Abstract. The outbreak of the novel coronavirus put democratic process, and security architecture across the globe in jeopardy. The global health crisis galvanised the proliferation of pandemic-related disinformation and other malign influence operations, and this phenomenon gave birth to a new buzzword, known as "infodemic". The "infodemic" provided hostile countries with a possibility to launch disinformation campaigns and other malign communication efforts, which in most cases were attributed to external state actors, such as China and Russia. Both actors aimed to weaken the legitimacy of European institutions, and undermine its democratic process. The "infodemic", thus, posed a threat to the EU's security and became a watershed moment in the disinformation discourse for the EU. In the aftermath, this mounting threat of disinformation was instantly acknowledged by EU representatives and institutions in their numerous official statements and policy documents. This dissertation examines the EU's approach towards Chinese and Russian disinformation campaigns through the lenses of securitisation theory and evaluates the success of this process. The findings of the evaluation showcased that neither Russian nor Chinese disinformation was successfully securitised by the EU. It also argues that the EU has...

This thesis makes use of biopolitical theory to examine the governmental and organizational response to the covid-19-infodemic. It aims to answer the puzzling research question as to why the infodemic – whose inherent problem is an overabundance of information – is responded to and met with even greater amounts of information by governments and health organizations, and what implications these measures may have on the population. The analysis finds that the question can partly be answered by derivation to previous research within the field of crisis communication: the most efficient way to respond to mis- and disinformation is to respond with correct information and with counter arguments. To answer the question in full an analysis of the subject of security is conducted where what can be interpreted from the material, following a modified version of Carol Lee Bacchi’s “What’s the Problem Represented to be?” method, is that the human judgement can be understood as the subject of security. The idea is that by securing the human judgment through improving people’s health literacy, people can be taught to act in a manner which is coherent with the state’s biopolitical goals, i.e. to secure the survival of the population. The analysis also shows that while these measures are made in an effort to secure the population, the measures themselves risk becoming a threat to the very population it is supposed to protect.

El presente trabajo analiza los factores que influencian la viralización de las fake news en Twitter durante la pandemia por Covid-19. En la actualidad, por la pandemia generada por el Covid-19, las fake news han sido tema de viralización en redes sociales. Por un lado, el coronavirus al ser una enfermedad nueva, ha sido utilizada para la creación de noticias falsas, ya que aún no hay información concreta y veraz por lo que es fácil crear y especular rumores. Por otro lado, ha salido de nuevo a la vista las fake news, ya que el uso de la internet ha generado que este fenómeno se vuelva más viral y tome relevancia en la actualidad, además de que por la pandemia muchas personas han tenido que cambiar su vida “normal” por una “virtual”. Las Fake News es un término que se le da a las noticias falsas que provocan alarma y desinformación en las personas, añadiendo que son más propensas a iniciar en internet. Lo que esta investigación busca a través de un análisis de contenido es hallar los factores que influyen en estas noticias y el porqué de su viralización. Asimismo, se seleccionarán tweets de Twitter que utilicen el hashtag #YoNoMeVacuno para entender a los usuarios de esta plataforma y ver los efectos de la viralización de Fake news en esta red social. / The present work analyzes the factors that influence the viralization of fake news on Twitter during the Covid-19 pandemic. Currently, due to the pandemic generated by Covid-19, fake news has been the subject of viralization on social networks. On the one hand, the coronavirus, being a new disease, has been used to create false news, since there is still no concrete and truthful information, so it is easy to create and speculate rumors. On the other hand, the fake news has come out again, since the use of the internet has caused this phenomenon to become more viral and become relevant today, in addition to the fact that due to the pandemic many people have had to change their "normal" life for a "virtual" one. Fake News is a term given to this news that causes alarm and misinformation in people, adding that they are more likely to start on the internet. What this research seeks through a content analysis is to find the factors that influence this news and the reason for its viralization. Likewise, Twitter tweets that use the hashtag #YoNoMeVacuno will be selected to understand the users of this social network and see the effects caused by the viralization of Fake news on this platform. / Trabajo de investigación

