This is a study of how the image of Sweden is constructed in international media. Using the country as a swinging bat in debates on socialism and progressiveness is nothing new but has had an upswing during recent years as a result of the global rise of right-wing forces. With the help of Critical Discourse Analysis, four articles from the United States and the United Kingdom are analysed. These are then presented according to Fairclough’s three-layered model. With the help of previous research on Sweden in international media, fake news and nation branding, these findings are then explained and put into context.The study finds that the image of Sweden presented in media tends to follow the narrative of Good Sweden and Bad Sweden. On the one hand is the classic welfare state in the north, which takes care of its people and with high levels of trust between the actors. On the other hand is a country in ruins as a result of letting in too many immigrants. Both narratives rely heavily on stereotypes. The discussion tends to use Sweden as an example, when it is really about ideologies and values. Another result shown by the study is that fake news is a common trace in news about Sweden, not only in alternative media but also in the established elite media. This can be seen as a result of the hardening situation in the media business as well as the rise of right-wing forces.

Sveriges regering och SÄPO har identifierat fake news som ett hot mot demokratin. I denna studie undersöker vi om fake news påverkar individer, trots att de vid samma tillfälle erhåller korrekt information inom ämnet. Detta gjordes genom en enkätundersökning på studenter vid Uppsala universitet. Vi fann att erhållandet av korrekt information inte är tillräckligt för att motverka effekten av att exponeras för falsk information. De studenter som fick läsa en mening med falsk information var 15 procentenheter mer sannolika att svara att de anser att staten lägger för mycket resurser på invandringen jämfört med kontrollgruppen. Resultatet tyder på att politiker, organisationer och privatpersoner kan dra nytta av att sprida fake news, att de kan göra så anonymt, och att faktagranskning ensamt inte kan stävja problemet med fake news. / The Swedish government and the Swedish Security Service have identified fake news as a threat to democracy. In this study, we investigate if fake news affect individuals, even though they receive correct information regarding the subject simultaneously. This was accomplished through handing out a survey to students at Uppsala University. We found that obtaining correct information is insufficient to counteract the effects of being exposed to fake news. The students who read a sentence with false information were 15 percentage points more likely to answer that they believe that the Swedish government allocates too much resources towards immigration compared to the control group. The result indicate that politicians, organizations and individuals can take advantage of spreading fake news, that they can do so anonymously, and that fact checking alone cannot solve the problem of fake news.

In recent years, deceptive contents such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect, for online users. Fake reviews affect consumers and stores a like. Furthermore, the problem of fake news has gained attention in 2016, especially in the aftermath of the last US presidential election. Fake reviews and fake news are a closely related phenomenon as both consist of writing and spreading false information or beliefs. The opinion spam problem was formulated for the first time a few years ago, but it has quickly become a growing research area due to the abundance of user-generated content. It is now easy for anyone to either write fake reviews or write fake news on the web. The biggest challenge is the lack of an efficient way to tell the difference between a real review or a fake one; even humans are often unable to tell the difference. In this thesis, we have developed an n-gram model to detect automatically fake contents with a focus on fake reviews and fake news. We studied and compared two different features extraction techniques and six machine learning classification techniques. Furthermore, we investigated the impact of keystroke features on the accuracy of the n-gram model. We also applied semantic similarity metrics to detect near-duplicated content. Experimental evaluation of the proposed using existing public datasets and a newly introduced fake news dataset introduced indicate improved performances compared to state of the art. / Graduate

A prominent British newspaper and its website publishes an inflammatory article stating that a lifeboat charity has been cynically abused by migrant traffickers who are using it as a ‘free ferry service’ to get their cargo of human beings into the United Kingdom. What reaction is generated on the Twitter social media network? What narrative, language usage and sentiment is formed? How does the charity react?This thesis examines this case and discovers through word frequency and conversational analysis how one news story reverberated in 280 characters or less. Themes impacted by this research include Twitter as a social media network service, fake news, echo chambers and their bubbles, trust and audience perception, news media literacy, social campaigning and awareness, and crisis communication and news/stakeholder management.The conclusion reached is that the story had the potential to adversely affect the charity’s reputation and future income stream even though it was doing its duty because of its unwillingness or inability to engage with stakeholders and correct any misunderstandings. The thesis discusses why this was not a good idea and considers how the story could have developed into a broader, more damaging entity with relative ease, especially with the role social media can play for news consumers in today’s society.

