The increasing popularity of social media promotes the proliferation of fake news. With the development of multimedia technology, fake news attempts to utilize multimedia contents with images or videos to attract and mislead readers for rapid dissemination, which makes visual contents an important part of fake news. Fake-news images, images attached in fake news posts,include not only fake images which are maliciously tampered but also real images which are wrongly used to represent irrelevant events. Hence, how to fully exploit the inherent characteristics of fake-news images is an important but challenging problem for fake news detection. In the real world, fake-news images may have significantly different characteristics from real-news images at both physical and semantic levels, which can be clearly reflected in the frequency and pixel domain, respectively. Therefore, we propose a novel framework Multi-domain Visual Neural Network (MVNN) to fuse the visual information of frequency and pixel domains for detecting fake news. Specifically, we design a CNN-based network to automatically capture the complex patterns of fake-news images in the frequency domain; and utilize a multi-branch CNN-RNN model to extract visual features from different semantic levels in the pixel domain. An attention mechanism is utilized to fuse the feature representations of frequency and pixel domains dynamically. Extensive experiments conducted on a real-world dataset demonstrate that MVNN outperforms existing methods with at least 9.2 in accuracy, and can help improve the performance of multimodal fake news detection by over 5.2 .
Some early works use basic statistical features about attached images to help classify fake news posts, such as the number of attached images @cite_11 @cite_1 , image popularity and image type @cite_30 . However, these statistical features are so basic that they can hardly represent complex distributions of visual contents in fake news.
@cite_30 @cite_1 @cite_11
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Unlabeled high-dimensional text-image web news data are produced every day, presenting new challenges to unsupervised feature selection on multi-view data. State-of-the-art multi-view unsupervised feature selection methods learn pseudo class labels by spectral analysis, which is sensitive to the choice of similarity metric for each view. For text-image data, the raw text itself contains more discriminative information than similarity graph which loses information during construction, and thus the text feature can be directly used for label learning, avoiding information loss as in spectral analysis. We propose a new multi-view unsupervised feature selection method in which image local learning regularized orthogonal nonnegative matrix factorization is used to learn pseudo labels and simultaneously robust joint @math -norm minimization is performed to select discriminative features. Cross-view consensus on pseudo labels can be obtained as much as possible. We systematically evaluate the proposed method in multi-view text-image web news datasets. Our extensive experiments on web news datasets crawled from two major US media channels: CNN and FOXNews demonstrate the efficacy of the new method over state-of-the-art multi-view and single-view unsupervised feature selection methods. During natural disasters or crises, users on social media tend to easily believe contents of postings related to the events, and retweet the postings with hoping them to be reached to many other users. Unfortunately, there are malicious users who understand the tendency and post misinformation such as spam and fake messages with expecting wider propagation. To resolve the problem, in this paper we conduct a case study of 2013 Moore Tornado and Hurricane Sandy. Concretely, we (i) understand behaviors of these malicious users, (ii) analyze properties of spam, fake and legitimate messages, (iii) propose flat and hierarchical classification approaches, and (iv) detect both fake and spam messages with even distinguishing between them. Our experimental results show that our proposed approaches identify spam and fake messages with 96.43 accuracy and 0.961 F-measure. In today's world, online social media plays a vital role during real world events, especially crisis events. There are both positive and negative effects of social media coverage of events, it can be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news. The aim of this paper, is to highlight the role of Twitter, during Hurricane Sandy (2012) to spread fake images about the disaster. We identified 10,350 unique tweets containing fake images that were circulated on Twitter, during Hurricane Sandy. We performed a characterization analysis, to understand the temporal, social reputation and influence patterns for the spread of fake images. Eighty six percent of tweets spreading the fake images were retweets, hence very few were original tweets. Our results showed that top thirty users out of 10,215 users (0.3 ) resulted in 90 of the retweets of fake images; also network links such as follower relationships of Twitter, contributed very less (only 11 ) to the spread of these fake photos URLs. Next, we used classification models, to distinguish fake images from real images of Hurricane Sandy. Best results were obtained from Decision Tree classifier, we got 97 accuracy in predicting fake images from real. Also, tweet based features were very effective in distinguishing fake images tweets from real, while the performance of user based features was very poor. Our results, showed that, automated techniques can be used in identifying real images from fake images posted on Twitter. When a message, such as a piece of news, spreads in social networks, how can we classify it into categories of interests, such as genuine or fake news? Classification of social media content is a fundamental task for social media mining, and most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags. However, for many emerging applications like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to make it look like real news. To address this problem, this paper concentrates on modeling the propagation of messages in a social network. Specifically, we propose a novel approach, TraceMiner, to (1) infer embeddings of social media users with social network structures; and (2) utilize an LSTM-RNN to represent and classify propagation pathways of a message. Since content information is sparse and noisy on social media, adopting TraceMiner allows to provide a high degree of classification accuracy even in the absence of content information. Experimental results on real-world datasets show the superiority over state-of-the-art approaches on the task of fake news detection and news categorization.