Eann

Wang, Yaqing; Ma, Fenglong; Jin, Zhiwei; Yuan, Ye; Xun, Guangxu; Jha, Kishlay; Lu, Shan; Gao, Jing

doi:10.1145/3219819.3219903

Cited by 630 publications

(51 citation statements)

References 16 publications

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“…Towards this direction, experiments were conducted by extracting features from two different modalities such as text and image. Works on that direction include EANN [37], MVAE [17], SpotFake [36] and SpotFake+ [35]. The EANN model, short for event adversarial neural networks, for multimodal fake news detection proposed by [37] consists of three sub-modules namely, textual feature extractor, visual feature extractor and an event discriminator module that when combined together is successful in detecting fake news.…”

Section: Related Workmentioning

confidence: 99%

“…Works on that direction include EANN [37], MVAE [17], SpotFake [36] and SpotFake+ [35]. The EANN model, short for event adversarial neural networks, for multimodal fake news detection proposed by [37] consists of three sub-modules namely, textual feature extractor, visual feature extractor and an event discriminator module that when combined together is successful in detecting fake news. Inspired by [37], [17] built a similar architecture, titled as Multimodal Variational Autoencoder for Fake News Detection (MVAE).…”

Section: Related Workmentioning

confidence: 99%

“…SpotFake extracted features from both the text and image modality. It differs from [17,37] in not using a secondary task within the formulation. Other multimodal features that recently gained a lot of attention to solve the detection problem are those from social media.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Fake News Detection

Gangireddy

Deepak

Long

et al. 2020

Proceedings of the 31st ACM Conference on Hypertext and Social Media

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Fake news has become more prevalent than ever, correlating with the rise of social media that allows every user to rapidly publish their views or hearsay. Today, fake news spans almost every realm of human activity, across diverse fields such as politics and healthcare. Most existing methods for fake news detection leverage supervised learning methods and expect a large labelled corpus of articles and social media user engagement information, which are often hard, time-consuming and costly to procure. In this paper, we consider the task of unsupervised fake news detection, which considers fake news detection in the absence of labelled historical data. We develop GTUT, a graph-based approach for the task which operates in three phases. Starting off with identifying a seed set of fake and legitimate articles exploiting high-level observations on inter-user behavior in fake news propagation, it progressively expands the labelling to all articles in the dataset. Our technique draws upon graph-based methods such as biclique identification, graph-based feature vector learning and label spreading. Through an extensive empirical evaluation over multiple real-world datasets, we establish the improved effectiveness of our method over state-of-the-art techniques for the task.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Fake News Detection

Gangireddy

Deepak

Long

et al. 2020

Proceedings of the 31st ACM Conference on Hypertext and Social Media

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show abstract

“…These features are used as input to a Deep Neural Network (DNN), which has been trained to predict the article's veracity. Rather than traditional machine learning, the deep learning approach was used due to the performance amplification it can achieve in the detection of fake news [19,26] as well as in other problems addressed with artificial intelligence techniques. Next, we present an overview of the article dataset, the different linguistic features, and the DNN model.…”

Section: Linguistic Modelmentioning

confidence: 99%

Check-It: A plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web

Paschalides

Kornilakis

Christodoulou

et al. 2019

IEEE/WIC/ACM International Conference on Web Intelligence

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Over the past few years, we have been witnessing the rise of misinformation on the Web. People fall victims of fake news during their daily lives and assist their further propagation knowingly and inadvertently. There have been many initiatives that are trying to mitigate the damage caused by fake news, focusing on signals from either domain flag-lists, online social networks or artificial intelligence. In this work, we present Check-It, a system that combines, in an intelligent way, a variety of signals into a pipeline for fake news identification. Check-It is developed as a web browser plugin with the objective of efficient and timely fake news detection, respecting the user's privacy. Experimental results show that Check-It is able to outperform the state-of-the-art methods. On a dataset, consisting of 9 millions of articles labeled as fake and real, Check-It obtains classification accuracies that exceed 99%.

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“…Machine learning algorithms are used to identify fake news posted on Facebook automatically [10]. End-to-end framework entitled Event Adversarial Neural Network is proposed to detect fake news in [11]. The model is evaluated A Novel Approach for Detection of Fake News on Social Media Using Metaheuristic Optimization Algorithms on two custom datasets.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Detection of Fake News on Social Media Using Metaheuristic Optimization Algorithms

Özbay¹,

Alataş²

2019

ELEKTRON ELEKTROTECH

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Deceptive content is becoming increasingly dangerous, such as fake news created by social media users. Individuals and society have been affected negatively by the spread of low-quality news on social media. The fake and real news needs to be detected to eliminate the disadvantages of social media. This paper proposes a novel approach for fake news detection (FND) problem on social media. Applying this approach, FND problem has been considered as an optimization problem for the first time and two metaheuristic algorithms, the Grey Wolf Optimization (GWO) and Salp Swarm Optimization (SSO) have been adapted to the FND problem for the first time as well. The proposed FND approach consists of three stages. The first stage is data preprocessing. The second stage is adapting GWO and SSO for construction of a novel FND model. The last stage consists of using proposed FND model for testing. The proposed approach has been evaluated using three different real-world datasets. The results have been compared with seven supervised artificial intelligence algorithms. The results show GWO algorithm has the best performance in comparison with SSO algorithm and the other artificial intelligence algorithms. GWO seems to be efficiently used for solving different types of social media problems.

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Eann

Cited by 630 publications

References 16 publications

Unsupervised Fake News Detection

Unsupervised Fake News Detection

Check-It: A plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web

A Novel Approach for Detection of Fake News on Social Media Using Metaheuristic Optimization Algorithms

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