scholarly journals Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model

2018 ◽  
pp. 163973
Author(s):  
Akshay Aggarwal ◽  
Aniruddha Chauhan ◽  
Deepika Kumar ◽  
Mamta Mittal ◽  
Sharad Verma
Keyword(s):  
Language Model ◽  
Fine Tuning ◽  
Fake News

scholarly journals Tuned bidirectional encoder representations from transformers for fake news detection

2021 ◽  
Vol 22 (3) ◽  
pp. 1667
Author(s):  
Amsal Pardamean ◽  
Hilman F. Pardede
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Traditional Method ◽  
Language Model ◽  
Learning Technologies ◽  
Fine Tuning ◽  
Fake News ◽  
Source Of Information ◽  
Better Than ◽  
Made In

Online medias are currently the dominant source of Information due to not being limited by time and place, fast and wide distributions. However, inaccurate news, or often referred as fake news is a major problem in news dissemination for online medias. Inaccurate news is information that is not true, that is engineered to cover the real information and has no factual basis. Usually, inaccurate news is made in the form of news that has mass appeal and is presented in the guise of genuine and legitimate news nuances to deceive or change the reader's mind or opinion. Identification of inaccurate news from real news can be done with natural language processing (NLP) technologies. In this paper, we proposed bidirectional encoder representations from transformers (BERT) for inaccurate news identification. BERT is a language model based on deep learning technologies and it has found effective for many NLP tasks. In this study, we use transfer learning and fine-tuning to adapt BERT for inaccurate news identification. The experiments show that our method could achieve accuracy of 99.23%, recall 99.46%, precision 98.86%, and F-Score of 99.15%. It is largely better than traditional method for the same tasks.

scholarly journals Adapting Pre-trained Language Models to Rumor Detection on Twitter

2021 ◽  
Vol 27 (10) ◽  
pp. 1128-1148
Author(s):  
Hamda Slimi ◽  
Ibrahim Bounhas ◽  
Yahya Slimani
Keyword(s):  
State Of The Art ◽  
Language Model ◽  
Fine Tuning ◽  
Language Models ◽  
Fake News ◽  
Timely Manner ◽  
Fast Pace ◽  
Social Media Platforms ◽  
Rumor Detection ◽  
Malicious Intent

Fake news has invaded social media platforms where false information is being propagated with malicious intent at a fast pace. These circumstances required the development of solutions to monitor and detect rumor in a timely manner. In this paper, we propose an approach that seeks to detect emerging and unseen rumors on Twitter by adapting a pre-trained language model to the task of rumor detection, namely RoBERTa. A comparison against content-based characteristics has shown the capability of the model to surpass handcrafted features. Experimental results show that our approach outperforms state of the art ones in all metrics and that the fine tuning of RoBERTa led to richer word embeddings that consistently and significantly enhance the precision of rumor recognition.

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

scholarly journals Smart Design Nano-Hybrid Formulations by Machine Learning

Proceedings ◽  
2020 ◽  
Vol 78 (1) ◽  
pp. 5
Author(s):  
Raquel de Melo Barbosa ◽  
Fabio Fonseca de Oliveira ◽  
Gabriel Bezerra Motta Câmara ◽  
Tulio Flavio Accioly de Lima e Moura ◽  
Fernanda Nervo Raffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Water Solubility ◽  
Inorganic Materials ◽  
Fine Tuning ◽  
Support Vector ◽  
Drug Solubility ◽  
Physical Behavior ◽  
Best Fit

Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms.

scholarly journals Automated Classification of Fake News Spreaders to Break the Misinformation Chain

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 248
Author(s):  
Simone Leonardi ◽  
Giuseppe Rizzo ◽  
Maurizio Morisio
Keyword(s):  
Social Media ◽  
Network Architecture ◽  
Diffusion Mechanism ◽  
Automated Classification ◽  
Fake News ◽  
Neural Network Architecture ◽  
User Classification ◽  
The Social ◽  
Network Topologies

In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.

scholarly journals Enhanced Language Model with Hybrid Knowledge Graph for Mathematical Topic Prediction

2021 ◽  
Author(s):  
Minghui Wu ◽  
Canghong Jin ◽  
Wenkang Hu ◽  
Yabo Chen
Keyword(s):  
Language Model ◽  
Real Data ◽  
Mathematical Concept ◽  
Fine Tuning ◽  
Knowledge Graph ◽  
Mathematics Knowledge ◽  
Hybrid Knowledge ◽  
Model Set ◽  
Set Up ◽  
Mathematical Topic

