scholarly journals Exploring Thematic Coherence in Fake News

2020 ◽  
pp. 571-580
Author(s):  
Martins Samuel Dogo ◽  
Deepak P. ◽  
Anna Jurek-Loughrey
Keyword(s):  
Topic Models ◽  
Experimental Results ◽  
Fake News ◽  
Cross Domain ◽  
Global Issue ◽  
Thematic Coherence

AbstractThe spread of fake news remains a serious global issue; understanding and curtailing it is paramount. One way of differentiating between deceptive and truthful stories is by analyzing their coherence. This study explores the use of topic models to analyze the coherence of cross-domain news shared online. Experimental results on seven cross-domain datasets demonstrate that fake news shows a greater thematic deviation between its opening sentences and its remainder.

A Transformer-Based Approach to Multilingual Fake News Detection in Low-Resource Languages

2022 ◽  
Vol 21 (1) ◽  
pp. 1-20
Author(s):  
Arkadipta De ◽  
Dibyanayan Bandyopadhyay ◽  
Baban Gain ◽  
Asif Ekbal
Keyword(s):  
Language Processing ◽  
English Language ◽  
Neural Model ◽  
Fake News ◽  
Web Content ◽  
Low Resource ◽  
Domain Specific ◽  
Cross Domain ◽  
Current State ◽  
Domain Transfer

Fake news classification is one of the most interesting problems that has attracted huge attention to the researchers of artificial intelligence, natural language processing, and machine learning (ML). Most of the current works on fake news detection are in the English language, and hence this has limited its widespread usability, especially outside the English literate population. Although there has been a growth in multilingual web content, fake news classification in low-resource languages is still a challenge due to the non-availability of an annotated corpus and tools. This article proposes an effective neural model based on the multilingual Bidirectional Encoder Representations from Transformer (BERT) for domain-agnostic multilingual fake news classification. Large varieties of experiments, including language-specific and domain-specific settings, are conducted. The proposed model achieves high accuracy in domain-specific and domain-agnostic experiments, and it also outperforms the current state-of-the-art models. We perform experiments on zero-shot settings to assess the effectiveness of language-agnostic feature transfer across different languages, showing encouraging results. Cross-domain transfer experiments are also performed to assess language-independent feature transfer of the model. We also offer a multilingual multidomain fake news detection dataset of five languages and seven different domains that could be useful for the research and development in resource-scarce scenarios.

scholarly journals A Method for Constructing Supervised Topic Model Based on Term Frequency-Inverse Topic Frequency

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1486
Author(s):  
Zhinan Gou ◽  
Zheng Huo ◽  
Yuanzhen Liu ◽  
Yi Yang
Keyword(s):  
Topic Modeling ◽  
Topic Model ◽  
State Of The Art ◽  
Topic Models ◽  
Document Classification ◽  
Experimental Results ◽  
Tag Recommendation ◽  
Term Frequency ◽  
Series Of Experiments ◽  
Dirichlet Prior

Supervised topic modeling has been successfully applied in the fields of document classification and tag recommendation in recent years. However, most existing models neglect the fact that topic terms have the ability to distinguish topics. In this paper, we propose a term frequency-inverse topic frequency (TF-ITF) method for constructing a supervised topic model, in which the weight of each topic term indicates the ability to distinguish topics. We conduct a series of experiments with not only the symmetric Dirichlet prior parameters but also the asymmetric Dirichlet prior parameters. Experimental results demonstrate that the result of introducing TF-ITF into a supervised topic model outperforms several state-of-the-art supervised topic models.

scholarly journals Neural User Response Generator: Fake News Detection with Collective User Intelligence

2018 ◽  
Author(s):  
Feng Qian ◽  
Chengyue Gong ◽  
Karishma Sharma ◽  
Yan Liu
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Semantic Information ◽  
Experimental Results ◽  
News Article ◽  
Fake News ◽  
Additional Information ◽  
Word Level

Fake news on social media is a major challenge and studies have shown that fake news can propagate exponentially quickly in early stages. Therefore, we focus on early detection of fake news, and consider that only news article text is available at the time of detection, since additional information such as user responses and propagation patterns can be obtained only after the news spreads. However, we find historical user responses to previous articles are available and can be treated as soft semantic labels, that enrich the binary label of an article, by providing insights into why the article must be labeled as fake. We propose a novel Two-Level Convolutional Neural Network with User Response Generator (TCNN-URG) where TCNN captures semantic information from article text by representing it at the sentence and word level, and URG learns a generative model of user response to article text from historical user responses which it can use to generate responses to new articles in order to assist fake news detection. We conduct experiments on one available dataset and a larger dataset collected by ourselves. Experimental results show that TCNN-URG outperforms the baselines based on prior approaches that detect fake news from article text alone.

