Fake news, or information disorder, is a societal problem that could be partially remedied by automatic detection tools. While still a young research field many such tools have been proposed in academic writing. This systematic mapping study gives an overview of the current research in Natural Language Process-based fake news detection utilising Machine Learning and Neural Network classification algorithms in regards to which classification algorithms have been studied and which datasets have been used. Furthermore, we attempt to make a generalised description of the performance (measured in f-score and accuracy) of the most commonly occurring classification algorithms. From a corpus of 124 research articles and other scientific texts we identify 63 different datasets mainly written in English, and 116 different classification algorithms. The seven most commonly occurring algorithms (Random Forest, Logistic Regression, Support Vector Machine, Decision Tree, Long Short- TermMemory, K-Nearest Neighbors, Convolutional Neural Network) together make up almost 50% of all algorithm occurences in the article corpus. For these seven, the ten occurrences with the best performance are listed. Out of the datasets, the six most common datasets (ISOT, FakeNewsNet, Patwa 2021, LIAR, Bisaillon, and UTK-MLC) together make up 44% of all dataset occurrences. Apart from English, the represented languages were mainly Chinese (Mandarin), Portugese, Indonesian, Bangla, and Albanian. / Olika typer av desinformation (så kallade fake news), är ett problem för dagens samhälle. En av flera möjliga dellösningar på problemet utgörs av automatiserad fake news-detektion. Trots att detta forskningsfält är relativt nytt finns det en uppsjö av olika föreslagna modeller för automatiserad fake news-detektion. Denna systematiska kartläggning syftar till att ge en överblick över den aktuella forskningen inom Natural Language Processing-baserad automatiserad fake news-detektion med klassifikationsalgoritmer både inom maskininlärning och neurala nätverk. Översikten avser vilka klassifikationsalgoritmer samt vilka dataset som förekommer inom forskningen. Vidare försöker vi göra en generell beskrivning av prestandan hos de vanligast förekommande klassifikationsalgoritmerna, mätt i accuracy och f-score. Kartläggningen omfattar en samling på 124 artiklar och andra vetenskapliga texter, ur vilka vi identifierade 63 förekommance dataset och 116 olika förekommande klassifikationsalgoritmer. De sju vanligast förekommande algoritmerna (Random Forest, Logistic Regression, Support Vector Machine, Decision Tree, Long-Short Memory Network, K-Nearest Neighbors, Convolutional Neural Network) utgör tillsammans 49% av alla förekomster inom artikelsamlingen. Vi har tagit ut santliga förekomster av prestandaresultat för dessa sju algoritmer, och listat de tio bästa prestandaresultaten för var och en av de sju algoritmerna. De sex vanligast förekommande dataseten (ISOT, FakeNewsNet, Patwa 2021, LIAR, Bisaillon, and UTK-MLC) utgör tillsammans 44% av alla förekomster. Engelska var med stor marginal det vanligast förekommande språket inom dataseten, andra språk som förekom var kinesiska (mandarin), portugisiska, indonesiska, bangla, och albanska.

Under det amerikanska presidentvalet 2016 blev falska nyheter ett omtalat fenomen, vilketkarakteriserades av att avsiktligt falsk eller vilseledande information spreds över internet ochsociala medier. Falska nyheter är falska eller vilseledande påståenden som avsiktligtpresenteras som sanna nyheter. Utvecklingen av internet och sociala medier medför att detidag finns en distinktion mellan etablerade och alternativa medier. För att förstå fenomenetfalska nyheter har studien undersökt hur begreppet påverkats av denna medieutveckling.Syftet var att undersöka hur rapporteringen kring falska nyheter ser ut i etablerade medierrespektive alternativmedier. Huvudfrågan var: Hur konstrueras begreppet falska nyheter i deetablerade och alternativa nyhetsmedier i Sverige som valts för denna uppsats? Studien haranvänt sig av diskursanalys för att undersöka olika diskursiva gestaltningar av falska nyheter,och hur alternativmedier förhåller sig till övrig media i medielandskapet. Resultatet visar attetablerad media huvudsakligen har en internationell diskurs medan alternativmedier fokuserarmer på den nationella diskursen. Vid analysen av resultatet har teorier om ideologi,diskursanalys samt gestaltning använts. Analysen visar att falska nyheter används avnyhetsmedierna som ett ideologiskt begrepp för att visa på skillnader mellan etablerad mediaoch alternativmedier samt att förståelsen av begreppet ”falska nyheter” är beroende av englobal intertextualitet. / During the US presidential election in 2016, “fake news” became a well-known phenomenon,which was characterized by the deliberate spread of false or misleading information over theinternet and social media. False news is false or misleading claims intentionally presented asreal news. The development of internet and social media means that there is today adistinction between established and alternative media. To understand the phenomenon of fakenews, the study has investigated how the concept was affected by this media development.The purpose was to investigate how reporting on false news appears in established media andalternative media. The main question was: How is the concept of fake news constructed inestablished and alternative news media in Sweden? The study has used discourse analysis toinvestigate various discursive news reports of fake news, and how alternative media relate toother news media. The result shows that established media mainly have an internationaldiscourse while alternative media focus more on the national discourse. The result has beenanalyzed with theories of ideology, discourse analysis and framing. The analysis shows thatfake news is used by the news media as an ideological concept to show differences betweenestablished media and alternative media and the understanding of the term "fake news" isdependent on a global intertextuality.

