The purpose of this study is to examine Donald Trump's political communication regarding COVID-19 on Twitter during the time period 01-01-2020 to 30-09-2020. This study is motivated by the importance of analyzing Trump's power of definition regarding the situation surrounding the national crisis caused by COVID-19. The research questions include aspects of identifying frequent problem definitions, who is responsible for various crisis and whether Trump is motivating it, all provided through Trump’s tweets during the time period of the study.  The theoretical framework is constructed based on Entman (1993) as well as Matthes and Kohring (2008) to fulfill the purpose of the study and answer the research questions. The method is based on quantitative content analysis with qualitative elements. The method provided the ability to focus on the most frequent themes and topics. The analytical categories that have been used are: problem identification, problem definition, treatment recommendation, distribution of responsibility and whether it is motivated or not.  Findings of the most frequent societal crises were: Health crisis, Invisible Enemy-crisis and Information-crisis. Based on these three frames were identified: China is responsible for causing a Health crisis in the USA, China is responsible for causing the Invisible enemy-crisis in the USA and The spreading of disinformation by certain actors is harmful for the USA. With support of previous research of Donald Trump's political communication and usage of Twitter, this study highlights the importance of critically analyzing Trump, belonging to the political elite, how he uses his power to define COVID-19.

En 1996 le Président de la République Jacques Chirac, suspendait le service national. Supprimant ainsi un vivier non négligeable de ressources que possédaient les armées. Depuis lors le ministère de la Défense à du trouver des moyens pour palier à ce déficit de recrutement, d’image et de compétences. Les armées n’ont eu d’autres choix que celui de communiquer pour attirer et de donner envie à des dizaines de milliers de jeunes de rejoindre les rangs des forces armées. Si le surnom de la « Grande Muette » colle à la peau de l’armée française, cette dernière a entrepris depuis des années des réformes visant à améliorer sa communication publique. Dans cette société ou l’instantanéité est un principe élémentaire, où les outils numériques sont légion, la Défense n’a d’autre choix qu’apprendre à se servir de ces nouvelles passerelles numériques pour faire parler d’elle. Ainsi se servant des nouvelles tendances et ayant analysé le comportement de la classe d’âge concernée (18-30 ans), le ministère de la Défense tente désespérément d’investir les réseaux sociaux afin d’occuper le champ de bataille médiatique pour faire parler de lui et tenter de recruter. / In 1196, the President of the French republic, Jacques Chirac suspends the military service. This action has for consequences to cut definitely the link between French army and the youth. Ever since the French army search a way for recruit young people. The ministry of defense has a bad reputation and suffers of a stereotypical image. For sort out of this deficit of renown the French army decide to use the digital tools for speak at the youth. Nowadays the young people can live without smartphone, internet or Facebook. For that the French ministry of Defense tries to invade the mediatized battlefield.

Artificiell intelligens (AI) har tagit världen med storm de senaste åren där nya implementationer och uppfinningar ständigt tas fram och implementeras. Ansiktsigenkänning är en teknik inom AI som kan användas för att identifiera bland annat kriminella eller terrorister genom övervakningskameror, identifiera underåriga drickare och motverka identifikationsstöld. Problemet med ansiktsigenkänningstekniker är att det finns en brist på kunskap om människors attityd till ansiktsigenkänning. Samtidigt som utvecklingen av AI går i en rasande fart och användandet av AI ständigt ökar i samhället, hänger inte de etiska reflektionerna på användningen av AI med i den snabba tekniska utvecklingen av AI. Etiska reflektioner handlar om egenskaper, syften och tilliten till AI. Det vill säga, används ansiktsigenkänning på ett sätt som är allmänt accepterat av de som utsätts för tekniken. Detta är ett intressant ämne eftersom samhällen och världen befinner sig i denna utveckling just nu.    Denna studie har som syfte att försöka fylla bristen på kunskap om människors attityd till ansiktsigenkänning genom att analysera människors inställning till det. Studien som genomförs består av Twitter-data som ligger till grund för en sentimentanalys. En sentimentanalys består av en kombination av en kvalitativ och kvantitativ analys. Studiens resultat visar att inställningen till ansiktsigenkänning beror på kontexten eller situationen den används i och till vilket syfte. Enligt den Twitter-data som hämtades för denna studie, verkar inställningen till ansiktsigenkänning skilja sig mellan olika länder. Resultatet av denna studie har även likheter med tidigare studier som undersökt inställning till ansiktsigenkänning.   Studien avser att göra ett metodbidrag genom att processen för datahämtning samt dataanalys dokumenteras. I resultatet görs en granskning av attitydklassificeringen där verktyget som används för att avgöra inställning jämförs med vad två verkliga personer anser att inställningen i ett visst tweet är. Det visade att det fanns en stor skillnad mellan hur människorna i testet och verktyget som används klassificerade sentiment. / Artificial intelligence (AI) has taken the world by storm in recent years where new implementations and inventions are constantly being developed and implemented. Facial recognition is a technology in AI that can be used to identify criminals or terrorists through surveillance cameras, identify underage drinkers and counteract identity theft. The problem with facial recognition techniques is that there is a lack of knowledge about how people react toward them. At the same time as the development of AI is accelerating and the use of AI is constantly increasing in society, the ethical reflections on the use of AI are not part of the rapid technological development of AI. Ethical reflections are about characteristics, purposes, and trust in AI. That means analyzing if facial recognition is used in a way that is widely accepted by those who are exposed to it. This is an interesting topic because societies and the world are currently in this development.   The aim of this study is to try and fill the gaps in the lack of knowledge about people's attitude toward facial recognition by analyzing people's attitudes toward it. The study that is carried out consists of Twitter data, which undergoes a sentiment analysis, which is a combination of a qualitative and quantitative analysis. The results of the study show that the attitude toward facial recognition depends on the context or situation it is used for and for what purpose. According to the Twitter data that was obtained for this study, the attitude toward facial recognition seems to differ between different countries. The results of this study also have similarities with previous studies that examined attitudes toward facial recognition.   The study intends to make a method contribution by documenting the process for data retrieval and data analysis. The result includes a review of the attitude classification where the tool used to determine attitude is compared to what two real people think the attitude in a particular tweet is. It turned out that there was a big difference between how the people in the test and the tool used for the analysis classified sentiments.