Syftet med denna uppsats är att ta reda på vilka socioekonomiska faktorer bidrar till intaget av vaccinet mot covid-19 i 13 olika bostadsområden i Stockholm. Författarna av denna uppsats åtar sig undersöka fyra samband med vaccinationsgraden i de utvalda områden, det vill säga samband med utbildning, inkomst, arbetslöshet och utländsk bakgrund. Tidigare forskning i frågan har påvisat motstridiga resultat, dock formas hypoteserna utifrån resultatet som återfinns i majoriteten studier. För att berika diskussionen tas även upp begrepp som institutionell tillit, infodemic och vaccintveksamhet. Metoden består av linjära regressionsmodeller där de oberoende variablerna i form av socioekonomiska faktorer redovisas och konstanthålls i flera kombinationer. Variablerna redovisas först för sig i enkla linjära regressioner, där inkomst och hög utbildning korrelerar positivt med vaccinationsgraden och arbetslöshet och utländsk bakgrund korrelerar negativt med vaccinationsgraden. Vidare görs regressionsmodeller om två och tre oberoende variabler. Överraskande i denna studie är att i detta skede visar sig inkomst ha ett negativt eller icke-signifikant samband med vaccinationsgraden. Även utbildning förlorar sin signifikans till vaccinationsgraden. Däremot behåller utländsk bakgrund sin signifikanta negativa korrelation med vaccinationsgraden. Avslutningsvis diskuteras variablerna i förhållande till den tidigare forskningen. / The purpose of this study is to find out which socioeconomic factors contribute to the intake of the covid-19 vaccine in 13 different residential areas in Stockholm. The authors of the following thesis undertake to research the association of four socioeconomic factors with the degree of vaccination in said areas, that is education level, average income, unemployment and immigrant background. Previous research in the question has shown contradictory evidence, the hypothesis of this study however is formulated in line with the majority of the research. To further enrich the discussion on the topic, notions of institutional trust, infodemic and vaccine hesitancy are considered. Additionally, tests of linear regression models are performed and adjusted in several combinations. Firstly, the independent variables are presented by themselves, where education and income correlate positively with the vaccination degree, whereas unemployment and immigrant background correlate negatively. Furthermore, linear regressions with two and three independent variables are performed. The surprising result here is that income either no longer has a significant correlation or correlates negatively with vaccination degree. Education level also loses its significance. On the other hand, immigrant background withholds its negative correlation with the vaccination degree. Finally, the results are discussed in relation to the prior research.

La presente investigación tiene como objetivo analizar la respuesta e interacción de los jóvenes estudiantes de la Facultad de Comunicaciones de la UPC dentro de grupos cerrados de la red social Facebook de la universidad con fake news o noticias falsas acerca del COVID-19. Para lograr el objetivo de la investigación se observa la publicación de noticias falsas y la interacción de los miembros del grupo cerrado de la UPC con respecto a ellas, durante la vigencia de la cuarentena por emergencia sanitaria en el Perú. A partir de los datos recogidos en la observación analizamos la respuesta e interacción de los miembros, utilizando los conceptos de noticia, noticia falsa y pos verdad. / This research aims to analyze the response and interaction of young students from the UPC's Faculty of Communications within closed groups of the university's Facebook social network with fake news about COVID-19. To achieve the objective of the investigation, the publication of fake news and the interaction of the members of the closed group of the UPC with respect to them are observed, during the validity of the quarantine for a health emergency in Peru. From the data collected in the observation, we analyzed the response and interaction of the members, using the concepts of news, false news and post truth. / Trabajo de investigación