In 2016, “post-truth” was chosen as the word of the year by the Oxford Dictionaries. This is a concept that has come to be associated with a type of political discourse in which objective facts are less important than factual inaccuracies which appeal to emotion to influence people’s attitudes. Due to this recent increase in post-truth politics, critical thinking becomes an important skill to master. Yet, studies have suggested that students often lack the necessary skills for critical thinking. One way of approaching this problem is through the reading of literature. This essay specifically argues that George Orwell’s 1984 provides teachers with an excellent opportunity to develop critical thinking skills among upper secondary English as a foreign language (EFL) students, with the novel as an excellent platform to also promote student reflection on current post-truth politics. In order to work with 1984 to foster critical thinking, this essay utilizes a literature-based, pedagogical model developed by Bobkina and Stefanova that draws inspiration from elements of reader-response theory and critical literacy pedagogy (CLP). To show how 1984 can be used to discuss current post-truth politics, a thematic analysis was performed where central themes and concepts from the novel, such as doublethink, Newspeak and telescreens, were compared to current trends in post-truth politics. The analysis itself was structured around the following themes: the distortion of truth for political gains, the use of language as an instrument of political power and the use of technology to spread misinformation. Following the analysis, a lesson project based on Bobkina and Stefanova’s four-stage model was constructed, focusing on different pre-, while- and post-reading activities aimed at making the students develop their critical thinking skills as well as their awareness of the three themes mentioned above. While this approach is deemed suitable for working with 1984 to discuss post-truth politics, a suggestion for further research would be to use Bobkina and Stefanova’s model together with more contemporary dystopian novels in order to discuss other topics that are more relatable to young adults, e.g. identity issues and social stratification.

Purpose The purpose of this study is to explore how the use of clickbait marketing strategies by online news outlets affects consumer perceptions of online news content quality.   Design/methodology/approach A qualitative, deductive, exploratory and cross-sectional method, wherein data was collected through semi-structured interviews to understand consumer perceptions of how the use of clickbait strategies affects perceived online news content quality. The gathered data was then analyzed with the help of a directed content analysis supported by qualitative content analysis software Atlas.Ti. Findings This study found that the use of clickbait content strategies affected the perceived quality of online news content. Furthermore, it was found that there are two primary consumer groups, one of which seeks to avoid clickbait and one of which seems to engage with clickbait. Finally, several new variables were found for perceived news content quality that applied in the online space. Research limitations/implications The main implications of this research are that clickbait strategies should be utilized cautiously as they are often found misleading or displeasing, and may cause damage to the publisher. Furthermore, despite clickbait sometimes being entertaining, most clickbait was found to be highly context sensitive. Finally, a number of new variables were found that expanded previous understandings of perceived news quality. This study was limited by several factors - firstly, a lack of english as a first language which may have resulted in misunderstandings. Secondly, the articles utilized were found to have several shortcomings during the research procedure. Finally, due to the qualitative nature of this study, its findings are non-generalizable.

