News Detection Research Articles

Escaping the neutralization effect of modality features fusion in multimodal Fake News Detection

Fake news spreads at unprecedented speeds through online social media, raising many concerns and negative impacts on a variety of domains. To control this issue, Fake News Detection (FND) naturally becomes the chief task while multimodal FND has recently attracted more attention due to the fast-growing multimodal content in online social media. Commonly, the existing multimodal FND methods directly fuse the unimodal features into the multimodal features before detecting the veracity, however, such methods result in the neutralization effect problem caused by the veracity conflict between different modalities. To alleviate this problem, we propose a novel multimodal FND method, namely MultImodal fake NEws detectoR with Unimodal Veracity Signals (Miner-uvs). The basic idea of Miner-uvs is to incorporate an unimodal veracity classifier that can predict the veracity labels of each modality, and then use the unimodal veracity labels to weigh the unimodal features, so as to achieve more discriminative multimodal features. Because the unimodal veracity labels are unknown, we formulate the unimodal veracity classification as the Positive and Unlabeled (PU) learning problem; and solve it by using a variational PU learning method with a contrastive multimodal alignment regularization. To evaluate the effectiveness of Miner-uvs, we compare it with dozens of existing multimodal FND methods across benchmark datasets. Experimental results demonstrate that Miner-uvs consistently outperforms the existing competitors.

Research on Fake News Detection Based on Dual Evidence Perception

With the development of online social networks, the prevalence of fake news on social media is increasing, leading to a heightened focus on detecting fake news. To combat this, researchers have developed various strategies, including text-based and fact-checking approaches. The text-based approach analyses linguistic features within fake news posts, while the fact-checking method verifies statements against an external knowledge base, disregarding the linguistic patterns between posts. Despite their differences, both approaches offer complementary benefits in the fight against fake news, highlighting the importance of integrating these methods. However, there has been relatively limited investigation into merging these two strategies into a unified framework. In this paper, we proposed the FakE News Detection Based on Dual Evidence Perception (DEP-FEND) method. This novel approach merges text-based and fact-checking techniques into a unified framework to enhance fake news detection. DEP-FEND consists of three sub-modules: evidence perception based on the historical news environment, evidence perception from an external knowledge base, and the fake news detector. Initially, two independent modules were developed to gather evidence from the historical news environment and an external knowledge base using the Gaussian kernel soft-counting method, generating two distinct evidence vectors. Subsequently, these two independent perception modules were combined using domain gates, and the posts, along with the combined evidence perception signals, were inputted into the fake news detector. The experimental results on the two Chinese datasets demonstrate that our proposed solution outperformed other fake news detection methods with an average accuracy improvement of 3.6% and a macro-f1 improvement of 2.1%.

MRAN: Multimodal relationship-aware attention network for fake news detection

Existing multimodal fake news detection methods face challenges in jointly capturing the intramodality and cross-modal correlation relationships between image regions and text fragments. Additionally, these methods lack comprehensive hierarchical semantics mining for text. These limitations result in ineffective utilization of multimodal information and impact detection performance. To address these issues, we propose a multimodal relationship-aware attention network (MRAN), which consists of three main steps. First, a multi-level encoding network is employed to extract hierarchical semantic feature representations of text, while the visual feature extractor VGG19 learns image feature representations. Second, the captured text and image representations are input into the relationship-aware attention network, which generates high-order fusion features by calculating the similarity between information segments within modalities and cross-modal similarity. Finally, the fusion features are passed through a fake news detector, which identifies fake news. Experimental results on three benchmark datasets demonstrate the effectiveness of MRAN, highlighting its strong detection performance.

