As news reading on social media becomes more and more popular, fake news becomes a major issue concerning the public and government. The fake news can take advantage of multimedia content to mislead readers and get dissemination, which can cause negative effects or even manipulate the public events. One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events. Unfortunately, most of the existing approaches can hardly handle this challenge, since they tend to learn event-specific features that can not be transferred to unseen events. In order to address this issue, we propose an end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events. It consists of three main components: the multi-modal feature extractor, the fake news detector, and the event discriminator. The multi-modal feature extractor is responsible for extracting the textual and visual features from posts. It cooperates with the fake news detector to learn the discriminable representation for the detection of fake news. The role of event discriminator is to remove the event-specific features and keep shared features among events. Extensive experiments are conducted on multimedia datasets collected from Weibo and Twitter. The experimental results show our proposed EANN model can outperform the state-of-the-art methods, and learn transferable feature representations.

https://dl.acm.org/doi/pdf/10.1145/3219819.3219903

EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

/pdf/detection-and-resolution-of-rumours-in-social-media-a-survey-4wwcvcnn3i.pdf

Detection and Resolution of Rumours in Social Media: A Survey

Microblogs have become popular media for news propagation in recent years. Meanwhile, numerous rumors and fake news also bloom and spread wildly on the open social media platforms. Without verification, they could seriously jeopardize the credibility of microblogs. We observe that an increasing number of users are using images and videos to post news in addition to texts. Tweets or microblogs are commonly composed of text, image and social context. In this paper, we propose a novel Recurrent Neural Network with an attention mechanism (att-RNN) to fuse multimodal features for effective rumor detection. In this end-to-end network, image features are incorporated into the joint features of text and social context, which are obtained with an LSTM (Long-Short Term Memory) network, to produce a reliable fused classification. The neural attention from the outputs of the LSTM is utilized when fusing with the visual features. Extensive experiments are conducted on two multimedia rumor datasets collected from Weibo and Twitter. The results demonstrate the effectiveness of the proposed end-to-end att-RNN in detecting rumors with multimodal contents.

Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs

Microblog has been a popular media platform for reporting and propagating news. However, fake news spreading on microblogs would severely jeopardize its public credibility. To identify the truthfulness of news on microblogs, images are very crucial content. In this paper, we explore the key role of image content in the task of automatic news verification on microblogs. Existing approaches to news verification depend on features extracted mainly from the text content of news tweets, while image features for news verification are often ignored. According to our study, however, images are very popular and have a great influence on microblogs news propagation. In addition, fake and real news events have different image distribution patterns. Therefore, we propose several visual and statistical features to characterize these patterns visually and statistically for detecting fake news. Experiments on a real-world multimedia dataset collected from Sina Weibo validate the effectiveness of our proposed image features. The news verification performance of our method outperforms baseline methods. To the best of our knowledge, this is the first attempt that systematically explores image features on news verification task.

Novel Visual and Statistical Image Features for Microblogs News Verification

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e. pieces of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how natural language processing and data mining techniques may be used to find ways of determining their veracity. In this survey we introduce and discuss two types of rumours that circulate on social media; long-standing rumours that circulate for long periods of time, and newly-emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far towards the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for detection and resolution of rumours.

This paper provides an overview of the Verifying Multimedia Use task that takes places as part of the 2015 MediaEval Benchmark. The task deals with the automatic detection of manipulation and misuse of Web multimedia content. Its aim is to lay the basis for a future generation of tools that could assist media professionals in the process of verication. Examples of manipulation include maliciously tampering with images and videos, e.g., splicing, removal/addition of elements, while other kinds of misuse include the reposting of previously captured multimedia content in a dierent context (e.g., a new event) claiming that it was captured there. For the 2015 edition of the task, we have generated and made available a large corpus of real-world cases of images that were distributed through tweets, along with manually assigned labels regarding their use, i.e. misleading (fake) versus appropriate (real).

/pdf/verifying-multimedia-use-at-mediaeval-2015-2jb7abbvqc.pdf

Verifying Multimedia Use at MediaEval 2015

Enabling effective multimedia information processing, analysis, and access applications in online social multimedia settings requires data representation models that capture a broad range of the characteristics of such environments and ensure interoperability. We propose a flexible model for describing Socially Interconnected MultiMedia-enriched Objects (SIMMO) that integrates in a unified manner the representation of multimedia and social features in online environments. Its specification is based on a set of identified requirements and its expressive power is illustrated using several diverse examples. Finally, a comparison of SIMMO with existing approaches demonstrates its unique features.

A Unified Model for Socially Interconnected Multimedia-Enriched Objects

Modern online social networks, such as Twitter and Instagram, are nowadays important sources for publishing information and content around breaking news stories and incidents related to public safety, ranging from natural disasters and aeroplane accidents to terrorist attacks and industrial accidents. A crucial issue regarding such information and content is the extent that they can be relied upon and used for improving the situational awareness and operational capabilities of decision makers. Given the proliferation of noisy, irrelevant and fake content posted to such platforms, two important requirements for systems supporting the information access needs in incidents, such as the ones described above, include the support for understanding the “big picture” around the incident and the verification of particular pieces of posted content. To this end, we propose Media REVEALr, a scalable and efficient content-based media crawling and indexing framework featuring a novel and resilient near-duplicate detection approach and intelligent content- and context-based aggregation capabilities (e.g. clustering, named entity extraction). We evaluate the system using both reference benchmark datasets as well as datasets collected around real-world incidents, and we describe the ways it contributes to the improvement of the situational awareness and journalistic verification in breaking news situations, like natural disasters.

Media REVEALr: A Social Multimedia Monitoring and Intelligence System for Web Multimedia Verification

In the context of using Web image content for analysis and retrieval, it is typically necessary to perform large-scale image crawling. A serious bottleneck in such set-ups pertains to the fetching of image content, since for each web page a large number of HTTP requests need to be issued to download all included image elements. In practice, however, only the relatively big images (e.g., larger than 400 pixels in width and height) are potentially of interest, since most of the smaller ones are irrelevant to the main subject or correspond to decorative elements (e.g., icons, buttons). Given that there is often no dimension information in the HTML img tag of images, to filter out small images, an image crawler would still need to issue a GET request and download the respective files before deciding whether to index them. To address this limitation, in this paper, we explore the challenge of predicting the size of images on the Web based only on their URL and information extracted from the surrounding HTML code. We present two different methodologies: The first one is based on a common text classification approach using the n-grams or tokens of the image URLs and the second one relies on the HTML elements surrounding the image. Eventually, we combine these two techniques, and achieve considerable improvement in terms of accuracy, leading to a highly effective filtering component that can significantly improve the speed and efficiency of the image crawler.

Katerina Andreadou

Papers

Verifying Multimedia Use at MediaEval 2015

A Unified Model for Socially Interconnected Multimedia-Enriched Objects

Media REVEALr: A Social Multimedia Monitoring and Intelligence System for Web Multimedia Verification

Web image size prediction for efficient focused image crawling