ABSTRACT
With the advancement in technology, web technol-ogy in the form of social media is one of the main origins of information worldwide. Web technology has helped people to enhance their ability to know, learn, and gain knowledge about things around them. The benefits that technological advancement offers are boundless. However, apart from these, social media also has major issues related to problems and challenges concerning filtering out the right information from the wrong ones. The sources of information become highly unreliable at times, and it is difficult to differentiate and decipher real news or real information from fake ones. Cybercrime, through fraud mechanisms, is a pervasive menace permeating media technology every single day. Hence, this article reports an attempt to fake news detection in Khasi social media data. To execute this work, the data analyzed are extracted from different Internet platforms mainly from social media articles and posts. The dataset consists of fake news and also real news based on COVID-19, and also other forms of wrong information disseminated throughout the pandemic period. We have manually annotated the assembled Khasi news and the data set consists of 116 news data. We have used three machine learning techniques in our experiment, the Decision Tree, the Logistic Regression, and the Random Forest approach. We have observed in the experimental results that the Decision Tree-based approach yielded accurate results with an accuracy of 87%, whereas the Logistic Regression approach yielded an accuracy of 82% and the Random Forest approach yielded an accuracy of 75%. © 2023 IEEE.
ABSTRACT
Combating fake news on social media is a critical challenge in today's digital age, especially when misinformation is spread regarding vital matters such as the Covid-19 pandemic. Manual verification of all content is infeasible;hence, Artificial Intelligence is used to classify fake news. Our ensemble model uses multiple Natural Language Processing techniques to analyze the truthfulness of the text in tweets. We create custom parameters that analyze the consistency and truthfulness of domains contained in hyperlinked URLs. We then combine these parameters with the results of our deep learning models to achieve classification with greater than 99% accuracy. We have proposed a novel method to calculate a custom coefficient, the Combined Metric of Prediction Uncertainty (CMPU), which is a measure of how uncertain the model is of its classification of a given tweet. Using CMPU, we have proposed the creation of a priority queue following which the tweets classified with the lowest certainty can be manually verified. By manually verifying 3.93% of tweets, we were able to improve the accuracy from 99.02% to 99.77%. © 2022 IEEE.
ABSTRACT
Fake news detection techniques are a topic of interest due to the vast abundance of fake news data accessible via social media. The present fake news detection system performs satisfactorily on well-balanced data. However, when the dataset is biased, these models perform poorly. Additionally, manual labeling of fake news data is time-consuming, though we have enough fake news traversing the internet. Thus, we introduce a text augmentation technique with a Bidirectional Encoder Representation of Transformers (BERT) language model to generate an augmented dataset composed of synthetic fake data. The proposed approach overcomes the issue of minority class and performs the classification with the AugFake-BERT model (trained with an augmented dataset). The proposed strategy is evaluated with twelve different state-of-the-art models. The proposed model outperforms the existing models with an accuracy of 92.45%. Moreover, accuracy, precision, recall, and f1-score performance metrics are utilized to evaluate the proposed strategy and demonstrate that a balanced dataset significantly affects classification performance.
ABSTRACT
Fake news confronts us on a daily basis in today’s fast-paced social media world. While some instances of fake news might seem innocuous, there are many examples that prove to be menacing. Misinformation or disinformation which takes the form of these weaponized lies which eventually amount to defective information, defamatory allegations, and hoaxes. The only motive behind such a malicious act is to engender emotional instability among the public. One such prevalent example today is COVID-19 which has caused an unprecedented paradigm shift in numerous businesses and quotidian activities across the globe. One of the primary activities is being news reporting. On average, people are spending almost one hour a day reading news via many different sources. The development in technology has obviated the barriers between sharing of information, thereby truly making the industry cosmopolitan. Therefore, it is paramount to curb fake news at source and prevent it from spreading to a larger audience. This paper describes a system, where the user can identify apocryphal news related to COVID-19 so as to ensure its authenticity. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Social media platforms have become popular news sources thanks to their immense popularity and high speed of information dissemination. Using these platforms is essential for news organizations and journalists to track and discover news in digital journalism age. However, the abundance of meaningless data and the lack of organization on these platforms make it difficult to reach valuable news for journalists. In this paper, we create the first public dataset containing large number of real-world Turkish news tweets belonging to different news categories, to the best of our knowledge. We propose an artificial intelligence-based two-step approach to assist journalists for accessing the news shared by various sources on social media under the relevant categories like politics (elections, riots, etc.), health (pandemic, covid-19, etc.), etc. via a single platform by reducing the possibility of overlooking needed information. In the first step, we propose a machine learning based novel model for collecting and categorizing news posts on social media. We implement several traditional machine learning and deep learning based algorithms and evaluate their classification performance in terms of accuracy, precision, recall, and F1 score. In the second step, we develop a software tool, named TwitterBulletin, which automatically retrieves Turkish news tweets and groups them under news categories in real time by using the CNN classifier which achieves the best performance in the first step. The results show that the overall accuracy rate of TwitterBulletin is reasonably high and satisfactory despite the challenge of classifying short tweets. © 2022, IICM. All rights reserved.
ABSTRACT
The year 2020 marked an important moment when the COVID-19 pandemic promoted Internet as a necessity even more than before, especially for school activities and businesses. This increased usage emphasized the importance of cybersecurity, a frequently overlooked subject by the common users, which in return plays a crucial role in safe Internet browsing. This paper introduces an approach grounded in Natural Language Processing techniques to identify the main trends in security news and empowers the analysis of vulnerable products, active attacks, as well as existing methods of defence against new attacks. Our dataset consists of 2264 news articles published on cybersecurity dedicated websites between January 2017 and May 2021. The RoBERTa language model was used to compute the texts embeddings, followed by dimensionality reduction techniques and topic clustering methods. Articles were grouped into approximately 20 clusters that were thoroughly evaluated in terms of importance and evolution. © 2021 IEEE.
ABSTRACT
With the spread of the COVID-19 over the globe, it has conducted a large amount of misinformation and fake news on social networking sites. In this situation, when true and accurate information is necessary for public safety and health, fake news related to COVID-19 has spread rapidly, even quicker than the truth. Rational confusion can be caused by this fake news and put people's lives in danger during times like the COVID-19 pandemic. We used the COVID-19 Fake News dataset to conduct a study to compare the effect of various machine learning-related approaches. We looked at different traditional machine learning models and deep learning language models for detecting fake news and compared their results in multiple ways. We discovered that LSTM and similar neural network models are the most effective at detecting fake news, especially with large datasets. We are confident that our benchmark study will assist the research community and various news blogs/sites to choose the best fake news detection algorithm. © 2021 IEEE.