Can Machines Learn to Detect Fake News? A Survey Focused on Social Media

Through a systematic literature review method, in this work we searched classical electronic libraries in order to find the most recent papers related to fake news detection on social medias. Our target is mapping the state of art of fake news detection, defining fake news and finding the most useful machine learning technique for doing so. We concluded that the most used method for automatic fake news detection is not just one classical machine learning technique, but instead a amalgamation of classic techniques coordinated by a neural network. We also identified a need for a domain ontology that would unify the different terminology and definitions of the fake news domain. This lack of consensual information may mislead opinions and conclusions

Applying Ensemble Machine Learning Techniques for Fake News Identification

The sharing of information has entered an unprecedented era in human history due to emergence of the Internet With the widespread use of social media sites like Facebook and Twitter. As these platforms enjoy extensive use, users are generating and disseminating a wealth of information, some of which is inaccurate and devoid of factual basis. Detecting false or misleading information within textual content poses a significant challenge. Before arriving at a judgment regarding the accuracy of an article, it is imperative to consider various factors within a specific domain. This paper proposes an Ensemble method for the identification of fraudulent news stories. We leverage different textual features found in both authentic and fake news articles. Our dataset comprises 72,134 news articles, with 35,028 being genuine and 37,106 being false, categorized as binary 0s and 1s. To evaluate our approach, we employed well-known machine learning classifiers including Logistic Regression (LR), Decision Tree, AdaBoost, XGBoost, Random Forest, Extra Trees, SGD, SVM, and Naive Bayes. To enhance the precision of our findings, we devised a multi-model system for identifying fake news the Ensemble approach and the aforementioned classifiers. Experimental analysis conclusively demonstrates that our suggested ensemble learning technique surpasses the performance of individual learners

FACTS-ON : Fighting Against Counterfeit Truths in Online social Networks : fake news, misinformation and disinformation