High Performing Explanatory Fake News Classification on Longer Texts

After misinformation became prevalent in 2020, the research community started prioritizing creating state of the art (SOTA) fake news detectors. However, these models did little in changing user attitudes towards misinformation. Therefore, we try to increase trust between users and AI fake news detectors by implementing an explanatory moderator. We started with two research questions: (1) can long texts like normal news articles perform well in current fake news detectors meant for short texts, and (2) can we create a fake news detector that can achieve comparable high performances to SOTA fake news detectors while representing its classifications in explainable visualizations. To fulfill our first research question, we picked WELFake, a dataset containing news articles from four different news platforms. In order to create a comparable, SOTA fake news detector performance, we ran preliminary models of Majority Class Baseline, Random Forest Classifier with bag of words, and the third place model from the AAAI 2021 Shared Task: COVID-19 Fake News Detection in English competition with WELFake. Lastly, we fulfilled our second research question by making a manually fine-tuned BERT model to access attention masks that we could visualize through BertViz. Our manually fine-tuned BERT model outperformed our comparable, SOTA Two-Fold Four-Model ensemble with a 99.99% test accuracy. We made conclusions that current SOTA fake news detectors made for short texts can perform the same level of accuracy with long texts and explanatory fake news detectors can be comparable to current SOTA models.

News-On-Clix: Enrich Multi-Category News Aggregation with Fake News Detector to Curb Spreading of Misinformation

In the era of Technology & Information, with rapid grow in number of social media platforms & user’s using them, the information which is being produced to each & every user is very important and sensitive as it can lead to various serious consequences. Misinformation is spread intentionally to disturb the harmony and peace also they hide their anonymity which gives them the advantage to do it on large scale. Our model contrasts from other models in-terms of imbalanced data which leads to skewed class proportions creating uneven curve distribution as well as offers a news classification on various domains such as Politics, Sports, Entertainment, Business, Health, etc. “Fake news can imply things like- a mistake, intentional misleading, twisting a new story, or a complete lie”, also panic which was created during the Covid-19 pandemic is not less. Hence our project aims to focus on identifying fake news using the fake news detector so that people who want to have a reality check on specific news of any related topic can verify and get to know whether the information spreading is true or false

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Feature importance in the age of explainable AI: Case study of detecting fake news & misinformation via a multi-modal framework

In recent years, fake news has become a global phenomenon due to its explosive growth and ability to leverage multimedia content to manipulate user opinions. Fake news is created by manipulating images, text, audio, and videos, particularly on social media, and the proliferation of such disinformation can trigger detrimental societal effects. False forwarded messages can have a devastating impact on society, spreading propaganda, inciting violence, manipulating public opinion, and even influencing elections. A major shortcoming of existing fake news detection methods is their inability to simultaneously learn and extract features from two modalities and train models with shared representations of multimodal (textual and visual) information. Feature engineering is a critical task in the fake news detection model's machine learning (ML) development process. For ML models to be explainable and trusted, feature engineering should describe how many features used in the ML models contribute to making more accurate predictions. Feature engineering, which plays an important role in the development of an explainable AI system by shaping the features used in the ML models, is an interconnected concept with explainable AI as it affects the model's interpretability. In the research, we develop a fake news detector model in which we (1) identify several textual and visual features that are associated with fake or credible news; specifically, we extract features from article titles, contents, and, top images; (2) investigate the role of all multimodal features (content, emotions and manipulation-based) and combine the cumulative effects using the feature engineering that represent the behavior of fake news propagators; and (3) develop a model to detect disinformation on benchmark multimodal datasets consisting of text and images. We conduct experiments on several real-world multimodal fake news datasets, and our results show that on average, our model outperforms existing single-modality methods by large margins that do not use any feature optimization techniques.

The Misinformation Susceptibility Test (MIST): A psychometrically validated measure of news veracity discernment.

