Towards the development of an explainable e-commerce fake review index: An attribute analytics approach

Instruments of corporate risk and reputation assessment tools are quintessentially developed on structured quantitative data linked to financial ratios and macroeconomics. An emerging stream of studies has challenged this norm by demonstrating improved risk assessment and model prediction capabilities through unstructured textual corporate data. Fake online consumer reviews pose serious threats to a business’ competitiveness and sales performance, directly impacting revenue, market share, brand reputation and even survivability. Research has shown that as little as three negative reviews can lead to a potential loss of 59.2 % of customers. Amazon, as the largest e-commerce retail platform, hosts over 85,000 small-to-medium-size (SME) retailers (UK), selling over fifty percent of Amazon products worldwide. Despite Amazon's best efforts, fake reviews are a growing problem causing financial and reputational damage at a scale never seen before. While large corporations are better equipped to handle these problems more efficiently, SMEs become the biggest victims of these scam tactics. Following the principles of attribute (AA) and responsible (RA) analytics, we present a novel hybrid method for indexing enterprise risk that we call the Fake Review Index (). The proposed modular approach benefits from a combination of structured review metadata and semantic topic index derived from unstructured product reviews. We further apply LIME to develop a Confidence Score, demonstrating the importance of explainability and openness in contemporary analytics within the OR domain. Transparency, explainability and simplicity of our roadmap to a hybrid modular approach offers an attractive entry platform for practitioners and managers from the industry

Combining Text Classification and Fact Checking to Detect Fake News

Due to the widespread use of fake news in social and news media, it is an emerging research topic gaining attention in today‘s world. In news media and social media, information is spread at high speed but without accuracy, and therefore detection mechanisms should be able to predict news quickly enough to combat the spread of fake news. It has the potential for a negative impact on individuals and society. Therefore, detecting fake news is important and also a technically challenging problem nowadays. The challenge is to use text classification to combat fake news. This includes determining appropriate text classification methods and evaluating how good these methods are at distinguishing between fake and non- fake news. Machine learning is helpful for building Artificial intelligence systems based on tacit knowledge because it can help us solve complex problems based on real-world data. For this reason, I proposed that integrating text classification and fact checking of check-worthy statements can be helpful in detecting fake news. I used text processing and three classifiers such as Passive Aggressive, Naïve Bayes, and Support Vector Machine to classify the news data. Text classification mainly focuses on extracting various features from texts and then incorporating these features into the classification. The big challenge in this area is the lack of an efficient method to distinguish between fake news and non-fake news due to the lack of corpora. I applied three different machine learning classifiers to two publicly available datasets. Experimental analysis based on the available dataset shows very encouraging and improved performance. Simple classification is not quite accurate in detecting fake news because the classification methods are not specialized for fake news. So I added a system that checks the news in depth sentence by sentence. Fact checking is a multi-step process that begins with the extraction of check-worthy statements. Identification of check-worthy statements is a subtask in the fact checking process, the automation of which would reduce the time and effort required to fact check a statement. In this thesis I have proposed an approach that focuses on classifying statements into check-worthy and not check-worthy, while also taking into account the context around a statement. This work shows that inclusion of context in the approach makes a significant contribution to classification, while at the same time using more general features to capture information from sentences. The aim of thischallenge is to propose an approach that automatically identifies check-worthy statements for fact checking, including the context around a statement. The results are analyzed by examining which features contributes more to classification, but also how well the approach performs. For this work, a dataset is created by consulting different fact checking organizations. It contains debates and speeches in the domain of politics. The capability of the approach is evaluated in this domain. The approach starts with extracting sentence and context features from the sentences, and then classifying the sentences based on these features. The feature set and context features are selected after several experiments, based on how well they differentiate check-worthy statements. Fact checking has received increasing attention after the 2016 United States Presidential election; so far that many efforts have been made to develop a viable automated fact checking system. I introduced a web based approach for fact checking that compares the full news text and headline with known facts such as name, location, and place. The challenge is to develop an automated application that takes claims directly from mainstream news media websites and fact checks the news after applying classification and fact checking components. For fact checking a dataset is constructed that contains 2146 news articles labelled fake, non-fake and unverified. I include forty mainstream news media sources to compare the results and also Wikipedia for double verification. This work shows that a combination of text classification and fact checking gives considerable contribution to the detection of fake news, while also using more general features to capture information from sentences

