Introduction to Special Issue on Misinformation, Fake News and Rumor Detection in Low-Resource Languages

2022 ◽  
Vol 21 (1) ◽  
pp. 1-3
Author(s):  
Akshi Kumar ◽  
Christian Esposito ◽  
Dimitrios A. Karras
Keyword(s):  
Fake News ◽  
Special Issue ◽  
Low Resource ◽  
Rumor Detection

scholarly journals Introduction to the Special Issue on Machine Translation for Low-Resource Languages

2021 ◽  
Author(s):  
Chao-Hong Liu ◽  
Alina Karakanta ◽  
Audrey N. Tong ◽  
Oleg Aulov ◽  
Ian M. Soboroff ◽  
...  
Keyword(s):  
Machine Translation ◽  
Special Issue ◽  
Low Resource

scholarly journals A Study of Rumor Detection based on Social Network Topic Models Relationship

2020 ◽  
Author(s):  
Diogo Nolasco ◽  
Jonice Oliveira
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Local Community ◽  
Topic Model ◽  
Fake News ◽  
Detection Problem ◽  
Model Method ◽  
Show Evidence ◽  
Rumor Detection ◽  
Scientific Topic

The rumor detection problem on social networks has attracted considerable attention in recent years with the rise of concerns about fake news and disinformation. Most previous works focused on detecting rumors by individual messages, classifying whether a post or blog entry is considered a rumor or not. This paper proposes a method for rumor detection on topic-level that identifies whether a social topic related to a scientific topic is a rumor. We propose the use of a topic model method on social and scientific domains and correlate the topics found to detect the most prone to be rumors. Results applied in the Zika epidemic scenario show evidence that the least correlated topics contain a mix of rumors and local community discussions.

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 Leadership in a post-truth era: A new narrative disorder?

Leadership ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 135-151 ◽  
Author(s):  
Hamid Foroughi ◽  
Yiannis Gabriel ◽  
Marianna Fotaki
Keyword(s):  
Social Media ◽  
Economic Crisis ◽  
Fake News ◽  
Special Issue ◽  
Conspiracy Theories ◽  
Digital Revolution ◽  
Counter Narrative ◽  
Counter Narratives ◽  
Different Types ◽  
Historical Periods

This essay, and the special issue it introduces, seeks to explore leadership in a post-truth age, focusing in particular on the types of narratives and counter-narratives that characterize it and at times dominate it. We first examine the factors that are often held responsible for the rise of post-truth in politics, including the rise of relativist and postmodernist ideas, dishonest leaders and bullshit artists, the digital revolution and social media, the 2008 economic crisis and collapse of public trust. We develop the idea that different historical periods are characterized by specific narrative ecologies, which, by analogy to natural ecologies, can be viewed as spaces where different types of narrative and counter-narrative emerge, interact, compete, adapt, develop and die. We single out some of the dominant narrative types that characterize post-truth narrative ecologies and highlight the ability of language to ‘do things with words’ that support both the production of ‘fake news’ and a type of narcissistic leadership that thrive in these narrative ecologies. We then examine more widely leadership in post-truth politics focusing on the resurgence of populist and demagogical types along with the narratives that have made these types highly effective in our times. These include nostalgic narratives idealizing a fictional past and conspiracy theories aimed at arousing fears about a dangerous future.

scholarly journals Monday Mo(u)rning

2017 ◽  
Vol 9 (2) ◽  
pp. 72-78
Author(s):  
Gary Levy
Keyword(s):  
Primary School ◽  
Fake News ◽  
Special Issue ◽  
Everyday Practices ◽  
Alternative Facts ◽  
Civil Societies

This piece presents an imaginary scenario taking place in any typical primary school around Australia. It was developed for the special issue of Cosmopolitan Civil Societies Journal, on fake news and alternative facts, to show how these may arise in everyday practices.

scholarly journals Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

2020 ◽  
Author(s):  
Liang Zhang ◽  
Jingqun Li ◽  
Bin Zhou ◽  
Yan Jia
Keyword(s):  
Neural Networks ◽  
Numerical Experiments ◽  
State Of The Art ◽  
Fake News ◽  
Wide Spread ◽  
Convolutional Networks ◽  
The Media ◽  
Graph Neural Networks ◽  
Rumor Detection ◽  
Simple Network

Identifying fake news on the media has been an important issue. This is especially true considering the wide spread of rumors on the popular social networks such as Twitter. Various kinds of techniques have been proposed to detect rumors. In this work, we study the application of graph neural networks for the task of rumor detection, and present a simplified new architecture to classify rumors. Numerical experiments show that the proposed simple network has comparable to or even better performance than state-of-the art graph convolutional networks, while having significantly reduced the computational complexity.