Interest in the psychology of misinformation has exploded in recent years. Despite ample research, to date there is no validated framework to measure misinformation susceptibility. Therefore, we introduce Verification done, a nuanced interpretation schema and assessment tool that simultaneously considers Veracity discernment, and its distinct, measurable abilities (real/fake news detection), and biases (distrust/naïvité-negative/positive judgment bias). We then conduct three studies with seven independent samples (Ntotal = 8504) to show how to develop, validate, and apply the Misinformation Susceptibility Test (MIST). In Study 1 (N = 409) we use a neural network language model to generate items, and use three psychometric methods-factor analysis, item response theory, and exploratory graph analysis-to create the MIST-20 (20 items; completion time < 2 minutes), the MIST-16 (16 items; < 2 minutes), and the MIST-8 (8 items; < 1 minute). In Study 2 (N = 7674) we confirm the internal and predictive validity of the MIST in five national quota samples (US, UK), across 2 years, from three different sampling platforms-Respondi, CloudResearch, and Prolific. We also explore the MIST's nomological net and generate age-, region-, and country-specific norm tables. In Study 3 (N = 421) we demonstrate how the MIST-in conjunction with Verification done-can provide novel insights on existing psychological interventions, thereby advancing theory development. Finally, we outline the versatile implementations of the MIST as a screening tool, covariate, and intervention evaluation framework. As all methods are transparently reported and detailed, this work will allow other researchers to create similar scales or adapt them for any population of interest.

FAKE NEWS DETECTOR USING DEEP LEARNING

In recent years, there has been an increase in worry about the presence of false news on the internet. Real and false news have becoming harder to identify as social media and other online platforms have rapidly replaced print media as many peoples major news sources. Techniques for natural language processing (NLP) have been developed to help identify and categorize bogus news in attempt to solve this problem. Examining the language used in news items is one method of spotting false information. Large datasets of news stories may be used to train NLP models to find linguistic patterns that are suggestive of false news. For instance, false news stories may employ dramatic or exaggerated language, or they may not cite reliable sources to back up their assertions. NLP models may be trained to recognize these patterns and spot news stories that are probably fraudulent by utilizing machine learning methods. One other method for spotting false news is to examine the social environment in which news pieces are circulated. The sharing and consumption of news stories by users is extensively documented by social media companies. NLP algorithms can spot stories that are probably false by looking for trends in social media activity. A sign that a news story is phony can be, for instance, if it is being shared quickly by a lot of bot accounts. Having access to good training data is crucial for developing an NLP-based false news detector. Real and false news stories should also be included in this data, along with information on how the pieces were shared and read on social media. Researchers may create algorithms that can precisely distinguish between real and false news stories by training NLP models on this data. Overall, the development of NLP algorithms for false news identification is a crucial milestone in the fight against the spread of untruth on the internet. We can create tools that assist individuals identify between true and false news and, in turn, foster a more informed and involved society by utilizing the power of machine learning and natural language processing.

A Unified Training Process for Fake News Detection Based on Finetuned Bidirectional Encoder Representation from Transformers Model.

An efficient fake news detector becomes essential as the accessibility of social media platforms increases rapidly. Previous studies mainly focused on designing the models solely based on individual data sets and might suffer from degradable performance. Therefore, developing a robust model for a combined data set with diverse knowledge becomes crucial. However, designing the model with a combined data set requires extensive training time and sequential workload to obtain optimal performance without having some prior knowledge about the model's parameters. The presented study here will help solve these issues by introducing the unified training strategy to have a base structure for the classifier and all hyperparameters from individual models using a pretrained transformer model. The performance of the proposed model is noted using three publicly available data sets, namely ISOT and others from the Kaggle website. The results indicate that the proposed unified training strategy surpassed the existing models such as Random Forests, convolutional neural networks, and long short-term memory, with 97% accuracy and achieved the F1 score of 0.97. Furthermore, there was a significant reduction in training time by almost 1.5 to 1.8 × by removing words lower than three letters from the input samples. We also did extensive performance analysis by varying the number of encoder blocks to build compact models and trained on the combined data set. We justify that reducing encoder blocks resulted in lower performance from the obtained results.