Introduction : Le contenu en ligne est une source significative et primordiale pour les utilisateurs à la recherche d'informations liées à la santé. Pour éviter la désinformation, il est crucial d'automatiser l'évaluation de la fiabilité des sources et de vérification de la véracité des informations. Objectif : Cette étude visait à d’automatiser l'identification de la qualité des sources de santé en ligne. Pour cela, deux éléments complémentaires de qualité ont été automatisés : (1) L'évaluation de la fiabilité des sources d’information liée à la santé, en tenant compte des critères de la HONcode, et (2) L’appréciation de la véracité des informations, avec la base de données PubMed comme source de vérité. Méthodes : Dans cette étude, nous avons analysé 538 pages Web en englais provenant de 43 sites Web. Dans la première phase d’évaluation de la fiabilité des sources, nous avons classé les critères HONcode en deux niveaux : le "niveau pages Web" (autorité, complémentarité, justifiabilité, et attribution) et le "niveau sites Web" (confidentialité, transparence, divulgation financière et politique publicitaire). Pour le niveau pages Web, nous avons annoté 200 pages manuellement et appliqué trois modèles d’apprentissage machine (ML) : Forêt aléatoire (RF), machines à vecteurs de support (SVM) et le transformateur BERT. Pour le niveau sites Web, nous avons identifié des sacs de mots et utilisé un modèle basé sur des règles. Dans la deuxième phase de l’appréciation de la véracité des informations, les contenus des pages Web ont été catégorisées en trois catégories de contenu (séméiologie, épidémiologie et gestion) avec BERT. Enfin, l’automatisation de l’extraction des requêtes PubMed basée sur les termes MeSH a permis d’extraire et de comparer automatiquement les 20 articles les plus pertinents avec le contenu des pages Web. Résultats : Pour le niveau page Web, le modèle BERT a obtenu une meilleure aire sous la courbe (AUC) de 96 %, 98 % et 100 % pour les phrases neutres, la justifiabilité et l'attribution respectivement. SVM a présenté une meilleure performance pour la classification de la complémentarité (AUC de 98 %). Enfin, SVM et BERT ont obtenu une AUC de 98 % pour le critère d'autorité. Pour le niveau sites Web, le modèle basé sur des règles a récupéré les pages Web avec une précision de 97 % pour la confidentialité, 82 % pour la transparence, 51 % pour la divulgation financière et la politique publicitaire. Finalement, pour l’appréciation de la véracité des informations, en moyenne, 23 % des phrases ont été automatiquement vérifiées par le modèle pour chaque page Web. Conclusion : Cette étude souligne l'importance des modèles transformateurs et l'emploi de PubMed comme référence essentielle pour accomplir les deux tâches cruciales dans l'identification de sources d'information fiables en ligne : l’évaluation de la fiabilité des sources et vérifier la véracité des contenus. Finalement, notre recherche pourrait servir à améliorer le développement d’une approche d’évaluation automatique de la crédibilité des sites Web sur la santé. / Introduction: Online content is a significant and primary source for many users seeking healthrelated information. To prevent misinformation, it's crucial to automate the assessment of reliability of sources and fact-checking of information. Objective: This study aimed to automate the identification of the credibility of online information sources. For this, two complementary quality elements were automated: (1) The reliability assessment of health-related information, considering the HONcode criteria, and (2) The factchecking of the information, using PubMed articles as a source of truth. Methods: In this study, we analyzed 538 English webpages from 43 websites. In the first phase of credibility assessment of the information, we classified the HONcode criteria into two levels: the “web page level” (authority, complementarity, justifiability, and attribution) and the “website level” (confidentiality, transparency, financial disclosure, and advertising policy). For the web page level, we manually annotated 200 pages and applied three machine learning (ML) models: Random Forest (RF), Support Vector Machines (SVM) and the BERT Transformer. For those at website level criteria, we identified the bags of words and used a rule-based model. In a second phase of fact-checking, the contents of the web pages were categorized into three themes (semiology, epidemiology, and management) with BERT. Finally, for automating the factchecking of information, the automation of PubMed queries extraction using MeSH terms made it possible to automatically extract and compare the 20 most relevant articles with the content of the web pages. Results: For the web page level the BERT model obtained the best area under the curve (AUC) of 96%, 98% and 100% for neutral sentences, justifiability and attribution respectively. SVM showed a better performance for complementarity classification (AUC of 98%). Finally, SVM and BERT obtained an AUC of 98% for the authority criterion. For the websites level, the rules-based model retrieved web pages with an accuracy of 97% for privacy, 82% for transparency, 51% for financial disclosure and advertising policy. Finally, for fact-checking, on average, 23% of sentences were automatically checked by the model for each web page. Conclusion: This study emphasized the significance of Transformers and leveraging PubMed as a key reference for two critical tasks: assessing source reliability and verifying information accuracy. Ultimately, our research stands poised to significantly advance the creation of an automated system for evaluating the credibility of health websites.