Introduction to Special Issue on Literacy, Democracy, and Fake News: Making it Right in the Era of Fast and Slow Literacies

2017 ◽  
Vol 5 (2) ◽  
pp. 10-23 ◽  
Author(s):  
Thomas P. Miller ◽  
◽  
Adele Leon ◽  
Keyword(s):  
Fake News ◽  
Special Issue

A Literature Review of NLP Approaches to Fake News Detection and Their Applicability to Romanian-Language News Analysis

Transilvania ◽  
2020 ◽  
pp. 65-71
Author(s):  
Costin Busioc ◽  
Stefan Ruseti ◽  
Mihai Dascalu
Keyword(s):  
Literature Review ◽  
Research Interest ◽  
Future Research ◽  
Political Environment ◽  
Fake News ◽  
Research Groups ◽  
Low Resource ◽  
News Analysis ◽  
The Social

Fighting fake news is a difficult and challenging task. With an increasing impact on the social and political environment, fake news exert an unprecedently dramatic influence on people’s lives. In response to this phenomenon, initiatives addressing automated fake news detection have gained popularity, generating widespread research interest. However, most approaches targeting English and low-resource languages experience problems when devising such solutions. This study focuses on the progress of such investigations, while highlighting existing solutions, challenges, and observations shared by various research groups. In addition, given the limited amount of automated analyses performed on Romanian fake news, we inspect the applicability of the available approaches in the Romanian context, while identifying future research paths.

scholarly journals The Alert Collector: Collection Development in an Era of "Fake News:

2018 ◽  
Vol 57 (3) ◽  
pp. 176
Author(s):  
Mark Shores
Keyword(s):  
Collection Development ◽  
Fake News ◽  
Special Issue ◽  
Innovative Approaches

This Alert Collector column for RUSQ’s special issue “Trusted Information in an Age of Uncertainty” is not going to be the usual list of great resources to add to your collection. In fact, despite a broadly distributed call for Alert Collector columns for this special issue, no one took me up. I do not blame them! At the suggestion of the editor of RUSQ, I decided to put together a “think” piece on fake news as it relates to collection development. I am not going to propose any radical or innovative approaches to how librarians develop collections for the purpose of battling fake news. I do not feel such an approach is possible. What I do want to do in this column is reaffirm and highlight things that I know many of my colleagues are already doing and have been trying to do since the dawn of collection building in libraries.—Editor

Enriching Conventional Ensemble Learner with Deep Contextual Semantics to Detect Fake News in Urdu

2022 ◽  
Vol 21 (1) ◽  
pp. 1-19
Author(s):  
Ramsha Saeed ◽  
Hammad Afzal ◽  
Haider Abbas ◽  
Maheen Fatima
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
English Language ◽  
Information Dissemination ◽  
Majority Voting ◽  
Public Image ◽  
Gradient Boosting ◽  
Fake News ◽  
Adaptive Boosting ◽  
Low Resource

Increased connectivity has contributed greatly in facilitating rapid access to information and reliable communication. However, the uncontrolled information dissemination has also resulted in the spread of fake news. Fake news might be spread by a group of people or organizations to serve ulterior motives such as political or financial gains or to damage a country’s public image. Given the importance of timely detection of fake news, the research area has intrigued researchers from all over the world. Most of the work for detecting fake news focuses on the English language. However, automated detection of fake news is important irrespective of the language used for spreading false information. Recognizing the importance of boosting research on fake news detection for low resource languages, this work proposes a novel semantically enriched technique to effectively detect fake news in Urdu—a low resource language. A model based on deep contextual semantics learned from the convolutional neural network is proposed. The features learned from the convolutional neural network are combined with other n-gram-based features and are fed to a conventional majority voting ensemble classifier fitted with three base learners: Adaptive Boosting, Gradient Boosting, and Multi-Layer Perceptron. Experiments are performed with different models, and results show that enriching the traditional ensemble learner with deep contextual semantics along with other standard features shows the best results and outperforms the state-of-the-art Urdu fake news detection model.

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