Covid-19 Fake News Detection on Twitter Based on Author Credibility Using Information Gain and KNN MethodsCovid-19 Fake News Detection on Twitter Based on Author Credibility Using Information Gain and KNN Methods

Twitter is one of the social media that is used as a tool to share various kinds of information about various kinds of things that are of concern to social media users. One of the information shared is information about COVID-19, which is known that the COVID-19 pandemic is currently spreading throughout the world at a very alarming rate. COVID-19 is an infectious disease caused by SARS-COV-2. The World Health Organization (WHO) claims that the spread of COVID-19 is supported by the spread of false/fake news. So to find out the truth of the news, a COVID-19 fake news detector is needed so that users don't fall for the hoaxes circulating. This study aims to classify COVID-19 news on Twitter based on author credibility. Credibility in question is a person's perception of the validity of information and is a multidimensional concept that is used as a means of receiving information to assess the source of communication. The method used in this research is Information Gain and KNN. KNN (K-Nearest Neighbor) is a supervised learning algorithm that works by classifying a set of data based on classified training data. Information Gain is used to ranking the most influential attributes, and KNN is used to classify data based on learning data taken from the nearest neighbors. The research consists of 6 main stages, namely data collection (crawling data), data preprocessing, feature extraction, feature selection, data split into training data and testing data, KNN stage, and data evaluation stage. The research carried out succeeded in obtaining an accuracy value of 91%, a correlation value between credibility and hoax of 0.115, and a p-value &lt;0.005.&#x0D;

Fake News Detection Using Deep Learning

Abstract: Social media news may be a double-edged sword. There are a number of benefits to utilizing it: It's simple to use, takes little time, and is user-friendly. It's also simple to share socially significant data with others. On the other hand, a number of social networking sites adapt the news based on personal opinions and interests. This sort of misinformation is spread over social media with the intent of causing harm to a person, organization, or institution. Because of the prevalence of fake news, computer tools are needed to detect it. Fake news detection aims to aid users in spotting various sorts of fake news. We can tell if the news is genuine or created if we have encountered fake or authentic news before. We may use a number of models to understand social media news. This is a donation in two ways. We must first give datasets containing both fake and accurate news and conduct multiple experiments before developing a false news detector. Various machine learning techniques are used to categorize the data. Random Forest, Logistic Regression, Naives Bayes, Gradient Boost and Decision Tree techniques are used and compared. It was found that Gradient Boost has the best accuracy.

Semantic‐enhanced multimodal fusion network for fake news detection

The increasing popularity of social media facilitates the propagation of fake news, posing a major threat to the government and journalism, and thereby making how to detect fake news from social media an urgent requirement. In general, multimodal-based methods can achieve better performance because of the complementation among different modalities. However, the majority of them simply concatenate features from different modalities, failing to well preserve the mutual information in common features. To address this issue, a novel framework named semantic-enhanced multimodal fusion network is proposed for fake news detection, which can better capture mutual features among events and thus benefit the detection of fake news. This model consists of three subnetworks, namely multimodal fusion and event domain adaptation networks as well as the fake news detector. Specifically, the multimodal fusion network aims to extract deep features from texts and images and fuse them into a common semantic feature known as a snapshot. Then, the fake news detector can learn the representation of posts. Finally, the event domain adaptation network can single out and remove the peculiar features of each event, and keep shared features among events. The experimental results show that the proposed model outperforms some state-of-the-art approaches on two real-world multimedia data sets.

Fake News Classifier

Abstract: Fake news is incorrect information that is spread through a social network to harm individuals, authorities or organizations. The spread of fake news poses great challenges to society. Fake news is difficult to detect, but it is easy to spread and have widespread effects. Automated analysis of the reliability of articles is the subject of ongoing research. To address this issue, we offer a model that detects fake information and communications using deep learning and natural language processing. This paper presents a fake news detection model based on LSTM (Long Short-Term Memory) and Bi-LSTM (Bidirectional Long Short-Term Memory). In the first place, we want to present a dataset containing both fake news and genuine news, and perform various tests to sort out a fake news detector. The model was prepared and assessed utilizing a fake news dataset got from Kaggle.

