18,761 research outputs found
Fake News Detection in Social Media Using Machine Learning and Deep Learning
Fake news detection in social media is a process of detecting false information that is intentionally created to mislead readers. The spread of fake news may cause social, economic, and political turmoil if their proliferation is not prevented. However, fake news detection using machine learning faces many challenges. Datasets of fake news are usually unstructured and noisy. Fake news often mimics true news. In this study, a data preprocessing method is proposed for mitigating missing values in the datasets to enhance fake news detection accuracy. The experimental results show that Multi- Layer Perceptron (MLP) classifier combined with the proposed data preprocessing method outperforms the state-of-the-art methods.
Furthermore, to improve the early detection of rumors in social media, a time-series model is proposed for fake news detection in social media using Twitter data. With the proposed model, computational complexity has been reduced significantly in terms of machine learning models training and testing times while achieving similar results as state-of-the-art in the literature. Besides, the proposed method has a simplified feature extraction process, because only the temporal features of the Twitter data are used. Moreover, deep learning techniques are also applied to fake news detection. Experimental results demonstrate that deep learning methods outperformed traditional machine learning models. Specifically, the ensemble-based deep learning classification model achieved top performance
Weak Supervision for Fake News Detection via Reinforcement Learning
Today social media has become the primary source for news. Via social media
platforms, fake news travel at unprecedented speeds, reach global audiences and
put users and communities at great risk. Therefore, it is extremely important
to detect fake news as early as possible. Recently, deep learning based
approaches have shown improved performance in fake news detection. However, the
training of such models requires a large amount of labeled data, but manual
annotation is time-consuming and expensive. Moreover, due to the dynamic nature
of news, annotated samples may become outdated quickly and cannot represent the
news articles on newly emerged events. Therefore, how to obtain fresh and
high-quality labeled samples is the major challenge in employing deep learning
models for fake news detection. In order to tackle this challenge, we propose a
reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which
can leverage users' reports as weak supervision to enlarge the amount of
training data for fake news detection. The proposed framework consists of three
main components: the annotator, the reinforced selector and the fake news
detector. The annotator can automatically assign weak labels for unlabeled news
based on users' reports. The reinforced selector using reinforcement learning
techniques chooses high-quality samples from the weakly labeled data and
filters out those low-quality ones that may degrade the detector's prediction
performance. The fake news detector aims to identify fake news based on the
news content. We tested the proposed framework on a large collection of news
articles published via WeChat official accounts and associated user reports.
Extensive experiments on this dataset show that the proposed WeFEND model
achieves the best performance compared with the state-of-the-art methods.Comment: AAAI 202
An Implementation of Machine Learning Algorithm for Fake News Detection
Fake news is a growing concern in the age of social media, as it can spread rapidly and have serious consequences. To combat this issue, machine learning techniques have been used for fake news detection. In this study, we propose two models, LSTM and SVM, for fake news detection. The LSTM model is a deep learning algorithm that is particularly suited to sequential data such as text. It can capture long-term dependencies in the text and has shown promising results in natural language processing tasks. The SVM model, on the other hand, is a classical machine learning algorithm that has been widely used for classification tasks. To evaluate the performance of the proposed models, we conducted experiments on a dataset of news articles. Our results show that both models achieve high accuracy in detecting fake news. However, the LSTM model outperforms the SVM model with an accuracy of 94% compared to 89%. Furthermore, we conducted a feature importance analysis to determine the most important features for detecting fake news. The results show that the presence of certain words and phrases, such as "unverified" and "anonymous sources", are strong indicators of fake news. In conclusion, our study demonstrates the effectiveness of using machine learning techniques, particularly LSTM and SVM, for detecting fake news. This research can be applied to assist individuals and organizations in identifying and combating fake news in the digital age
Survei Literatur: Deteksi Berita Palsu Menggunakan Pendekatan Deep Learning
Social media has become an inseparable part of everyday life in modern society to make it easier to interact and communicate with each other. The purpose of this study is to review and compare the deep learning methods implemented in the case of detecting fake news from several previous studies, and to get an overview of the corpus or dataset used by previous studies. This research is also to help researchers identify and map the use of deep learning algorithms in cases of detecting fake news. The research method is conducting a literature survey of 12 literatures obtained from the ScienceDirect and IEEE Xplore websites. The collection of literature that has been surveyed is selected based on the year published in 2021 with the topic of research on detection of fake news using a deep learning approach. The results of this study summarize that the strategy to detect fake news can be done with four approaches, based on the content, based on the writing style, based on the distribution pattern, and based on the credibility of the source. The results of this research also show that the Convolutional Neural Network algorithm is a favorite of researchers by appearing 6 times in the literature collection. The next favorite algorithm is Long Short Term Memory which appears in 5 literatures and Bidirectional LSTM which appears in 4 literatures
A scientometric analysis of deep learning approaches for detecting Fake News
The unregulated proliferation of counterfeit news creation and dissemination that has been
seen in recent years poses a constant threat to democracy. Fake news articles have the power to
persuade individuals, leaving them perplexed. This scientometric study examined 569 documents
from the Scopus database between 2012 and mid-2022 to look for general research trends, publication
and citation structures, authorship and collaboration patterns, bibliographic coupling, and productivity patterns in order to identify fake news using deep learning. For this study, Biblioshiny and
VOSviewer were used. The findings of this study clearly demonstrate a trend toward an increase in
publications since 2016, and this dissemination of fake news is still an issue from a global perspective.
Thematic analysis of papers reveals that research topics related to social media for surveillance and
monitoring of public attitudes and perceptions, as well as fake news, are crucial but underdeveloped,
while studies on deep fake detection, digital contents, digital forensics, and computer vision constitute
niche areas. Furthermore, the results show that China and the USA have the strongest international
collaboration, despite India writing more articles. This paper also examines the current state of the art
in deep learning techniques for fake news detection, with the goal of providing a potential roadmap
for researchers interested in undertaking research in this fiel
Uncovering Semantic Inconsistencies and Deceptive Language in False News Using Deep Learning and NLP Techniques for Effective Management
In today's information age, false news and deceptive language have become pervasive, leading to significant challenges for individuals, organizations, and society as a whole. This study focuses on the application of deep learning and natural language processing (NLP) techniques to uncover semantic inconsistencies and deceptive language in false news, with the aim of facilitating effective management strategies.
The research employs advanced deep learning models and NLP algorithms to analyze large volumes of textual data and identify patterns indicative of deceptive language and semantic inconsistencies. By leveraging the power of machine learning, the study aims to enhance the detection and classification of false news articles, enabling proactive management measures. The proposed approach not only examines the superficial aspects of false news but also delves deeper into the linguistic nuances and contextual inconsistencies that are characteristic of deceptive language. By employing advanced NLP techniques, such as sentiment analysis, topic modeling, and named entity recognition, the study strives to identify the underlying manipulative strategies employed by false news purveyors.
The findings from this research have far-reaching implications for effective management. By accurately detecting semantic inconsistencies and deceptive language in false news, organizations can develop targeted strategies to mitigate the spread and impact of misinformation. Additionally, individuals can make informed decisions, enhancing their ability to critically evaluate news sources and protect themselves from falling victim to deceptive practices.
In this research study, we suggest a hybrid system for detecting fake news that incorporates source analysis and machine learning techniques. Our system analyzes the language used in news articles to identify indicators of fake news and evaluates the credibility of the sources cited in the articles. We trained our system using a large dataset of news articles manually annotated as real or fake and evaluated its performance measured by common metrics like F1-score, recall, and precision. In comparison to other advanced fake news detection systems, our results show that our hybrid method has a high level of accuracy in detecting false news
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