The aim of this study is to explore favorable opportunity structures for populist communication of politicians in Western democracies. We analyze the content and style of 2,517 statements from 103 politicians from six countries (France, Italy, Germany, Switzerland, United Kingdom, and United States) who differ in their party affiliation (populist versus nonpopulist) and hierarchical position (backbencher vs. frontbencher). To learn more about their media strategies and chances of success, we investigate four communication channels (Facebook, Twitter, talk shows, and news media) that systematically differ in their degree of journalistic intervention and examine fourteen often-raised topics that differ in their suitability for populist mobilization. Our content analysis shows the highest probability of populist communication comes from (1) members of populist parties and (2) backbenchers who address (3) mobilizable issues in (4) social media or newspaper articles. We conclude by explaining why populists have become so successful in getting their messages into newspapers.

In the traditional agenda-setting theory, the agenda-setters were the news media and the public has a minimal role in the process of agenda-setting, which makes the public a passive receiver located at the bottom in the top-down agenda-setting dynamics. This study claims that with the development of Information communication technologies, primarily social media, the networked public may be able to set their own agendas through connective actions, outside the influence of the news media agenda. There is little empirical research focused on development and dynamics of public agenda-setting through social media platforms. Understanding the development and dynamics of public agenda-setting may be key to accounting for and overcoming conflicting findings in previous reverse agenda-setting research. This study examined the public agenda-setting dynamics through a case of gun violence prevention activism Twitter network, the #MarchForOurLives Twitter network. This study determined that the agenda setters of the #MarchForOurLives Twitter network are the key Never Again MSD student leaders and the March For Our Lives. The weekly reflected important events and issues and the identified topics were highly co-related with the themes examined in the tweets created by the agenda setters. The amplifiers comprised the vast majority of the tweets. The advocates and the supporters consisted of 0.44% and 4.43% respectively. The tweets made by the agenda setters accounted for 0.03%. The young activists and the like-minded and participatory public could continuously make changes taking advantage of technologies, and they could be the hope in the current and future society.

Since recent years, the use of non-traditional data sources by hedge funds in order to support investment decisions has increased. One of the data sources which has increased most is social media and it has become popular to analyze the public opinion with help of sentiment analysis in order to predict the performance of a company. In order to evaluate the public opinion one need big sets of Twitter data. The Twitter data was collected by streaming the Twitter feed and the stock data was collected from a Bloomberg Terminal. The aim of this study was to examine if there is a correlation between the public opinion of a stock and the stock price, and also what affects this relationship. While such a relationship cannot be established in general, we are able to show that if the data quality is good, there is a high correlation between the public opinion and stock price, and that significant events surrounding the company results in a higher correlation during that period. / De senaste åren har användandet av icke-traditionella datakällor ökat av hedgefonder för att ta investeringsbeslut. En av datakällorna som blivit populära är sociala medier och det har blivit vanligt att analysera folkopinionen med hjälp av sentimentanalys för att kunna förutspå ett företags resultat. För att analysera folkopinionen krävdes stora mängder Twitterdata. Twitter-datan hämtades genom att strömma Twitter-flödet och aktiedatan hämtades från en Bloomberg Terminal. Målet med studien var att undersöka ifall det finns en korrelation mellan folkopinionen av en aktie och aktiens prisutveckling, och även vad som påverkar denna relationen. Även om en sådan relation inte kan fastställas i allmänhet så kan vi visa att om datakvaliten är god, så finns det en hög korrelation mellan folkopinionen och aktiepriset, samt att vid betydande händelser som rör företaget, så resultar det i en hög korrelation under den perioden.

