XFake: Explainable Fake News Detector with Visualizations

Yang, Fan; Pentyala, Shiva; Mohseni, Sina; Du, Mengnan; Yuan, Hao; Linder, Rhema; Ragan, Eric D.; Ji, Shuiwang; Hu, Xia

doi:10.1145/3308558.3314119

Cited by 73 publications

(31 citation statements)

References 8 publications

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“…It leverages a GCN to learn the patterns of rumor propagation. a CNN+RNN model has been also introduced to detect fake Using user characteristics to create a feature vector, modeling five minutes of tweets into a time interval [27,28,29,30]. Other researchers combined convolutional neural networks (CNN) and long short-term memory (LSTM) to detect rumors based on the relationship between user and textual information [24].…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

HANN: Hybrid Attention Neural Network for Detecting Covid-19 Related Rumors

et al. 2022

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In the age of social media, the spread of rumors is becoming easier due to the proliferation of communication and information dissemination platforms. Detecting rumors is a major problem with significant consequences for the economy, democracy, and public safety. Deep learning approaches were used to classify rumors and have yielded state-of-the-art results. Nevertheless, the majority of techniques do not attempt to explain why or how decisions are made. This paper introduces a hybrid attention neural network (HANN) to identify rumors from social media. The advantage of HANN is that it will allow the main user to capture the relative and important features between different classes as well as provide an explanation of the model's decisions. Two deep neural networks are included in the proposal: CNNs and Bidirectional Long Short Term Memory (Bi-LSTM) networks with attention modules. In this paper, the model is trained using a benchmark dataset containing 3612 distinct tweets crawled from Twitter include several types of rumors related to COVID-19. Each subset of data has a balanced label distribution with 1480 rumors tweets (46.87% ) and 1677 non-rumors tweets (53.12% ). The experimental results demonstrate that the new approach (HANN model) performs better results in terms of performance and accuracy (about 0.915% ) than many contemporary models (AraBERT, MARBEART, PCNN,LSTM, LSTM-PCNN and Attention LSTM). Moreover, a number of software engineering features such as followers, friends, and registration age are used to enhance the model's accuracy.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

HANN: Hybrid Attention Neural Network for Detecting Covid-19 Related Rumors

et al. 2022

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“…By understanding the outputs achieved from these frameworks, they could derive appropriate explanations for interpreting detection outcomes. [54] .In 2009, the SHAP (for Shapley Additive Explanations) method was analyzed to explain a model by Julio C. S. Reis and André Correia with many others. [57] Most of the explanations were proposed to derive explanation from the perspectives of news contents and user comments [57], for which many researchers used Graphaware Co-Attention Networks [60].…”

Section: Credibility Assessmentmentioning

confidence: 99%

Explainable AI for Multimodal Credibility Analysis: Case Study of Online Beauty Health (Mis)-Information

et al. 2021

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One person's data or experience is another person's information" this has become the golden rule of the 21st century which has resulted in a massive reservoir of data and immense amounts of information generation. However, there is no control over the source of this information, accessibility of this information, or the quality of it, which has given rise to the presence of "misinformation". The research community has reacted by proposing frameworks and difficulties, which are helpful for (different subtasks of) recognizing misinformation. Most of these frameworks, however, fail to consider all the aspects that can contribute to making information "credible". Furthermore, a valid explanation for each considered feature's contribution to the model's decision stands missing in most work. With this in mind, the authors have attempted to produce a system that yields highly accurate decisions, thus effectively separating credible health blogs from their non-credible counterparts while providing valid user-friendly explanations. The study proposes an Explainable AI-assisted Multimodal Credibility Assessment System that examines the credibility of the platform where the blog is hosted, the credibility of the author of the blog and the credibility of the images that contribute to the blog. This novel framework contributes to the existing body of knowledge by assessing the credibility of misleading beauty blogs using multiple crucial modalities which would lead to an insightful information consumption by the users. The proposed pipeline was successfully implemented on multiple carefully curated datasets and correctly identified 274 non credible blogs out of 321 blogs with an accuracy of 97.5%, Precision of 0.973 & F1score of 0.986. Further, the Explainable AI model, with the help of several visualizations displayed the feature contributions for each blog & it's impact and magnitude in a concise comprehensible format. The framework can be further customized and applied to various domains where presence of misinformation is of high concern such as pharmaceutical drug information, pandemic management, financial advisories, online healthcare services and cyber frauds.

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“…57 Because the focus of this paper is on the human-subjects experiment and its findings, we limit the description of our system to an abbreviated summary, though we aim to provide enough details that a skilled machine-learning system designer could develop a similar system. For a more detailed description of the machine-learning aspects of the system, we refer the reader to the following demo paper 58 that describes the XFake architecture and technical details in full.…”

Section: News Statement Data and Classifiermentioning

confidence: 99%

How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checking

et al. 2021

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Explainable artificial intelligence (XAI) systems aim to provide users with information to help them better understand computational models and reason about why outputs were generated. However, there are many different ways an XAI interface might present explanations, which makes designing an appropriate and effective interface an important and challenging task. Our work investigates how different types and amounts of explanatory information affect user ability to utilize explanations to understand system behavior and improve task performance. The presented research employs a system for detecting the truthfulness of news statements. In a controlled experiment, participants were tasked with using the system to assess news statements as well as to learn to predict the output of the AI. Our experiment compares various levels of explanatory information to contribute empirical data about how explanation detail can influence utility. The results show that more explanation information improves participant understanding of AI models, but the benefits come at the cost of time and attention needed to make sense of the explanation.

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XFake: Explainable Fake News Detector with Visualizations

Cited by 73 publications

References 8 publications

HANN: Hybrid Attention Neural Network for Detecting Covid-19 Related Rumors

HANN: Hybrid Attention Neural Network for Detecting Covid-19 Related Rumors

Explainable AI for Multimodal Credibility Analysis: Case Study of Online Beauty Health (Mis)-Information

How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checking

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