Deep Convolutional Neural Network for Fake News Detection over Online Social Networks

With the proliferation of Online Social Networks (OSNs), people of all walks of life are able to have instant sharing of information and opinions. Thus they became virtual platforms for exchanging views and making friends. The news shared over social media helps in understanding various aspects. They also come in the form of reviews or product or service news that will help people in making well informed decisions. As people are increasingly relying on social media to ascertain knowledge of products or services, the news over social media needs to be genuine. Unfortunately, it is observed that there are some fake news articles or reviews over OSNs. Unless, such news is identified and eliminated, it will cause people in making wrong decisions. They influence of fake news on people will defeater the purpose of social media content. The existing machine learning algorithms used for fake news detection provide a great deal of advantages in detection of fake news. However, they suffer from deterioration of performance due to lack of multiple layers of training. This is achieved by deep learning techniques. In this paper, a deep Convolutional Neural Network (CNN) is used for prediction of fake news. An algorithm known as Deep Fake News Detection (DFND) is proposed and implemented. A prototype application is built using Python data science platform to show the utility of the proposed system. The empirical results of the proposed system are compared with that of existing ones. Keywords – Deep Learning, Convolutional Neural Network, Fake News Detection Deep Fake News Detection Algorithm

A Survey on Fake News Detection using Support Vector Model

In the boosting period of Social Media availability and easy availability of Internet to end users in various regions, many challenges are also occurred with usage of this technology. Fake news spreading in various field is also a major challenge in recent time. Fake news has been spreading into vast in significant numbers for various business reasons and also for political reasons. Problem of fake news has become frequent in the online world. People can get affected and their view are also affected easily by these type of fake news for its fabricated words. This type of news has enormous effects on the offline community in various sectors. In this way it is an interesting topic for research. Significant research has been conducted on the detection of fake news from English texts and other languages but there is chancel to improve the work with other languages as well as from multiple sources. Various algorithms like SVM and other supervised algorithm can be helpful to classify fake news. As Sentiment Score is an also a major point for detection of Fake news; in our work we are applying SVM algorithm with TF/IDF, multiple Languages (Language Conversation) etc.”

An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19

Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure.

Hybrid Metaheuristic Approach for Detection of Fake News on Social Media

Hybrid Metaheuristic Approach for Detection of Fake News on Social Media

An Experimental Study of Fake News Detection on Covid-19 Twitter data and a Conceptual framework for the Early Detection and Prevention of Fake News in Social Media

An Experimental Study of Fake News Detection on Covid-19 Twitter data and a Conceptual framework for the Early Detection and Prevention of Fake News in Social Media

Survey on Fake News or Truth - Rumours Detection using Machine Learning

This study is to help readers to understand detection of fake news using machine learning. The main purpose of the planned system is to build an application which identifies fake news stories from a bunch of news stories to make people aware of fake news rumours. With the help of machine learning algorithms, we can detect and separate out the fake news. Nowadays, it is become harder to identify the original source of news stories, like looking for a needle in a haystack. In the modern world, news is a kind of communication that keeps us up to date on the latest events, topics, and people in the wider globe. A society relies on news for a variety of reasons, the most important of which is informing its members about events taking on in and around them that might influence them. Oral and traditional media, as well as digital communication methods, altered videos, memes, unconfirmed marketing, and social media have all contributed to the spread of rumors. As nowadays many people use social media in many cases people get wrong and misleading information and people share those stories without verifying whether it is real or fake news stories. Spreading false information on social media has become a major problem these days. That is why we need a system that can tell us whether something is false news or not. Applications are: 1. Fake news may be detected on social media using this approach. 2. The system can be used to help news channels to broadcast only real and classified news. 3. Users can easily detect and eliminate fake articles that contain misinformation intended to mislead readers.