Bird feeding is somthing most are familiar with, either when children are feeding the swans or adults using some type of bird feeder in the garden. This project took that as inspiration to create something that automatically feed the birds while also monitoring them by capturing a picture and sending out this information as a teet on Twitter. This thesis examined if it was possible to connect different components relatively simple in order to create something more advanced. Research of the components was the first step in order to ensure the ability to create such a product. The following components were used, a Raspberry Pi 3 b+, Raspberry Pi camera module, IKEA PIR sensor, zigbee USB dongel, ultrasonic sensor and a micro servo motor. These components were connected to the Raspberry Pi and tested individually to ensure quality and function. When successfully tested, implementing them together was done and later construction of the mounting brackets and housing was made. Testing was done and results were finalized. This showed that all of our research questions were successfully answered with positive results and further research around implementing AI and weather protection would be usefully to successfully make this a fully functioning product. / Fågelmatning är något de flesta de flesta känner till, antingen när barn matar svanar eller vuxna som har en fågelmatare i trädgården. Det här projektet tog det som inspiration för att skapa något som automatiskt matar fåglar samtidigt som den övervakar funktionen genom att ta kort och skicka denna information som en tweet på Twitter. Den här avhandlingen undersökte om det var möjligt att sammankoppla olika komponenter relativt enkelt för att skapa något mer avancerat. Forskning om komponenterna var det första steget i att undersöka om detta var möjligt. Följande komponenter kom att användas, an Raspberry Pi 3 b+, Raspberry Pi kameramodul, IKEA PIRsensor, zigbee USBdongel, ultraljudsensor och en micro servomotor. Dessa komponenter kopplades in i Raspberry Pi:en och testades individuellt för att säkerhetställa kvalite och funktion. När testerna var lyckade implementerades de tillsammans och konstruktionen av fästen och ytterhölje fördigställdes. Tester gjordes och resultat sammanställdes. Dessa visade att alla våra forskningsfrågor blev besvarade med ett positivt resultat och framtida forskning kring implementeringen av AI och väderskydd är något som skulle kunna möjliggöra detta att bli en fullt fuktionerande produkt.

Работа посвящена изучению анализа тональности текстов в социальных сетях на примере сообщений-твитов из социальной сети Twitter. Материал исследования составили 818 224 сообщения по 17-ти ключевым словам, из которых 89 025 твитов содержали слова «COVID-19» и «Сoronavirus». В первой части работы рассматриваются общие теоретические и методологические вопросы: вводится понятие Sentiment Analysis, анализируются различные подходы к классификации тональности текстов. Особое внимание в задачах классификации текстов уделяется Байесовскому классификатору, который показывает высокую точность работы. Изучаются особенности анализа тональности текстов в социальных сетях во время эпидемий и вспышек болезней. Описывается процедура и алгоритм анализа тональности текста. Большое внимание уделяется анализу тональности текстов в Python с помощью библиотеки TextBlob, а также выбирается ещё один из инструментов «SaaS» - программное обеспечение как услуга, который позволяет реализовать анализ тональности текстов в режиме реального времени, где нет необходимости в большом опыте машинного обучения и обработке естественного языка, в сравнении с языком программирования Python. Вторая часть исследования начинается с построения выборок, т.е. определения ключевых слов, по которым в работе осуществляется поиск и экспорт необходимых твитов. Для этой цели используется корпус - Coronavirus Corpus, предназначенный для отражения социальных, культурных и экономических последствий коронавируса (COVID-19) в 2020 году и в последующий период. Анализируется динамика использования слов по изучаемой тематике в течение 2020 года и проводится аналогия между частотой их использования и происходящими событиями. Далее по выбранным ключевым словам осуществляется поиск твитов и, основываясь на полученных данных, реализуется анализ тональности cообщений с помощью библиотеки Python - TextBlob, созданной для обработки текстовых данных, и онлайн - сервиса Brand24. Сравнивая данные инструменты, отмечается схожесть полученных результатов. Исследование помогает быстро и в реальном времени понять общественные настроения по поводу вспышки COVID-19, способствуя тем самым пониманию развивающихся событий. Также данная работа может быть использована в качестве модели для определения эмоционального состояния интернет-пользователей в различных ситуациях. / The work is devoted to the sentiment analysis study of messages in Twitter social network. The research material consisted of 818,224 messages and 17 keywords, whereas 89,025 tweets contained the words "COVID-19" and "Coronavirus". In the first part, theoretical and methodological issues are considered: the concept of sentiment analysis is introduced, various approaches to text classification are analyzed. Particular attention in the problems of text classification is given to Naive Bayes classifier, which shows high accuracy of work. The features of sentiment analysis in social networks during epidemics and disease outbreaks are studied. The procedure and algorithm for analyzing the sentiment of the text are described. Much attention is paid to the analysis of sentiment of texts in Python using TextBlob library, and also one of the SaaS tools is chosen - software as a service, which allows real-time sentiment analysis of texts, where there is no need for extensive experience in machine learning and natural language processing against Python programming language. The second part of the study begins with sampling, i.e. definition of keywords by which the search and export of the necessary tweets is carried out. For this purpose, the Coronavirus Corpus is used, designed to reflect the social, cultural and economic consequences of the coronavirus (COVID-19) in 2020 and beyond. The dynamics of the topic words usage during 2020 is analyzed and an analogy is drawn between the frequency of their usage and the events in place. Next, the selected keywords are used to search for tweets and, based on the data obtained, the sentiment analysis of messages is carried out using the Python library - TextBlob, created for processing textual data, and the Brand24 online service. Comparing these tools, the results are similar. The study helps to understand quickly and in real-time public sentiments about the COVID-19 outbreak, thereby contributing to the understanding of developing events. Also, this work can be used as a model for determining the emotional state of Internet users in various situations.

