HINDI FAKE NEWS DETECTOR

Nowadays, with the advancement of technology, the use of news sources has also undergone a great evolution. News sources have constantly evolved from past to present, ranging from magazines to radios, from newspapers to televisions. The fact that it has become so easy to access news has caused society to pay more attention to fake news. Fake news has the ability to influence society through news sources such as social media, which can reach wider audiences with the development of technology. The difficulties of users in accessing accurate and reliable sources in this information flow that shapes their daily lives increases the potential for the spread of fake news, and it becomes increasingly difficult to distinguish between real and fake news. In this study, classification models for fake news detection were designed using machine learning algorithms. The dataset, which includes fake and real news examples, contains 42,000 examples. Each class, including fake and real samples, contains 22,000 sample data. In order to increase data quality, accuracy and usability, preprocessing methods were applied to the data set. The removal of numbers, stop words, and html tags was done in the pre-processing step to remove unnecessary information from the text. Models were created for fake news detection with singular and ensemble classification algorithms. Performance evaluation of the models was performed using 5-fold cross-validation. In the performance comparisons of the models, values such as accuracy, sensitivity, specificity, tp rate and fp rate were calculated. The highest performance results were observed in the random forest classification algorithm with an accuracy rate of 76%.

Fake news is a growing problem on social media and can have significant negative consequences for individuals and society owing to its accessibility, low cost, and quick distribution. It is difficult to automatically identify bogus news that defies the current content-based analysis techniques. One of the key reasons is that current NLP algorithms still lack common sense, which is frequently necessary for understanding how to read the news. Recent research has demonstrated that the propagation patterns of true and fake news differ on social media. With the potential for automatic fake news detection, this study investigates the use of Deep Learning (DL) models to detect fake news on social media. A Neural Network (NN) model was developed using Natural Language Toolkit (NLTK), TensorFlow, and Natural Language Processing (NLP) for textual analysis. The model was trained on a dataset of fake and real news articles, and was able to achieve high accuracy in identifying fake news. The results of this study suggest that DL models can be valuable tools for detecting fake news on social media.

The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known “Fake News” dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial naïve bayes (MNB), gaussian naïve bayes (GNB), Bernoulli naïve Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem.

Covid-19 fake news sentiment analysis

Due to the widespread use of social networks by people of all ages, distinguishing between fake and real news on these platforms has become a significant challenge in today's world. Individuals who disseminate fake news on social networks often aim to achieve various commercial, political, and economic goals. Therefore, identifying and distinguishing real news from fake news is crucial in addressing this issue. The objective of this research is to present an intelligent model for detecting fake news using a news propagation tree in the social network Twitter. The dataset used in this study is sourced from the political news section of the Fake News Net website. Initially, a news propagation tree was constructed for both real and fake news using this dataset, followed by the development of features from structural, temporal, and syntactic perspectives based on the news propagation tree. Finally, machine learning algorithms were employed to build a model for predicting fake and real news. The results indicated that among the algorithms used for modeling, the Random Forest algorithm, with an accuracy of 75.8%, was the best model for distinguishing fake news from real news.

Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.

Nowadays, social media have opened a door in front of us to share individual’s expressions, emotions, and attitudes toward any incident. Sentiment analysis from different social media posts may help us to detect positive, negative, or emotional behavior toward society. Depressive text detection from social media posts is one of the most challenging parts of individual behavior or sentiment analysis. In this paper, different machine learning algorithms are used to detect depressive Bangla text from social media posts. Pre-processing steps like stemming, stop word removal, etc., are used to clean the collected data, and feature extraction techniques like count vectorization, TF-IDF, word embedding, etc., are applied to the collected dataset which consists of 6178 texts collected from social media posts. We have achieved the highest 97% classification accuracy using decision tree and 94% accuracy for bidirectional LSTM (deep learning model) to predict depressive text in Bangla language. Depressive text detection from social media posts will create an opportunity for psychologists to analyze sentiment from shared posts, reactions, and attitudes which may lessen the unwanted activities of the depressed people through diagnosis and taking them under treatment.KeywordsDepressive textBangla languageDeep learningSocial media post

Along with the advancement of the world wide web, the rise and far reaching appropriation of the social site initiative have distorted the manner in which news is shaped and distributed. News has gotten quicker, less expensive and effectively available among web based life. This modify has joined a few hindrances also. Specifically, flabbergasting content, for example, fake news made by online networking clients, is getting progressively perilous. The fake news issue, in spite of being presented just because as of late, has become a significant examination theme because of the high substance of online networking. Writing fake remarks and news via web-based networking media is simple for clients. The primary test is to decide the distinction among genuine and fake news. We developed a method for the fake news classification on twitter. Web- based GUI is developed for the fake news classification system to categorize the tweets as fake or genuine. We develop a machine learning program to identify fake news by comparing tweets with genuine sources. Naive bayes and passive aggressive machine learning algorithms are estimated with TF-IDF feature extraction method.

