Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
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TLDR
Wang et al. as discussed by the authors presented a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets.Abstract:
Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.read more
Citations
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Journal ArticleDOI
Fake news detection using deep learning models: A novel approach
TL;DR: This study compares multiple state‐of‐the‐art approaches such as convolutional neural networks (CNNs), long short‐term memories (LSTMs), ensemble methods, and attention mechanisms and concludes that CNN + bidirectional LSTM ensembled network with attention mechanism achieved the highest accuracy.
Journal ArticleDOI
Fake news y coronavirus: detección de los principales actores y tendencias a través del análisis de las conversaciones en Twitter
TL;DR: Agarwal et al. as mentioned in this paper investigate a red tejida alrededor de las noticias falsas that circulan en Twitter sobre the pandemia del coronavirus mediante la tecnica del analisis de redes sociales.
Journal ArticleDOI
FNED: A Deep Network for Fake News Early Detection on Social Media
Yang Liu,Yi-Fang Brook Wu +1 more
TL;DR: Zhang et al. as discussed by the authors proposed a novel deep neural network to detect fake news early using a status-sensitive crowd response feature extractor that extracts both text features and user features from combinations of users' text response and their corresponding user profiles, and a position-aware attention mechanism that highlights important user responses at specific ranking positions.
Journal ArticleDOI
A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks
TL;DR: A multimodal fake news detection framework based on Crossmodal Attention Residual and Multichannel convolutional neural Networks (CARMN) is proposed and it is demonstrated that the proposed model outperforms the state-of-the-art methods and learns more discriminable feature representations.
Journal ArticleDOI
Rising Above Misinformation or Fake News in Africa: Another Strategy to Control COVID-19 Spread
Bright Opoku Ahinkorah,Edward Kwabena Ameyaw,John Elvis Hagan,John Elvis Hagan,Abdul-Aziz Seidu,Thomas Schack +5 more
TL;DR: A clear understanding is provided on some COVID-19 misinformation, the inherent implications this poses to public health in Africa and the potential strategies to curb this trend are highlighted.
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