Marketing Intelligence: Boom or Bust of Service Marketing?

Marketing intelligence fosters two major developments within digital service marketing. On the one hand, a boom of services seems to have evolved, accelerated by the opportunities of marketing intelligence. It has contributed to the optimization of customer experiences, e.g., supported by mobile, personalized, and customized marketing services. On the other hand, (digital) self-services are likely to pervert the term “service”. Lifecycle marketing, including annoying marketing communication in real-time, automated price adjustment and programmatic advertising based on artificial intelligence, affects the vision of fully standardized marketing automation. Additionally, there are incentives to pollute the digital information in order to manufacture opinions. Fake news is one popular example. This leads to the (open) question if marketing intelligence means service boom or bust of marketing. This contribution aims to elaborate the boom-and-bust aspects of marketing intelligence and suggests a trade-off. The method applied in this paper will be a descriptive and conceptual literature review, through which the paradigmatic thoughts will be juxtaposed from the perspective of service

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

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

Fake News in the era of online intentional misinformation ; a review of existing approaches

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.Fake news is probably one of the most discussed issues of the past years. The term has acquired greater legitimacy after being named the word of the year by Collins Dictionary, following what the dictionary called its “ubiquitous presence” over the year 2017. However, the fake news issue has not been yet deeply researched. Therefore, in this thesis, definitions by the literature about the term “fake news” are gathered and through them, specific characteristics and criteria are extracted in order to verify the exact elements of false news and intentional misinformation in general. This study aims to identify eventually as thoroughly as possible what fake news is and what is not. For that purpose, through qualitative research, the total features of the term are exhibited and analyzed concluding in the classification of characteristics most of the fake news incidents present. Following the proposed feature identification is examined through specific fake news case studies. Finally, after understanding deeper and verifying specific characteristics that appear on the nature of fake news detection and mitigation actions are proposed, demonstrating the need for technological development on the issue and educational evolution on digital skills of the public, accomplishing an inclusive review of a less studied term, such as the fake news. At last conclusions are presented leading to the main remark of the current thesis, namely the need for further quantitative and statistical research as much as deeper theoretical study, to better decipher the issue of fake news and thus resolve it

Improving Reader Motivation with Machine Learning

This thesis focuses on the problem of increasing reading motivation with machine learning (ML). The act of reading is central to modern human life, and there is much to be gained by improving the reading experience. For example, the internal reading motivation of students, especially their interest and enjoyment in reading, are important factors in their academic success. There are many topics in natural language processing (NLP) which can be applied to improving the reading experience in terms of readability, comprehension, reading speed, motivation, etc. Such topics include personalized recommendation, headline optimization, text simplification, and many others. However, to the best of our knowledge, this is the first work to explicitly address the broad and meaningful impact that NLP and ML can have on the reading experience. In particular, the aim of this thesis is to explore new approaches to supporting internal reading motivation, which is influenced by readability, situational interest, and personal interest. This is performed by identifying new or existing NLP tasks which can address reader motivation, designing novel machine learning approaches to perform these tasks, and evaluating and examining these approaches to determine what they can teach us about the factors of reader motivation. In executing this research, we make use of concepts from NLP such as textual coherence, interestingness, and summarization. We additionally use techniques from ML including supervised and self-supervised learning, deep neural networks, and sentence embeddings. This thesis, presented in an integrated-article format, contains three core contributions among its three articles. In the first article, we propose a flexible and insightful approach to coherence estimation. This approach uses a new sentence embedding which reflects predicted position distributions. Second, we introduce the new task of pull quote selection, examining a spectrum of approaches in depth. This article identifies several concrete heuristics for finding interesting sentences, both expected and unexpected. Third, we introduce a new interactive summarization task called HARE (Hone as You Read), which is especially suitable for mobile devices. Quantitative and qualitative analysis support the practicality and potential usefulness of this new type of summarization

Emotions related to mergers and acquisitions : Discussion analyses of SSAB Ab–Rauraruukki Oyj and STX Finland Oy–Meyer Werft GmbH

There has been a wave of mergers and acquisitions during the past few decades. However, their failure rate remains high and poorly understood. Moreover, the M&A process is highly sensitive in nature. The pre-acquisition, combination and post-merger integration phases are in the core of this study due to these strong emotions evoked. Moreover, M&As are highly emotional event. Emotions are short-term, and have a certain trigger. Majority of management literature still overrides emotions. Despite the individuality of emotions, they are proved to be rather homogenous in an organisational change. Media provokes these emotions, and functions as a daily mirror. Thereby, newspaper articles have a big influence on employees’ emotions. In the case of cross-border M&As, cultural differences add their own spice to managing emotions. The purpose of this study is to increase understanding regarding the subject of how emotions are portrayed in the media in the case of cross-border mergers and acquisitions. It has been further divided into sub-objectives, to characterize what types of emotions are reflected in mergers and acquisitions and to analyze how these emotions evolve during the merger and acquisition process, and to further describe what are the triggers for these emotions. The purpose of the study was approached through both theory and applying it to practice through discussion analysis research strategy. The aim of this study was to describe real-life case events with time and space limitations, and thereby a qualitative approach was chosen. Moreover, a holistic approach was needed, and discussion analysis of two case studies was provided with a sample of 139 newspaper articles with reference to SSAB Ab–Rautaruukki Oyj merger or to STX Finland Oy–Meyer Werft GmbH acquisition. The articles were coded according to their content, based on what emotion or emotions was reflected in each article and they were targeted accordingly with the NVivo software. For the analysis, frameworks and decision rules were developed using the theories presented in the study. The analyzes of this study support the theory of M&As being highly emotional events, as various emotions, both positive, as for STX–Meyer, and negative, case SSAB–Rautaruukki, were reflected in the case M&As. These emotions evolve and differ in intensity as well as between the emotional tones throughout the phases of the deal. Triggers for these emotions are various, and they vary from case to case. However, common denominators often are, the newly combined organization, cultural differences in management styles or the management itself, the deal itself, the former parent companies of each party, or the collective emotions of the employees. This study has contributed in providing discussions on international M&As as well as on how the group emotions evolve and illustrations through the two, highly comparable yet so divergent case studies