This research explores how community-centered solutions facilitate the success and ownership of the response actions to deal with a public health emergency, such as the Covid-19 pandemic. When an outbreak or a hazard impacts a group of people, there is a strong need for communication in order to be able to access to the right information that takes people to make the correct decision and thus to take a protective action to be safe. This approach, known as Risk Communication and Community Engagement (RCCE)[1], allows the co-production of knowledge needed for a group of people to remain safe. For this approach, social listening tools, such as media monitoring and community feedback collection are critical understand communities’ needs. Its analysis allows to tailor a RCCE strategy that is able to substantially reduce the threat that a public health emergency poses to human lives[2].  Communities need solutions that are adapted to their needs in order to be able to deal with any emergency, including the Covid-19 pandemic. The RCCE approach empowers communities and provides them with the tools to amplify their voices. This participatory approach allows them to co-produce knowledge and get full ownership of the solutions.  Nevertheless, in an environment with excess of information, it may not be easy to discern the truth from the false. Unverified information and rumors are frequent and social media channels facilitate their rapid dissemination without borders. ‘Infodemic’ refers to an excessive amount of information concerning a problem such that the solution is made more difficult. (WHO, 2020)[3]  Some rumors may encourage people to take wrong decisions and perform actions that exacerbate risks during an emergency. The RCCE approach helps to promote real-time exchange of information to avoid that rumors and disinformation flourish. (WHO, 2018)[4]. It also allows to identify and implement community-centered solutions to communities’ problems.  RCCE needs data to monitor and evaluate its activities and reach effectively populations in risk to encourage them to observe the health preventive measures. Lives at risk depends on the right information conveyed through the right channel at the right time. To be able to supply tailored and accurate information to those communities and engage them, evidence-based RCCE strategies are needed, respecting the socio-anthropological and cultural context of the community. This research is based on the findings from five African countries -Cabo Verde, Cameroon, the Gambia, Mozambique and Niger-, all of them seriously affected by current Covid-19 pandemic. Its conclusions help to understand the critical role that RCCE plays in health emergencies resilient recovery.   [1] World Health Organization (WHO) (‎2020)‎. Risk communication and community engagement (‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎RCCE)‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎ readiness and response to the 2019 novel coronaviruses (‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎2019-‎‎nCoV)‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎: interim guidance, 26 January 2020. Geneva: WHO. [2] Risk Communication is one of the eight core functions of the International Health Regulations (2005) [3] World Health Organization (WHO) (‎2020)‎. Infodemic management: a key component of the COVID-19 global response. Weekly Epidemiological Record 95 (‎16)‎, 145 - 148. World Health Organization.  [4] World Health Organization (WHO) (2018). Communicating Risk in Public Health Emergencies - A WHO Guideline for Emergency Risk Communication (ERC) policy and practice. Geneva: World Health Organization.

