news categorization help understand emotions and access news. However, most studies focus on complex models requiring heavy resources and slowing inference times, making deployment difficult in resource-limited environments. In this paper, we process both structured and unstructured data, determining the polarity of text using the TextBlob scheme to determine the sentiment of news headlines. We propose a Stochastic Gradient Descent (SGD)-based Ridge classifier (RC) for blending SGDR with an advanced string processing technique to effectively classify news articles. Additionally, we explore existing supervised and unsupervised ML algorithms to gauge the effectiveness of our SGDR classifier. The scalability and generalization capability of SGD and L2 regularization techniques in RCs to handle overfitting and balance bias and variance provide the proposed SGDR with better classification capability. Experimental results highlight that our string processing pipeline significantly boosts the performance of all ML models. Notably, our ensemble SGDR classifier surpasses all state-of-the-art ML algorithms, achieving an impressive 98.12% accuracy. McNemar's significance tests reveal that our SGDR classifier achieves a 1% significance level improvement over K-Nearest Neighbor, Decision Tree, and AdaBoost and a 5% significance level improvement over other algorithms. These findings underscore the superior proficiency of linear models in news categorization compared to tree-based and nonlinear counterparts. This study contributes valuable insights into the efficacy of the proposed methodology, elucidating its potential for news categorization and sentiment analysis.