Denna uppsats syftar till att undersöka hur filterbubblor och ekokammare skapar politisk polarisering. För att uppnå detta syfte appliceras en diskursanalys på debatten om koranbränning. Trådar på Flashback forum och kommentarsfält på Twitter undersöks för att urskilja eventuella retoriska strategier, diskursiva yttringar och sociokulturella aspekter som kan bidra till polarisering. För att uppnå detta appliceras Fairclough’s tredimensionella modell för att utföra en kritisk diskursanalys av innehållet på de definierade filterbubblorna / ekokammarna. / The aim of this study is to analyze how filterbubbles and echochambers affects political polarization. A discourse analysis will be applied on the debate that has broken out due to recent burnings of Qurans. More specifically, this method will be applied on Twitter and Flashback to study rhetorical strategies, discursive utterances and sociocultural views that is affecting polarization. Fairclough’s three-dimensional model will be applied to perform a critical discourse analysis on the defined filterbubbles / echochambers.

This paper uses a back-propagating neural network (BPN) to predict the price movements of major crypto currencies, leveraging technical factors as well as measurements of collective sentiment derived from the micro-blogging network Twitter. Our dataset consists of daily, hourly and minutely price levels for Bitcoin, Ether and Litecoin along with 8 popular technical indicators, as well as all tweets with the currencies' cash tags during respective time periods. Insprired by previous research which suggest that artificial neural networks are superior forecasting models in this setting, we were able to create a system generating automated investment decisions on a daily, hourly and minutely time basis. The study concluded that price trends are indeed predictable, with a correct prediction rate above 50% for all models, and corrensponding profitable trading strategies for all currencies on an hourly basis when neglecting trading fees, buy-sell spreads and order delays. The overall highest predictability is obtained on the hourly trading interval for Bitcoin, yielding an accuracy of 55.74% and a cumulative return of 175.1% between October 16, 2021 and December 31, 2021. / I denna studie används ett bakåtpropagerande neoronnät (BPN) för att förutsäga prisrörelser i större kryptovalutor med hjälp av tekniska faktorer och kvantifiering av kollektivt sentimentet från mikrobloggnätverket Twitter. Vårt dataset består av dagliga, timvisa och minutvisa prisnivåer för Bitcoin, Ether och Litecoin tillsammans med 8 populära tekniska indikatorer, samt alla tweets med valutornas "cash tags" under respektive tidsperiod. Med inspiration från tidigare forskning som hävdar att artificiella nauronnät är överlägsna prognosmodeller i denna typ av analys kunde vi skapa ett system som genererar automatiska investeringsbeslut på daglig, timvis och minutvis basis. Vi hävdar med denna studie att pristrender är förutsägbara för dessa kryptovalutor, med en korrekt förutsägelsefrekvens på över 50% för alla modeller, och med lönsamma handelsstrategier för alla valutor på timbasis när man bortser från handelsavgifter, köp- och säljspreadar och orderfördröjningar. Den högsta förutsägbarheten erhålls på timhandelsintervallet för Bitcoin, vilket ger en nogrannhet på 55,74% och en ackumulerad avkastning på 175,1% mellan den 16 oktober 2021 och den 31 december 2021.