Understanding mathematical topics is important for both educators and students to capture latent concepts of questions, evaluate study performance, and recommend content in online learning systems. Compared to traditional text classification, mathematical topic classification has several main challenges: (1) the length of mathematical questions is relatively short; (2) there are various representations of the same mathematical concept(i.e., calculations and application); (3) the content of question is complex including algebra, geometry, and calculus. In order to overcome these problems, we propose a framework that combines content tokens and mathematical knowledge concepts in whole procedures. We embed entities from mathematics knowledge graphs, integrate entities into tokens in a masked language model, set up semantic similarity-based tasks for next-sentence prediction, and fuse knowledge vectors and token vectors during the fine-tuning procedure. We also build a Chinese mathematical topic prediction dataset consisting of more than 70,000 mathematical questions with topics. Our experiments using real data demonstrate that our knowledge graph-based mathematical topic prediction model outperforms other state-of-the-art methods.

scholarly journals Typology of Business-Related Fake News Online: A Literature Review

2020 ◽  
Vol 5 (4) ◽  
pp. 234-243
Author(s):  
Marko Selakovic ◽  
Anna Tarabasz ◽  
Monica Gallant
Keyword(s):  
Content Analysis ◽  
Literature Review ◽  
Academic Research ◽  
Marketing Research ◽  
Jel Classification ◽  
Future Research ◽  
Fake News ◽  
Recent Emergence ◽  
News Online

Objective – This review paper discusses the emergence of scholarly articles related to the typology and classification of fake news and offers solutions for identified gaps, such as unstandardized terminology and unstandardized typology in the field of fake news-related research. Typology of fake news is a critical topic nowadays: recently emerged fake news needs to be categorized and analyzed in a structured manner in order to respond appropriately. Methodology/Technique – Based on the systematic review of literature identified in scientific databases, different typologies of fake news have been identified and a new typology of business-related fake news online has been proposed. New typology of business-related fake news online is based on factors such as level of facticity, intention to deceive and financial motivation. Findings and novelty – Content analysis of 326 articles containing terms related to the typology of fake news and classification of fake news indicates that the term “typology of fake news” is predominantly used in management, marketing and communications research, while the term “classification of fake news” is predominantly used in the information technology research. The content analysis also indicates the recent emergence of the topic of typology and classification of fake news in academic research, revealing that all articles related to these topics have been published on or after 2016. In addition to the contribution by presenting comprehensive typology of business-related fake news online, this paper also provides recommendations for future research and improvements related to the typology of fake news, emphasizing business-related fake news and fake news spread in the digital space. Type of Paper: Review JEL Classification: M31, M39. Keywords: Fake News; Crisis Communications; Online Communications; Digital Marketing; Management Research; Marketing Research Reference to this paper should be made as follows: Selakovic, M; Tarabasz, A; Gallant, M. (2020). Typology of Business-Related Fake News Online: A Literature Review, J. Mgt. Mkt. Review 5(4) 234 – 243. https://doi.org/10.35609/jmmr.2020.5.4(5)

scholarly journals Research note: The scale of Facebook’s problem depends upon how ‘fake news’ is classified

2020 ◽  
Author(s):  
Richard Rogers
Keyword(s):  
Presidential Elections ◽  
Mass Scale ◽  
Research Note ◽  
User Engagement ◽  
Fake News ◽  
Scale Problem ◽  
Media Organizations ◽  
False News

Ushering in the contemporary ‘fake news’ crisis, Craig Silverman of Buzzfeed News reported that it outperformed mainstream news on Facebook in the three months prior to the 2016 US presidential elections. Here the report’s methods and findings are revisited for 2020. Examining Facebook user engagement of election-related stories, and applying Silverman’s classification of fake news, it was found that the problem has worsened, implying that the measures undertaken to date have not remedied the issue. If, however, one were to classify ‘fake news’ in a stricter fashion, as Facebook as well as certain media organizations do with the notion of ‘false news’, the scale of the problem shrinks. A smaller scale problem could imply a greater role for fact-checkers (rather than deferring to mass-scale content moderation), while a larger one could lead to the further politicisation of source adjudication, where labelling particular sources broadly as ‘fake’, ‘problematic’ and/or ‘junk’ results in backlash.

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