In the first phase of my thesis I seek to explore how novelist use the fictional architectural environments in order to propel narrative and create an immersive experience for a reader, using the well-known 1984 by George Orwell and The Castle by Franz Kafka as the primary vehicle for investigation. The thesis studies the narrative and architectural typologies, physical settings and imagined spaces used to connect the viewer to the narrative’s highly detailed world. In the second phase of my thesis, I used elements learned from this two novels and applied them to propose a building in Midtown Manhattan in New York. I chose misinformation and fake news as the my main theme in order to design a mixed use proposal as it is one of the biggest challenges of our era. I end my thesis with a comic-style story using my proposal building as the main key in the comic-strips. If the first phase is about how architecture is used in fiction, the second phase is about creating a fictional story using architecture.

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La diffusion des Fake News sur les réseaux sociaux est devenue un problème central ces dernières années. Notamment, hoaxy rapporte que les efforts de fact checking prennent généralement 10 à 20 heures pour répondre à une fake news, et qu'il y a un ordre de magnitude en plus de fake news que de fact checking. Le fact checking automatique pourrait aider en accélérant le travail humain et en surveillant les tendances dans les fake news. Dans un effort contre la désinformation, nous résumons le domaine de Fact Checking Automatique basé sur le contenu en 3 approches: les modèles avec aucune connaissances externes, les modèles avec un Graphe de Connaissance et les modèles avec une Base de Connaissance. Afin de rendre le Fact Checking Automatique plus accessible, nous présentons pour chaque approche une architecture efficace avec le poids en mémoire comme préoccupation, nous discutons aussi de comment chaque approche peut être appliquée pour faire usage au mieux de leur charactéristiques. Nous nous appuyons notamment sur la version distillée du modèle de langue BERT tinyBert, combiné avec un partage fort des poids sur 2 approches pour baisser l'usage mémoire en préservant la précision. / The spreading of fake news on social media has become a concern in recent years. Notably, hoaxy found that fact checking generally takes 10 to 20 hours to respond to a fake news, and that there is one order of magnitude more fake news than fact checking. Automatic fact checking could help by accelerating human work and monitoring trends in fake news. In the effort against disinformation, we summarize content-based automatic fact-checking into 3 approaches: models with no external knowledge, models with a Knowledge Graph and models with a Knowledge Base. In order to make Automatic Fact Checking more accessible, we present for each approach an effective architecture with memory footprint in mind and also discuss how they can be applied to make use of their different characteristics. We notably rely on distilled version of the BERT language model tinyBert, combined with hard parameter sharing on two approaches to lower memory usage while preserving the accuracy.

Social media platforms create rich social structures, expand users' boundaries of social networks and revolutionize traditional forms of communications, social interactions and social relationships. These platforms not only facilitate the creation and sharing of news and information, but they also drive various kinds of businesses models, processes and operations, knowledge sharing, marketing strategies for brand management and socio-political discourses essential for healthy and democratic functions. As such, social media has greater implications on organizations and society brought about by individuals' social media usage patterns, and therefore, calls for further investigations. The main objective of this dissertation is to explore and offer insights into such social media usage and information sharing behaviors via data driven examination of various theories. This dissertation involves three studies that focus on factors that explain individuals' three different social media usage behaviors. Essay 1 investigates individuals' perceived importance of online affiliation, self-esteem, self-regulation and risk-benefit structure as antecedents of users' geo-tagging behavior on social media. Essay 2 examines the role of online news quality, source credibility, individuals' perception towards online civic engagement, attitude towards news sharing and social influences to understand users' news sharing behavior on social media platforms. Essay 3 seeks to examine the individuals' information verification behavior on social media through the lens of individuals' fake news awareness, perceived cost of information verification, trust in social media and truth-seeking.