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

On the Detection of False Information: From Rumors to Fake News

Tesis por compendio[ES] En tiempos recientes, el desarrollo de las redes sociales y de las agencias de noticias han traído nuevos retos y amenazas a la web. Estas amenazas han llamado la atención de la comunidad investigadora en Procesamiento del Lenguaje Natural (PLN) ya que están contaminando las plataformas de redes sociales. Un ejemplo de amenaza serían las noticias falsas, en las que los usuarios difunden y comparten información falsa, inexacta o engañosa. La información falsa no se limita a la información verificable, sino que también incluye información que se utiliza con fines nocivos. Además, uno de los desafíos a los que se enfrentan los investigadores es la gran cantidad de usuarios en las plataformas de redes sociales, donde detectar a los difusores de información falsa no es tarea fácil. Los trabajos previos que se han propuesto para limitar o estudiar el tema de la detección de información falsa se han centrado en comprender el lenguaje de la información falsa desde una perspectiva lingüística. En el caso de información verificable, estos enfoques se han propuesto en un entorno monolingüe. Además, apenas se ha investigado la detección de las fuentes o los difusores de información falsa en las redes sociales. En esta tesis estudiamos la información falsa desde varias perspectivas. En primer lugar, dado que los trabajos anteriores se centraron en el estudio de la información falsa en un entorno monolingüe, en esta tesis estudiamos la información falsa en un entorno multilingüe. Proponemos diferentes enfoques multilingües y los comparamos con un conjunto de baselines monolingües. Además, proporcionamos estudios sistemáticos para los resultados de la evaluación de nuestros enfoques para una mejor comprensión. En segundo lugar, hemos notado que el papel de la información afectiva no se ha investigado en profundidad. Por lo tanto, la segunda parte de nuestro trabajo de investigación estudia el papel de la información afectiva en la información falsa y muestra cómo los autores de contenido falso la emplean para manipular al lector. Aquí, investigamos varios tipos de información falsa para comprender la correlación entre la información afectiva y cada tipo (Propaganda, Trucos / Engaños, Clickbait y Sátira). Por último, aunque no menos importante, en un intento de limitar su propagación, también abordamos el problema de los difusores de información falsa en las redes sociales. En esta dirección de la investigación, nos enfocamos en explotar varias características basadas en texto extraídas de los mensajes de perfiles en línea de tales difusores. Estudiamos diferentes conjuntos de características que pueden tener el potencial de ayudar a discriminar entre difusores de información falsa y verificadores de hechos.[CA] En temps recents, el desenvolupament de les xarxes socials i de les agències de notícies han portat nous reptes i amenaces a la web. Aquestes amenaces han cridat l'atenció de la comunitat investigadora en Processament de Llenguatge Natural (PLN) ja que estan contaminant les plataformes de xarxes socials. Un exemple d'amenaça serien les notícies falses, en què els usuaris difonen i comparteixen informació falsa, inexacta o enganyosa. La informació falsa no es limita a la informació verificable, sinó que també inclou informació que s'utilitza amb fins nocius. A més, un dels desafiaments als quals s'enfronten els investigadors és la gran quantitat d'usuaris en les plataformes de xarxes socials, on detectar els difusors d'informació falsa no és tasca fàcil. Els treballs previs que s'han proposat per limitar o estudiar el tema de la detecció d'informació falsa s'han centrat en comprendre el llenguatge de la informació falsa des d'una perspectiva lingüística. En el cas d'informació verificable, aquests enfocaments s'han proposat en un entorn monolingüe. A més, gairebé no s'ha investigat la detecció de les fonts o els difusors d'informació falsa a les xarxes socials. En aquesta tesi estudiem la informació falsa des de diverses perspectives. En primer lloc, atès que els treballs anteriors es van centrar en l'estudi de la informació falsa en un entorn monolingüe, en aquesta tesi estudiem la informació falsa en un entorn multilingüe. Proposem diferents enfocaments multilingües i els comparem amb un conjunt de baselines monolingües. A més, proporcionem estudis sistemàtics per als resultats de l'avaluació dels nostres enfocaments per a una millor comprensió. En segon lloc, hem notat que el paper de la informació afectiva no s'ha investigat en profunditat. Per tant, la segona part del nostre treball de recerca estudia el paper de la informació afectiva en la informació falsa i mostra com els autors de contingut fals l'empren per manipular el lector. Aquí, investiguem diversos tipus d'informació falsa per comprendre la correlació entre la informació afectiva i cada tipus (Propaganda, Trucs / Enganys, Clickbait i Sàtira). Finalment, però no menys important, en un intent de limitar la seva propagació, també abordem el problema dels difusors d'informació falsa a les xarxes socials. En aquesta direcció de la investigació, ens enfoquem en explotar diverses característiques basades en text extretes dels missatges de perfils en línia de tals difusors. Estudiem diferents conjunts de característiques que poden tenir el potencial d'ajudar a discriminar entre difusors d'informació falsa i verificadors de fets.