Undoubtedly, the rapid development of information systems and the widespread use of electronic means and social networks have played a significant role in accelerating the pace of events worldwide, such as, in the 2012 Gaza conflict (the 8-day war), in the pro-secessionist rebellion in the 2013-2014 conflict in Eastern Ukraine, in the 2016 US Presidential elections, and in conjunction with the COVID-19 outbreak pandemic since the beginning of 2020. As the number of daily shared data grows quickly on various social networking platforms in different languages, techniques to carry out automatic classification of this huge amount of data timely and correctly are needed. Of the many social networking platforms, Twitter is of the most used ones by netizens. It allows its users to communicate, share their opinions, and express their emotions (sentiments) in the form of short blogs easily at no cost. Moreover, unlike other social networking platforms, Twitter allows research institutions to access its public and historical data, upon request and under control. Therefore, many organizations, at different levels (e.g., governmental, commercial), are seeking to benefit from the analysis and classification of the shared tweets to serve in many application domains, for examples, sentiment analysis to evaluate and determine user’s polarity from the content of their shared text, and misleading information detection to ensure the legitimacy and the credibility of the shared information. To attain this objective, one can apply numerous data representation, preprocessing, natural language processing techniques, and machine/deep learning algorithms. There are several challenges and limitations with existing approaches, including issues with the management of tweets in multiple languages, the determination of what features the feature vector should include, and the assignment of representative and descriptive weights to these features for different mining tasks. Besides, there are limitations in existing performance evaluation metrics to fully assess the developed classification systems. In this dissertation, two novel frameworks are introduced; the first is to efficiently analyze and classify bilingual (Arabic and English) textual content of social networks, while the second is for evaluating the performance of binary classification algorithms. The first framework is designed with: (1) An approach to handle Arabic and English written tweets, and can be extended to cover data written in more languages and from other social networking platforms, (2) An effective data preparation and preprocessing techniques, (3) A novel feature selection technique that allows utilizing different types of features (content-dependent, context-dependent, and domain-dependent), in addition to (4) A novel feature extraction technique to assign weights to the linguistic features based on how representative they are in in the classes they belong to. The proposed framework is employed in performing sentiment analysis and misleading information detection. The performance of this framework is compared to state-of-the-art classification approaches utilizing 11 benchmark datasets comprising both Arabic and English textual content, demonstrating considerable improvement over all other performance evaluation metrics. Then, this framework is utilized in a real-life case study to detect misleading information surrounding the spread of COVID-19. In the second framework, a new multidimensional classification assessment score (MCAS) is introduced. MCAS can determine how good the classification algorithm is when dealing with binary classification problems. It takes into consideration the effect of misclassification errors on the probability of correct detection of instances from both classes. Moreover, it should be valid regardless of the size of the dataset and whether the dataset has a balanced or unbalanced distribution of its instances over the classes. An empirical and practical analysis is conducted on both synthetic and real-life datasets to compare the comportment of the proposed metric against those commonly used. The analysis reveals that the new measure can distinguish the performance of different classification techniques. Furthermore, it allows performing a class-based assessment of classification algorithms, to assess the ability of the classification algorithm when dealing with data from each class separately. This is useful if one of the classifying instances from one class is more important than instances from the other class, such as in COVID-19 testing where the detection of positive patients is much more important than negative ones. / Graduate

En el marco de la pandemia global del coronavirus, las desinformaciones han ido aumentando cada vez más, especialmente, en redes sociales. Aunque este fenómeno no es nuevo, ha cobrado una mayor relevancia en los últimos años debido al avance de las tecnologías. En este trabajo de investigación, uno de los principales planteamientos, busca reconocer cuáles son las herramientas que utilizan los medios de verificación para detectar una noticia falsa en temas de salud e infodemia. Para esto se analizará el trabajo de dos medios verificadores: Ojo Público y Maldita.es / In the framework of the global coronavirus pandemic, misinformation has been increasing more and more, especially on social networks. Although this phenomenon is not new, it has become more relevant in recent years due to the advancement of technologies. In this research work, one of the main approaches, seeks to recognize which are the tools used by the verification media to detect false news on health and infodemic issues. For this, the work of two verifying media will be analyzed: Ojo Público and Maldita.es / Trabajo de investigación