Query: fake new

title: Use of a Capsule Network to Detect Fake Images and Videos
abstract: The revolution in computer hardware, especially in graphics processing units and tensor processing units, has enabled significant advances in computer graphics and artificial intelligence algorithms. In addition to their many beneficial applications in daily life and business, computer generated/manipulated images and videos can be used for malicious purposes that violate security systems, privacy, and social trust. The deepfake phenomenon and its variations enable a normal user to use his or her personal computer to easily create fake videos of anybody from a short real online video. Several countermeasures have been introduced to deal with attacks using such videos. However, most of them are targeted at certain domains and are ineffective when applied to other domains or new attacks. In this paper, we introduce a capsule network that can detect various kinds of attacks, from presentation attacks using printed images and replayed videos to attacks using fake videos created using deep learning. It uses many fewer parameters than traditional convolutional neural networks with similar performance. Moreover, we explain, for the first time ever in the literature, the theory behind the application of capsule networks to the forensics problem through detailed analysis and visualization.

title: Combating Fake News: A Survey on Identification and Mitigation Techniques
abstract: The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.

title: Behind the cues: A benchmarking study for fake news detection
abstract: Fake news has become a problem of great impact in our information driven society because of the continuous and intense fakesters content distribution. Information quality in news feeds is under questionable veracity calling for automated tools to detect fake news articles. Due to many faces of fakesters, creating such tool is a challenging problem. In this work, we propose a model for fake news detection using content based features and Machine Learning (ML) algorithms. To conclude in most accurate model we evaluate several feature sets proposed for deception detection and word embeddings as well. Moreover, we test the most popular ML classifiers and investigate the possible improvement reached under ensemble ML methods such as AdaBoost and Bagging. An extensive set of earlier data sources has been used for experimentation and evaluation of both feature sets and ML classifiers. Moreover, we introduce a new text corpus, the "UNBiased" (UNB) dataset, which integrates various news sources and fulfills several standards and rules to avoid biased results in classification task. Our experimental results show that the use of an enhanced linguistic feature set with word embeddings along with ensemble algorithms and Support Vector Machines (SVMs) is capable to classify fake news with high accuracy.

title: Supervised Learning for Fake News Detection
abstract: A large body of recent works has focused on understanding and detecting fake news stories that are disseminated on social media. To accomplish this goal, these works explore several types of features extracted from news stories, including source and posts from social media. In addition to exploring the main features proposed in the literature for fake news detection, we present a new set of features and measure the prediction performance of current approaches and features for automatic detection of fake news. Our results reveal interesting findings on the usefulness and importance of features for detecting false news. Finally, we discuss how fake news detection approaches can be used in the practice, highlighting challenges and opportunities.

title: A Topic Agnostic Approach for Identifying Fake News Pages
abstract: Fake news and misinformation have been increasingly used to manipulate popular opinion and influence political processes. To better understand fake news, how they are propagated, and how to counter their effect, it is necessary to first identify them. Recently, approaches have been proposed to automatically classify articles as fake based on their content. An important challenge for these approaches comes from the dynamic nature of news: as new political events are covered, topics and discourse constantly change and thus, a classifier trained using content from articles published at a given time is likely to become ineffective in the future. To address this challenge, we propose a topic agnostic (TAG) classification strategy that uses linguistic and web markup features to identify fake news pages. We report experimental results using multiple data sets which show that our approach attains high accuracy in the identification of fake news, even as topics evolve over time.

title: Using Blockchain to Rein in the New Post Truth World and Check the Spread of Fake News
abstract: In recent years, "fake news" has become a global issue that raises unprecedented challenges for human society and democracy. This problem has arisen due to the emergence of various concomitant phenomena such as 1) the digitization of human life and the ease of disseminating news through social networking applications (such as Facebook and WhatsApp) 2) the availability of "big data" that allows customization of news feeds and the creation of polarized so called "filter bubbles" and 3) the rapid progress made by generative machine learning (ML) and deep learning (DL) algorithms in creating realistic looking yet fake digital content (such as text, images, and videos) There is a crucial need to combat the rampant rise of fake news and disinformation. In this article, we propose a high level overview of a blockchain based framework for fake news prevention and highlight the various design issues and consideration of such a blockchain based framework for tackling fake news.

title: Combating Fake News
abstract: The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.

title: Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online
abstract: Fake or manipulated images propagated through the Web and social media have the capacity to deceive, emotionally distress, and influence public opinions and actions. Yet few studies have examined how individuals evaluate the authenticity of images that accompany online stories. This article details a 6 batch large scale online experiment using Amazon Mechanical Turk that probes how people evaluate image credibility across online platforms. In each batch, participants were randomly assigned to 1 of 28 news source mockups featuring a forged image, and they evaluated the credibility of the images based on several features. We found that participants' Internet skills, photo editing experience, and social media use were significant predictors of image credibility evaluation, while most social and heuristic cues of online credibility (e.g. source trustworthiness, bandwagon, intermediary trustworthiness) had no significant impact. Viewers' attitude toward a depicted issue also positively influenced their credibility evaluation.

title: Graph Neural Networks with Continual Learning for Fake News Detection from Social Media
abstract: Although significant effort has been applied to fact checking, the prevalence of fake news over social media, which has profound impact on justice, public trust and our society, remains a serious problem. In this work, we focus on propagation based fake news detection, as recent studies have demonstrated that fake news and real news spread differently online. Specifically, considering the capability of graph neural networks (GNNs) in dealing with non Euclidean data, we use GNNs to differentiate between the propagation patterns of fake and real news on social media. In particular, we concentrate on two questions: (1) Without relying on any text information, e.g. tweet content, replies and user descriptions, how accurately can GNNs identify fake news? Machine learning models are known to be vulnerable to adversarial attacks, and avoiding the dependence on text based features can make the model less susceptible to the manipulation of advanced fake news fabricators. (2) How to deal with new, unseen data? In other words, how does a GNN trained on a given dataset perform on a new and potentially vastly different dataset? If it achieves unsatisfactory performance, how do we solve the problem without re training the model on the entire data from scratch? We study the above questions on two datasets with thousands of labelled news items, and our results show that: (1) GNNs can achieve comparable or superior performance without any text information to state of the art methods. (2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem this issue has not been addressed in the previous work that applies GNNs for fake news detection. In order to solve the problem, we propose a method that achieves balanced performance on both existing and new datasets, by using techniques from continual learning to train GNNs incrementally.

title: MFC GAN: Class imbalanced dataset classification using Multiple Fake Class Generative Adversarial Network
abstract: Class imbalanced datasets are common across different domains such as health, banking, security and others. With such datasets, the learning algorithms are often biased toward the majority class instances. Data augmentation is a common approach that aims at rebalancing a dataset by injecting more data samples of the minority class instances. In this paper, a new data augmentation approach is proposed using a Generative Adversarial Networks (GAN) to handle the class imbalance problem. Unlike common GAN models, which use a single fake class, the proposed method uses multiple fake classes to ensure a fine grained generation and classification of the minority class instances. Moreover, the proposed GAN model is conditioned to generate minority class instances aiming at rebalancing the dataset. Extensive experiments were carried out using public datasets, where synthetic samples generated using our model were added to the imbalanced dataset, followed by performing classification using Convolutional Neural Network. Experiment results show that our model can generate diverse minority class instances, even in extreme cases where the number of minority class instances is relatively low. Additionally, superior performance of our model over other common augmentation and oversampling methods was achieved in terms of classification accuracy and quality of the generated samples.