Kapitel 1: Im Rahmen des Erwartungsnutzenmodells leite ich ein theoretisches Modell von choice bracketing aus zwei verhaltensökonomischen Axiomen ab. Das erste etabliert einen direkten Zusammenhang zwischen narrow bracketing und correlation neglect. Das zweite identifiziert den Referenzpunkt als den Ort, an dem broad und narrow Präferenzen miteinander verbunden sind. In meinem Modell ist der narrow bracketer durch die Unfähigkeit, Veränderungen vom Referenzpunkt in unterschiedlichen Dimensionen gleichzeitig zu verarbeiten, charakterisiert. Kapitel 2: Warum geben Menschen, wenn man sie fragt, präferieren aber, nicht gefragt zu werden, und nehmen sogar, wenn sich die Gelegenheit ergibt? Wir zeigen, dass Axiome wie Separabilität, narrow bracketing, und scaling invariance diese scheinbar widersprüchlichen Beobachtungen vorhersagen. Insbesondere implizieren diese Axiome, dass die Interdependenz von Präferenzen (“Altruismus”) ein Ergebnis des Interesses für das Wohlbefinden anderer im Gegensatz zu ihren bloßen Auszahlungen ist. Hierbei wird das Wohlbefinden durch die referenzabhängige Wertfunktion aus der Prospekttheorie erfasst. Kapitel 3: Wir untersuchen, wie sich fake news auf den Informationsfluss zwischen Nachrichtenportalen und  ökonomischen Agenten auswirkt. Wir erweitern das klassische cheaptalk- Modell um Unsicherheit über die Präferenzen des sender (Nachrichtenportal). Es gibt zwei Typen von Nachrichtenportalen. Ein fake-news-Portal möchte im Agenten unabhängig vom wahren Zustand eine maximale Erwartung wecken. Ein legitimes Nachrichtenportal möchte die Wahrheit offenbaren. Wir zeigen, dass jedes informative perfekte Bayesianische Gleichgewicht durch einen Schwellenwert charakterisiert ist. Während der Agent alle Zustände unter dem Schwellenwert unterscheiden kann, ist es ihm unmöglich, Zustände über dem Schwellenwert zu unterscheiden. / Chapter 1: I derive a theoretical model of choice bracketing from two behavioral axioms in an expected utility framework. The first behavioral axiom establishes a direct link between narrow bracketing and correlation neglect. The second behavioral axiom identifies the reference point as the place where broad and narrow preferences are connected. In my model, the narrow bracketer is characterized by an inability to process changes from the reference point in different dimensions simultaneously. Chapter 2: Why do people give when asked, but prefer not to be asked, and even take when possible? We show that standard behavioral axioms including separability, narrow bracketing, and scaling invariance predict these seemingly inconsistent observations. Specifically, these axioms imply that interdependence of preferences (“altruism”) results from concerns for the welfare of others, as opposed to their mere payoffs, where individual welfares are captured by the reference-dependent value functions known from prospect theory. The resulting preferences are non-convex, which captures giving, sorting, and taking directly. Chapter 3: We present a theoretical model to investigate how the presence of fake news affects information transmission from media outlets to economic agents. In a standard cheap talk framework we introduce uncertainty about the sender’s (media outlet’s) preferences. There are two types of media outlets. A fake news outlet wants to push the agent’s belief to the maximum irrespective of the state of the world. A legitimate outlet wants to reveal the true state to the agent. We show that any informative perfect Bayesian equilibrium of our game is characterized by a threshold value. While the agent can perfectly separate amongst states below the threshold value, there is no separation amongst states above the threshold value. We determine the unique most informative threshold value for a general class of equilibria.

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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/10251/158570 / Compendio

In an attempt to create a fake news detection tool using a large language model (LLM), the emphasis is on validating the effectiveness of this approach and then making the tooling readily available. With the current model of gpt-4-turbo-preview and its assistant capabilities combined with simple prompts tailored to different objectives. While tools to detect fake news and simplify the process are not new, insight into how they work and why is not commonly available, most likely due to the monetization around the current services. By building an open-source platform that others can expand upon, giving insight into the prompts used, and enabling experimentation and a baseline to start at when developing further or taking inspiration from.  The results when articles are not willfully written as fake but missing key data are obviously very hard to detect. However, common tabloid-style news, which are often shared to create an emotional response, shows more promising detection results.

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As with every U.S. election cycle, the early 2016 election season was a roller-coaster experience, with positive and negative campaign messages emerging from both parties, and with high hopes and dashed hopes for both Democratic and Republican candidates. However, as Donald J. Trump emerged as the Republican candidate to challenge Democrat Hillary R. Clinton for the Office of President, the United States appeared to be equally bemused, horrified and confident in a Democratic victory — after all, how could someone so unorthodox as Donald Trump become the next president? His divisive campaign had become predictive, for many voters, of a clear victory for Clinton. However, those within vulnerable groups, including women, immigrants and the LGBTQ communities, along with many allies, noted the growing normalization of his micro- and macro-aggressions by the mainstream media, and the societal legitimization of his campaign.