L'évolution rapide des réseaux sociaux en ligne (RSO) représente un défi significatif dans l'identification et l'atténuation des fausses informations, incluant les fausses nouvelles, la désinformation et la mésinformation. Cette complexité est amplifiée dans les environnements numériques où les informations sont rapidement diffusées, nécessitant des stratégies sophistiquées pour différencier le contenu authentique du faux. L'un des principaux défis dans la détection automatique de fausses informations est leur présentation réaliste, ressemblant souvent de près aux faits vérifiables. Cela pose de considérables défis aux systèmes d'intelligence artificielle (IA), nécessitant des données supplémentaires de sources externes, telles que des vérifications par des tiers, pour discerner efficacement la vérité. Par conséquent, il y a une évolution technologique continue pour contrer la sophistication croissante des fausses informations, mettant au défi et avançant les capacités de l'IA. En réponse à ces défis, ma thèse introduit le cadre FACTS-ON (Fighting Against Counterfeit Truths in Online Social Networks), une approche complète et systématique pour combattre la désinformation dans les RSO. FACTS-ON intègre une série de systèmes avancés, chacun s'appuyant sur les capacités de son prédécesseur pour améliorer la stratégie globale de détection et d'atténuation des fausses informations. Je commence par présenter le cadre FACTS-ON, qui pose les fondements de ma solution, puis je détaille chaque système au sein du cadre : EXMULF (Explainable Multimodal Content-based Fake News Detection) se concentre sur l'analyse du texte et des images dans les contenus en ligne en utilisant des techniques multimodales avancées, couplées à une IA explicable pour fournir des évaluations transparentes et compréhensibles des fausses informations. En s'appuyant sur les bases d'EXMULF, MythXpose (Multimodal Content and Social Context-based System for Explainable False Information Detection with Personality Prediction) ajoute une couche d'analyse du contexte social en prédisant les traits de personnalité des utilisateurs des RSO, améliorant la détection et les stratégies d'intervention précoce contre la désinformation. ExFake (Explainable False Information Detection Based on Content, Context, and External Evidence) élargit encore le cadre, combinant l'analyse de contenu avec des insights du contexte social et des preuves externes. Il tire parti des données d'organisations de vérification des faits réputées et de comptes officiels, garantissant une approche plus complète et fiable de la détection de la désinformation. La méthodologie sophistiquée d'ExFake évalue non seulement le contenu des publications en ligne, mais prend également en compte le contexte plus large et corrobore les informations avec des sources externes crédibles, offrant ainsi une solution bien arrondie et robuste pour combattre les fausses informations dans les réseaux sociaux en ligne. Complétant le cadre, AFCC (Automated Fact-checkers Consensus and Credibility) traite l'hétérogénéité des évaluations des différentes organisations de vérification des faits. Il standardise ces évaluations et évalue la crédibilité des sources, fournissant une évaluation unifiée et fiable de l'information. Chaque système au sein du cadre FACTS-ON est rigoureusement évalué pour démontrer son efficacité dans la lutte contre la désinformation sur les RSO. Cette thèse détaille le développement, la mise en œuvre et l'évaluation complète de ces systèmes, soulignant leur contribution collective au domaine de la détection des fausses informations. La recherche ne met pas seulement en évidence les capacités actuelles dans la lutte contre la désinformation, mais prépare également le terrain pour de futures avancées dans ce domaine critique d'étude.The rapid evolution of online social networks (OSN) presents a significant challenge in identifying and mitigating false information, which includes Fake News, Disinformation, and Misinformation. This complexity is amplified in digital environments where information is quickly disseminated, requiring sophisticated strategies to differentiate between genuine and false content. One of the primary challenges in automatically detecting false information is its realistic presentation, often closely resembling verifiable facts. This poses considerable challenges for artificial intelligence (AI) systems, necessitating additional data from external sources, such as third-party verifications, to effectively discern the truth. Consequently, there is a continuous technological evolution to counter the growing sophistication of false information, challenging and advancing the capabilities of AI. In response to these challenges, my dissertation introduces the FACTS-ON framework (Fighting Against Counterfeit Truths in Online Social Networks), a comprehensive and systematic approach to combat false information in OSNs. FACTS-ON integrates a series of advanced systems, each building upon the capabilities of its predecessor to enhance the overall strategy for detecting and mitigating false information. I begin by introducing the FACTS-ON framework, which sets the foundation for my solution, and then detail each system within the framework: EXMULF (Explainable Multimodal Content-based Fake News Detection) focuses on analyzing both text and image in online content using advanced multimodal techniques, coupled with explainable AI to provide transparent and understandable assessments of false information. Building upon EXMULF’s foundation, MythXpose (Multimodal Content and Social Context-based System for Explainable False Information Detection with Personality Prediction) adds a layer of social context analysis by predicting the personality traits of OSN users, enhancing the detection and early intervention strategies against false information. ExFake (Explainable False Information Detection Based on Content, Context, and External Evidence) further expands the framework, combining content analysis with insights from social context and external evidence. It leverages data from reputable fact-checking organizations and official social accounts, ensuring a more comprehensive and reliable approach to the detection of false information. ExFake's sophisticated methodology not only evaluates the content of online posts but also considers the broader context and corroborates information with external, credible sources, thereby offering a well-rounded and robust solution for combating false information in online social networks. Completing the framework, AFCC (Automated Fact-checkers Consensus and Credibility) addresses the heterogeneity of ratings from various fact-checking organizations. It standardizes these ratings and assesses the credibility of the sources, providing a unified and trustworthy assessment of information. Each system within the FACTS-ON framework is rigorously evaluated to demonstrate its effectiveness in combating false information on OSN. This dissertation details the development, implementation, and comprehensive evaluation of these systems, highlighting their collective contribution to the field of false information detection. The research not only showcases the current capabilities in addressing false information but also sets the stage for future advancements in this critical area of study

Sentiment Analysis for Fake News Detection

[Abstract] In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2020/11This work has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades — Agencia Estatal de Investigación through the ANSWERASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (ref. ED431G 2019/01). David Vilares is also supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation. Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant No. 714150

Fake Content Detection in the Information Exponential Spreading Era

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementRecent years brought an information access democratization, allowing people to access a huge amount of information and the ability to share it, in a way that it can easily reach millions of people in a very short time. This allows to have right and wrong uses of this capabilities, that in some cases can be used to spread malicious content to achieve some sort of goal. Several studies have been made regarding text mining and sentiment analysis, aiming to spot fake information and avoid misinformation spreading. The trustworthiness and veracity of the information that is accessible to people is getting increasingly important, and in some cases critical, and can be seen has a huge challenge for the current digital era. This problem might be addressed with the help of science and technology. One question that we can do to ourselves is: How do we guarantee that there is a correct use of information, and that people can trust in the veracity of it? Using mathematics and statistics, combined with machine learning classification and predictive algorithms, using the current computation power of information systems, can help minimize the problem, or at least spot the potential fake information. One suggests developing a research work that aims to reach a model for the prediction of a given text content is trustworthy. The results were promising reaching a predicting model with good performance

An Exploratory Study of COVID-19 Misinformation on Twitter

During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1 500 tweets relating to 1 274 false and 276 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating (new tweets) or spreading (retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others. Our results enable us to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities and social media users to counter misinformation.Comment: 20 pages, nine figures, four tables. Submitted for peer review, revision