Evaluation of multispectral imaging for freeze damage assessment in strawberries using AI-based computer vision technology

Discovering fake news is very important in the press to maintain credibility and combat wrong information.Traditional fake news detection systems (FNDS) are challenged due to accuracy problems due to the complex nature of misinformation in news articles.This study introduces a new approach using a group method that combines automatic learning algorithms such as naive Bays, K-NN, Random Forest, Decision Tree, and Support Vector Machine (SVM) by integrating majority votes.The results indicate that group technologies greatly enhance the accuracy of defining the fake news compared to the algorithm-based works.By leveraging collective intelligence, FNDS addresses the complexities of fake news detection.This approach enhances its capability to identify and refute misinformation effectively.This research emphasizes the choice of appropriate algorithms and the integration of the group's methods to develop the most accurate and strong fake news discovery systems, contributing to combating wrong information in the press.The proposed approach achieved an accuracy improvement of 15% compared to individual algorithms, with an overall F1-score of 89.5%.

Fake news has become a serious and destabilizing problem in our increasingly polarized society. The core tasks of detecting and characterizing untrue or misleading online content are quite challenging. News consumers intervention has been identified as a crucial addition to Fake News Detection Systems (FNDS). However, even though detection explanation is starting to gain research momentum, not as much attention has been given to personalization of explanations. As humans are the obvious targets of fake news, explanations that can evoke emotional responses and/or are aligned with individual personality traits and cognitive styles can be leveraged to nudging of the news consumer into a reflective state about the subject, which has been shown to be more effective than the crude presentation of facts in changing pre-conceived beliefs. This paper adds to the main goal of misinformation detection systems, aiming to expand them onto personalized FNDS. It proposes a metric to be used in their evaluation. It offers a definition to help research on the implementation of personalized fake news explanations. And finally, it proposes a personalized fake news taxonomy, discussing its components centered around emotion-based and personality-based explanations. This taxonomy highlights several opportunities for those researching in the area of personalized fake news explanation systems.

FakeNews is one of the most popular phenomena that have considerable effects on our social life, especially in the political domain. Nowadays, creating fake news becomes very easy because of users' widespread using the internet and social media. Therefore, the detection of elusiveness news is a crucial problem that needs to be considerable mainly because of its challenges like the limited amount of the benchmark datasets and the amount of the published news every second. This research proposed utilizing two different machine learning algorithms (random forest and decision tree (J48)) to detect the fake news. In this paper, the full dataset size equals 20,761 samples, while the testing sample size equals 4,345 samples.The preprocessing steps start with cleaning data by removing unnecessary special characters, numbers, English letters, and white spaces, and finally, removing stop words is implemented. After that, the most popular feature extraction method (TF-IDF) is used before applying the two suggested classification algorithms. The results show that the best accuracy achieved equals 89.11% using the decision tree model while using the random forest; the accuracy achieved equals 84.97 %.

Due to the ease with which information may be obtained and the exponential growth in the amount of information available on the internet, it has become more challenging to differentiate between false and genuine information. Any of these fake news websites may easily infect people due to their fabricated claims. This situation has a significant impact on the offline community in general. As a result, interest in this subject has grown. A critical study has been conducted on identifying fake news in English and other languages, save for a few in Bangla. Our research shows an experimental benchmark investigation into identifying false news on a Bengali news website since there is less work in this domain. This research analyses 11434 fake news and true news in the Bengali language and evaluates the performance of machine learning and deep learning algorithms to create a benchmark for detecting Bangla fake news. This research compares the model’s performance to a variety of linguistic characteristics and word vectorizers. The best accuracy obtained for lemmatized text is 95.45% for TF-IDF with the SGD classifier and 95.10% for the count vectorizer with the MLP classifier. For stemmed text, we received the best accuracy of 94.9% for the Count Vectorizer with MLP classifier and 94.83% for TF-IDF with MLP classifier. Among deep learning models, RNN gave the best performance with 96.55% accuracy where the f1 score is 0.96. The pre-trained Bangla BERT model gave an F1-Score of 0.96 and showed an accuracy of 93.35%.

The phenomenon of Fake news is experiencing a rapid and growing progress with the evolution of the means of communication and Social media. Fake news detection is an emerging research area which is gaining big interest. It faces however some challenges due to the limited resources such as datasets and processing and analysing techniques. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. We propose also a dataset of fake and true news to train the proposed system. Obtained results show the efficiency of the system. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. We propose also a dataset of fake and true news to train the proposed system. Obtained results show the efficiency of the system.

The rapid growth of online news consumption in Indonesia has intensified the challenge of combating fake news, which undermines public trust and threatens social stability. Conventional approaches, including manual verification, are increasingly inadequate to address the scale and speed of digital information dissemination. This study aims to develop an automatic Indonesian fake news classification system using a deep learning framework that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) with Word2Vec embeddings. Unlike many existing fake news detection models that rely on limited validation settings or focus predominantly on English-language data, this work explicitly addresses the linguistic characteristics and practical constraints of the Indonesian context, thereby strengthening model relevance for real-world deployment. The dataset comprises 6,000 balanced news articles, including 3,000 valid items from Detik.com and 3,000 hoax items from Turnbackhoax.id, collected between January and October 2024. Text preprocessing involved cleaning, stopword removal, tokenization, and padding. A 300-dimensional Word2Vec embedding model was employed, and the classifier was trained using stratified 3-fold cross-validation to ensure robust performance estimation. An ensemble inference strategy was further applied to reduce inter-fold variance and enhance generalization on unseen data, directly addressing a common limitation of prior single-model approaches. Experimental results show that the proposed model achieves an accuracy of 86.43% and an F1-score of 86.28%, alongside a high mean Average Precision of 0.927 during validation. Compared with previously reported deep learning baselines, this framework demonstrates competitive yet more stable performance under realistic evaluation settings, supporting scalable deployment.