abstract: Social media has become an important means of user-centered information sharing and communications in a gamut of domains, including news consumption, entertainment, marketing, public relations, and many more. The low cost, easy access, and rapid dissemination of information on social media draws a large audience but also exacerbate the wide propagation of disinformation including fake news, i.e., news with intentionally false information. Disinformation on social media is growing fast in volume and can have detrimental societal effects. Despite the importance of this problem, our understanding of disinformation in social media is still limited. Recent advancements of computational approaches on detecting disinformation and fake news have shown some early promising results. Novel challenges are still abundant due to its complexity, diversity, dynamics, multi-modality, and costs of fact-checking or annotation. Social media data opens the door to interdisciplinary research and allows one to collectively study large-scale human behaviors otherwise impossible. For example, user engagements over information such as news articles, including posting about, commenting on, or recommending the news on social media, contain abundant rich information. Since social media data is big, incomplete, noisy, unstructured, with abundant social relations, solely relying on user engagements can be sensitive to noisy user feedback. To alleviate the problem of limited labeled data, it is important to combine contents and this new (but weak) type of information as supervision signals, i.e., weak social supervision, to advance fake news detection. The goal of this dissertation is to understand disinformation by proposing and exploiting weak social supervision for learning with little labeled data and effectively detect disinformation via innovative research and novel computational methods. In particular, I investigate learning with weak social supervision for understanding disinformation with the following computational tasks: bringing the heterogeneous social context as auxiliary information for effective fake news detection; discovering explanations of fake news from social media for explainable fake news detection; modeling multi-source of weak social supervision for early fake news detection; and transferring knowledge across domains with adversarial machine learning for cross-domain fake news detection. The findings of the dissertation significantly expand the boundaries of disinformation research and establish a novel paradigm of learning with weak social supervision that has important implications in broad applications in social media. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2020

Conocer sobre el impacto de las noticias falsas, la desinformación durante la crisis del COVID19, así como las oportunidades y riesgos en el tratamiento de contenidos periodísticos

abstract: The development of the internet provided new means for people to communicate effectively and share their ideas. There has been a decline in the consumption of newspapers and traditional broadcasting media toward online social mediums in recent years. Social media has been introduced as a new way of increasing democratic discussions on political and social matters. Among social media, Twitter is widely used by politicians, government officials, communities, and parties to make announcements and reach their voice to their followers. This greatly increases the acceptance domain of the medium. The usage of social media during social and political campaigns has been the subject of a lot of social science studies including the Occupy Wall Street movement, The Arab Spring, the United States (US) election, more recently The Brexit campaign. The wide spread usage of social media in this space and the active participation of people in the discussions on social media made this communication channel a suitable place for spreading propaganda to alter public opinion. An interesting feature of twitter is the feasibility of which bots can be programmed to operate on this platform. Social media bots are automated agents engineered to emulate the activity of a human being by tweeting some specific content, replying to users, magnifying certain topics by retweeting them. Network on these bots is called botnets and describing the collaboration of connected computers with programs that communicates across multiple devices to perform some task. In this thesis, I will study how bots can influence the opinion, finding which parameters are playing a role in shrinking or coalescing the communities, and finally logically proving the effectiveness of each of the hypotheses. / Dissertation/Thesis / Masters Thesis Computer Science 2020

Due to the large increase in the proliferation of "fake news" in recent years, it has become a widely discussed menace in the online world. In conjunction with this popularity, research of ways to limit the spread has also increased. This paper aims to look at the current research of this area in order to see what automatic fake news detection methods exist and are being developed, which can help online users in protecting themselves against fake news. A systematic literature review is conducted in order to answer this question, with different detection methods discussed in the literature being divided into categories. The consensus which appears from the collective research between categories is also used to identify common elements between categories which are important to fake news detection; notably the relation of headlines and article content, the importance of high-quality datasets, the use of emotional words, and the circulation of fake news in social media groups.

The submitted thesis focuses on the photograph and its role in the process of disseminating disinformation in the online environment. The thesis deals with the opinion that, despite the fundamental changes in the understanding of photography and its credibility, which together with the transition from its analogue form to digital one, photography retains the status of an authentic medium. For this reason photography is becoming a very powerful tool for spreading misinformation. The thesis deals with the theoretical basis of objectivity of photography, its documentary value and expectation of authenticity. The role of the context, which is crucial for understanding the photographic message, will be emphasized. The thesis also defines the basic concepts related to the phenomenon of disinformation. The strategies used to spread disinformation via photography is also presented. In the practical part of the thesis particular disinformative photographic messages is analyzed. Ways to verify the authenticity of particular photographic images are presented. Keywords: photography, authenticity, disinformation, hoax, fake news, online environment, manipulation

The diploma thesis consists of two main parts. In the theoretical part, we deal with the basic terminological framework of fake news, the development of misinformation, fake news, hoaxes, propaganda and their form and the form they acquired. We will also look at misinformation, hoax and propaganda as a specific genre of false news. Furthermore, we analyze the spread of hoaxes and disinformation and the criteria by which hoaxes are recognized and labeled. An important part of the theoretical part of the thesis is also the manipulation with the content and the determination of the criteria on the basis of which false information can be recognized. We will focus primarily on the electronic and new media domains, which are mainly represented by social networks. In the research and analytical part of the thesis we look at the ways in which different sites classify misinformation and hoaxes, by what criteria they approach their classification, and whether these methods are unambiguous and consistent. The second important part of the research will analyze the attitudes of traditional and alternative media to work with false news and hoaxes. We will try to bring a glimpse of both stakeholders, that is to say, representatives of traditional media and alternative media.