Metadata-Based Information Management Framework for Grids

2011 ◽  
pp. 147-166
Author(s):  
Wie Jie ◽  
Tianyi Zang ◽  
Terence Hung ◽  
Stephen Turner ◽  
Wentong Cai
Keyword(s):  
Performance Evaluation ◽  
Information Management ◽  
Hierarchical Structure ◽  
Virtual Organization ◽  
Information Service ◽  
Experimental Results ◽  
Data Organization ◽  
Organization Model ◽  
Management Framework ◽  
Cross Domain

Information service is a key component of a Grid environment and crucial to the operation of Grids. This chapter presents an information management framework for a Grid virtual organization (VO). This information management framework is a hierarchical structure which consists of VO layer, site layer and resource layer. We propose different models of information data organization for information management in Grids and simulation experiments were conducted to evaluate the performance of these models. Based on the experimental results, we further introduce the data organization model for our information management framework. A performance evaluation conducted on a cross-domain Grid testbed indicates that our information management framework presents good scalability with large number of concurrent users and large amount of information aggregated. In this chapter some application experiences of using the information management framework are also presented.

Understanding the Landscape of Online Deception

2021 ◽  
pp. 39-66
Author(s):  
Hicham Hage ◽  
Esma Aïmeur ◽  
Amel Guedidi
Keyword(s):  
Social Networking ◽  
Information And Communication Technologies ◽  
Social Networking Sites ◽  
Communication Technologies ◽  
Fake News ◽  
New Information ◽  
Global Issue ◽  
Information And Communication ◽  
Online Deception ◽  
Major Factors

While fake and distorted information has been part of our history, new information and communication technologies tremendously increased its reach and proliferation speed. Indeed, in current days, fake news has become a global issue, prompting reactions from both researchers and legislators in an attempt to solve this problem. However, fake news and misinformation are part of the larger landscape of online deception. Specifically, the purpose of this chapter is to present an overview of online deception to better frame and understand the problem of fake news. In detail, this chapter offers a brief introduction to social networking sites, highlights the major factors that render individuals more susceptible to manipulation and deception, detail common manipulation and deception techniques and how they are actively used in online attacks as well as their common countermeasures. The chapter concludes with a discussion on the double role or artificial intelligence in countering as well as creating fake news.

scholarly journals Authorship Attribution with Topic Models

2014 ◽  
Vol 40 (2) ◽  
pp. 269-310 ◽  
Author(s):  
Yanir Seroussi ◽  
Ingrid Zukerman ◽  
Fabian Bohnert
Keyword(s):  
State Of The Art ◽  
Topic Models ◽  
Experimental Results ◽  
Authorship Attribution ◽  
Data Sets ◽  
New Model ◽  
On Line ◽  
Art Performance

Authorship attribution deals with identifying the authors of anonymous texts. Traditionally, research in this field has focused on formal texts, such as essays and novels, but recently more attention has been given to texts generated by on-line users, such as e-mails and blogs. Authorship attribution of such on-line texts is a more challenging task than traditional authorship attribution, because such texts tend to be short, and the number of candidate authors is often larger than in traditional settings. We address this challenge by using topic models to obtain author representations. In addition to exploring novel ways of applying two popular topic models to this task, we test our new model that projects authors and documents to two disjoint topic spaces. Utilizing our model in authorship attribution yields state-of-the-art performance on several data sets, containing either formal texts written by a few authors or informal texts generated by tens to thousands of on-line users. We also present experimental results that demonstrate the applicability of topical author representations to two other problems: inferring the sentiment polarity of texts, and predicting the ratings that users would give to items such as movies.