[EN] In the recent years, the development of social media and online news agencies has brought several challenges and threats to the Web. These threats have taken the attention of the Natural Language Processing (NLP) research community as they are polluting the online social media platforms. One of the examples of these threats is false information, in which false, inaccurate, or deceptive information is spread and shared by online users. False information is not limited to verifiable information, but it also involves information that is used for harmful purposes. Also, one of the challenges that researchers have to face is the massive number of users in social media platforms, where detecting false information spreaders is not an easy job. Previous work that has been proposed for limiting or studying the issue of detecting false information has focused on understanding the language of false information from a linguistic perspective. In the case of verifiable information, approaches have been proposed in a monolingual setting. Moreover, detecting the sources or the spreaders of false information in social media has not been investigated much. In this thesis we study false information from several aspects. First, since previous work focused on studying false information in a monolingual setting, in this thesis we study false information in a cross-lingual one. We propose different cross-lingual approaches and we compare them to a set of monolingual baselines. Also, we provide systematic studies for the evaluation results of our approaches for better understanding. Second, we noticed that the role of affective information was not investigated in depth. Therefore, the second part of our research work studies the role of the affective information in false information and shows how the authors of false content use it to manipulate the reader. Here, we investigate several types of false information to understand the correlation between affective information and each type (Propaganda, Hoax, Clickbait, Rumor, and Satire). Last but not least, in an attempt to limit its spread, we also address the problem of detecting false information spreaders in social media. In this research direction, we focus on exploiting several text-based features extracted from the online profile messages of those spreaders. We study different feature sets that can have the potential to help to identify false information spreaders from fact checkers.Ghanem, BHH. (2020). On the Detection of False Information: From Rumors to Fake News [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/158570TESISCompendi

Review Paper on Enhancing COVID-19 Fake News Detection With Transformer Model

The growing propagation of disinformation about the COVID-19 epidemic needs powerful fake news detection technologies. This review provides an in-depth examination of existing techniques, including traditional machine learning methods such as Random Forest and Naive Bayes, as well as sophisticated models for deep learning such as Bi- GRU, CNN, and LSTM, RNN, & transformer-based architecture such as BERT and XLM- Roberta, are also available. One noticeable development is the merging of traditional algorithmswith sophisticated transformers, which emphasize the quest of improved accuracy and flexibility.However, important research gaps have been identified. There has been little research on cross- lingual detection algorithms, revealing a substantial gap in multilingual false news detection, which is critical in the global context of COVID-19 information spread. Furthermore, the researchemphasizes the need of flexible methodologies by emphasizing the need for appropriate preprocessing strategies for various content types. Furthermore, the lack of common assessment measures is a barrier, underlining the need of unified frameworks for successfully benchmarking and comparing models. This analysis provides light on the changing COVID-19 false news detection environment, emphasizing the need for novel, adaptive, and internationally relevant approaches to successfully address the ubiquitous dissemination of disinformation during the current pandemic