ДЕЗІНФОРМАЦІЯ І ФЕЙКОВІ НОВИНИ: ОЗНАКИ ТА МЕТОДИ ВИЯВЛЕННЯ В МЕРЕЖІ ІНТЕРНЕТ

The development of the global Internet, the large-scale introduction of fast and free online services not only expanded the possibilities of access to information, but also changed the principles of communication of society. Due to the simplification of the mechanisms for creating and disseminating news via the Internet, as well as the physical impossibility to verify huge amounts of information circulating in the network, the spread of disinformation and fake news has increased dramatically. In view of this, detecting false news is an important task that not only ensures that users are provided with verified information and prevent manipulation of public consciousness, but also helps to maintain a reliable news ecosystem. According to the analysis of international organizations and scientific publications, disinformation is false, misleading, manipulative information created deliberately for the sake of economic, political or other benefits, and fake news is one of the methods of its dissemination. Fake news is characterized by the following features: false manipulative content; aiming to deliberately mislead, disorient the consumer; presenting information on behalf of false or anonymous sources; inconsistency with the content of the headline; use of rumors and satire; aiming to criticize social or political issues; imitation of legitimate news; dissemination on the Internet; economic or political motives of creation. As a result of the study, it was found that Internet users, through conscious perception of information and a responsible approach to its dissemination, can reduce the effectiveness of disinformation and fake news tools. It is noted that a proven method to avoid false information is to receive news from reliable sources. However, in order to identify fake news, it is advisable to use such methods as: analysis of the source, content and headline of the news; checking information about the author and sources referred to in the message; checking the "freshness" of the news; using fact-checking tools; consulting with an expert; analyzing own emotional reaction to the news, etc.Розвиток глобальної мережі Інтернет, масштабне впровадження швидких і безкоштовних онлайн-сервісів не тільки розширили можливості доступу до інформації, але й змінили засади комунікації суспільства. Внаслідок спрощення механізмів створення та розповсюдження новин через Інтернет, а також фізичної неможливості перевірити величезні обсяги інформації, що циркулює в мережі, різко збільшилися обсяги поширення дезінформації та фейкових новин. З огляду на це виявлення неправдивих новин є важливим завданням, яке не тільки гарантує надання користувачам перевіреної інформації та запобігання маніпуляціям суспільною свідомістю, але й допоможе підтримувати надійну екосистему новин. На основі аналізу напрацювань міжнародних організацій і наукових публікацій встановлено, що дезінформація — це недостовірна, оманлива, маніпулятивна інформація, створена навмисно заради отримання економічних, політичних або інших вигод, а фейкові новини є одним із методів її поширення. Фейковим новинам притаманні такі риси як неправдивий маніпулятивний зміст; спрямованість на навмисне введення в оману, дезорієнтацію споживача; подання інформації від імені хибних або анонімних джерел; невідповідність заголовка змісту повідомлення; використання чуток і сатири; спрямування на критику соціальних або політичних питань; імітація легітимних новин; поширення в мережі Інтернет; економічні або політичні мотиви створення. У результаті дослідження встановлено, що саме користувачі Інтернету завдяки свідомому сприйняттю інформації та відповідальному підходу до її розповсюдження можуть знизити ефективність інструментів дезінформації та фейкових новин. Відзначено, що перевіреним методом уникнути неправдивих відомостей є отримувати перевірені новини з надійних джерел. Натомість для виявлення фейкових новин доцільно використовувати такі методи як: аналіз джерела, змісту й заголовку новин; перевірка інформації про автора та джерел, на які посилаються в повідомленні; перевірка «свіжості» новини; використання фактчекінгових інструментів; консультування з експертом; аналіз власної емоційної реакції на новину тощо

Research Methods in Machine Learning: A Content Analysis

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research.  To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.  

Research Methods in Machine Learning: A Content Analysis

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research.  To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.  

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Peer reviewedPostprin

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions

A Systematic Review on the Detection of Fake News Articles

Currently submitted to ACM Transactions on Intelligent Systems and Technology. Awaiting peer-review.It has been argued that fake news and the spread of false information pose a threat to societies throughout the world, from influencing the results of elections to hindering the efforts to manage the COVID-19 pandemic. To combat this threat, a number of Natural Language Processing (NLP) approaches have been developed. These leverage a number of datasets, feature extraction/selection techniques and machine learning (ML) algorithms to detect fake news before it spreads. While these methods are well-documented, there is less evidence regarding their efficacy in this domain. By systematically reviewing the literature, this paper aims to delineate the approaches for fake news detection that are most performant, identify limitations with existing approaches, and suggest ways these can be mitigated. The analysis of the results indicates that Ensemble Methods using a combination of news content and socially-based features are currently the most effective. Finally, it is proposed that future research should focus on developing approaches that address generalisability issues (which, in part, arise from limitations with current datasets), explainability and bias

Which machine learning paradigm for fake news detection?

Fake news detection/classification is gradually becoming of paramount importance to out society in order to avoid the so-called reality vertigo, and protect in particular the less educated persons. Various machine learning techniques have been proposed to address this issue. This article presents a comprehensive performance evaluation of eight machine learning algorithms for fake news detection/classification. © 2019 Association for Computing Machinery