A multi-grained aspect vector learning model for unsupervised aspect identification

2021 ◽  
pp. 1-11
Author(s):  
Jinglei Shi ◽  
Junjun Guo ◽  
Zhengtao Yu ◽  
Yan Xiang
Keyword(s):  
Vector Space ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Topic Models ◽  
Experimental Results ◽  
Coarse Grained ◽  
Product Reviews ◽  
Fine Grained ◽  
Proposed Model ◽  
Identification Model

Unsupervised aspect identification is a challenging task in aspect-based sentiment analysis. Traditional topic models are usually used for this task, but they are not appropriate for short texts such as product reviews. In this work, we propose an aspect identification model based on aspect vector reconstruction. A key of our model is that we make connections between sentence vectors and multi-grained aspect vectors using fuzzy k-means membership function. Furthermore, to make full use of different aspect representations in vector space, we reconstruct sentence vectors based on coarse-grained aspect vectors and fine-grained aspect vectors simultaneously. The resulting model can therefore learn better aspect representations. Experimental results on two datasets from different domains show that our proposed model can outperform a few baselines in terms of aspect identification and topic coherence of the extracted aspect terms.

scholarly journals Deepfake Detection Method Based on Cross-Domain Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fang Sun ◽  
Niuniu Zhang ◽  
Pan Xu ◽  
Zengren Song
Keyword(s):  
Classification Accuracy ◽  
Detection Method ◽  
Image Features ◽  
Security Threats ◽  
Geometric Features ◽  
Fake News ◽  
Domain Fusion ◽  
Cross Domain ◽  
Facial Information ◽  
Active Detection

In recent years, despite its wide use in various fields, deepfake has been abused to generate hazardous contents such as fake movies, rumors, and fake news by manipulating or replacing facial information of the original sources and, thus, exerts huge security threats to the society. Facing the continuous evolution of deepfake, research on active detection and prevention technology becomes particularly important. In this paper, we propose a new deepfake detection method based on cross-domain fusion, which, on the basis of traditional spatial domain features, realizes the fusion of cross-domain image features by introducing edge geometric features of the frequency domain and, therefore, achieves considerable improvements on classification accuracy. Further evaluations of this method have been performed on publicly deepfake datasets, and the results show that our method is effective particularly on the Meso-4 DeepFake Database.

Understanding the Landscape of Online Deception

2020 ◽  
pp. 290-317
Author(s):  
Hicham Hage ◽  
Esma Aïmeur ◽  
Amel Guedidi
Keyword(s):  
Social Networking ◽  
Information And Communication Technologies ◽  
Social Networking Sites ◽  
Communication Technologies ◽  
Fake News ◽  
New Information ◽  
Global Issue ◽  
Information And Communication ◽  
Online Deception ◽  
Major Factors

While fake and distorted information has been part of our history, new information and communication technologies tremendously increased its reach and proliferation speed. Indeed, in current days, fake news has become a global issue, prompting reactions from both researchers and legislators in an attempt to solve this problem. However, fake news and misinformation are part of the larger landscape of online deception. Specifically, the purpose of this chapter is to present an overview of online deception to better frame and understand the problem of fake news. In detail, this chapter offers a brief introduction to social networking sites, highlights the major factors that render individuals more susceptible to manipulation and deception, detail common manipulation and deception techniques and how they are actively used in online attacks as well as their common countermeasures. The chapter concludes with a discussion on the double role or artificial intelligence in countering as well as creating fake news.

scholarly journals Learning priors for adversarial autoencoders

2020 ◽  
Vol 9 ◽  
Author(s):  
Hui-Po Wang ◽  
Wen-Hsiao Peng ◽  
Wei-Jan Ko
Keyword(s):  
Image Quality ◽  
Data Distribution ◽  
Image Synthesis ◽  
Factor Models ◽  
Experimental Results ◽  
Limited Capacity ◽  
Cross Domain ◽  
Proposed Model ◽  
Code Generators ◽  
Latent Factor Models

Abstract Most deep latent factor models choose simple priors for simplicity, tractability, or not knowing what prior to use. Recent studies show that the choice of the prior may have a profound effect on the expressiveness of the model, especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders (AAEs). We introduce the notion of code generators to transform manually selected simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than AAEs in both supervised and unsupervised settings. Lastly, we present its ability to do cross-domain translation in a text-to-image synthesis task.

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