Syftet med den här masteruppsatsen är att förstå nyhetsdiskurser i public service och invandringskritiska alternativmedier i Sverige. Uppsatsen fokuserar på hur de fyra största politiska partierna behandlar invandringsfrågan månaden före det riksdagsvalet 2018. Uppsatsen syftar mot att förstå skillnader och likheter i diskursiva praktiker om invandringsfrågan i två public service medier (Sveriges Television och Sveriges Radio) samt två invandringskritiska alternativmedier (Fria Tider och Nyheter Idag), och för att undersöka hur detta kan kopplas till minskat förtroende i traditionella medier.Uppsatsen grundar analysen på diskursteori av Fairclough i sammanhanhet av minskat förtroende för traditionella nyhetsmedier. För analys av resultaten används gestaltningteorin och dagordningsteorin, tillsammans med teorier om kommersialiserad journalistik.Metoderna som används är en kritisk diskursanalys baserad på Faircloughs tredimensionella modell, tillsammans med en retorisk analys som appliceras på Faircloughs textnivå. Dessa metoder användes på tolv medietexter som handlade om samma nyhet under augusti 2018.Resultaten visade en Överlägsenhetsdiskurs, där de invandringskritiska medierna intar en roll som överlägnsa de andra deltagarna i det politiska "spelet". Jag presebterar också en erkännandediskurs för att förklara en tredimensionell modell som innehåller politiker i botten, traditionella medier på andra nivån och invandringskritiska medier på toppen. Den sista diskursen är en "Vänskaps"-diskurs där de invandringskritiska medierna intar en "vänskaplig" roll mot andra aktörer i det politiska spelet, vilket ytterligare expanderar på teorier om politisk nyhetsjournalistik som ett spel.Framtida forskning kan riktas mot att bredda analysen och inkludera flera sorters alternativa medier, utföras i andra länder eller utveckla den model jag presenterar. / The aim of this master’s thesis is to understand news discursive practices in public service and the immigration critical alternative media of Sweden. The thesis focus on how the four largest political parties addresses the topic of immigration the month before the Swedish election in 2018. The thesis aims at identifying and understanding similarities and differences in the discursive construction of immigration as a political topic in two public service media (Sveriges Television and Sveriges Radio) and two immigration critical alternative media (Fria Tider and Nyheter Idag), and to examine how this connects to the diminished trust in mainstream media.The thesis bases the analysis on discourse theory by Fairclough, and the context of distrust in traditional news media. To analyze the result, framing and agenda setting theories are used along with the theory of commercialized journalism.The methods used are a Critical Discourse Analysis based on Fairclough’s model supported by a rhetorical analysis which is applied on the text layer in Fairclough’s model. These methods are applied to a total of twelve media texts that covered the same stories during August of 2018.The results showed a superiority discourse where the immigration critical media enters a role of superior to other actors in the political “game”. I also present an acknowledgement discourse to support a three-level model, which consists of politics at the bottom, the mainstream media on the second level, and the alternative media on the top. The final discourse is the “Friendship” discourse where immigration critical media takes on a “friendship” role towards other actors in the political game, to further expand upon political journalism as a game.Future research could be aimed at widening the analysis to cover other forms of alternative media, be placed in other countries, or develop the model I propose.

This thesis focused on disinformation, providing analysis of media system in the Czech Republic. The main focus is on content analysis of disinformations and their persuasive function - explaining what are the ain characteristics of those news and which lingual and rhetorical tools they use. The main tool which is discussed and analysed in this thesis is fallacy, false or invalid arguments. Disinformation phenomenon and its place in today's world is explained in their first part of the thesis. The second, empirical part of the thesis looks at specific examples of disinformations which were published at selected Czech disinformation news websites (Aaronet, AC24, Instory.cz). Selection of those websites was guided by their diversity when it comes to structure and themes. The sample of analysed articles was subject to predefined standards, chosing articles with the theme of immigration crisis which were published between 2015 and 2017.

During the past few years online misinformation, generally referred to as fake news, has been identified as an increasingly dangerous threat. As the spread of misinformation online has increased, fake news detection has become an active line of research. One approach is to use knowledge graphs for the purpose of automated fake news detection. While large scale knowledge graphs are openly available these are rarely up to date, often missing the relevant information needed for the task of fake news detection. Creating new knowledge graphs from online sources is one way to obtain the missing information. However extracting information from unstructured text is far from straightforward. Using Natural Language Processing techniques we developed a pre-processing pipeline for extracting information from text for the purpose of creating knowledge graphs. In order to classify news as fake or not fake with the use of knowledge graphs, these need to be converted into a machine understandable format, called knowledge graph embeddings. These embeddings also allow new information to be inferred or classified based on the already existing information in the knowledge graph. Only one knowledge graph embedding model has previously been used for the purpose of fake news detection while several new models have recently been developed. We compare the performance of three different embedding models, all relying on different fundamental architectures, in the specific context of fake news detection. The models used were the geometric model TransE, the tensor decomposition model ComplEx and the deep learning model ConvKB. The results of this study shows that out of the three models, ConvKB is the best performing. However other aspects than performance need to be considered and as such these results do not necessarily mean that a deep learning approach is the most suitable for real world fake news detection.

Background: It is evident that fake news has become more common since the world transitioned into a more digital age. Previous research on the topic of fake news has usually been done on different generations and often by comparing them to one another in order to highlight differences between age groups. However, there are no studies to be found on Generation X alone in Sweden.  Purpose: The purpose of this study was to examine Swedish Generation X-ers and their relation to news posts found on Facebook, in order to find out how they interact with it and what they would do to make sure a news post is valid before sharing it with their network of contacts. With a focus on exploring their experiences and finding patterns among the different answers we collected, the goal with this thesis was to present an overview of how Swedish Generation X-ers relate to news posts on Facebook.  Method: The research conducted in this paper was done by a set of semi-structured interviews. The transcriptions and data were analyzed by using a thematic approach, which allowed us to find themes and codes in the material, and in turn to be able to draw conclusions.  Findings: The results showed that Swedish Generation X-ers generally tend to avoid interacting with news posts on Facebook. This is because they worry about accidentally sharing fake news, think news is too private to share with others, and do not like the presence that comes with presenting themselves online. However, if they were to share a news post, even hypothetically, the majority of our participants would take measures to ensure validity beforehand. This thesis can be used as a groundwork for similar studies in other countries to get a comparative view. Further research: Suggestions for further research include doing the same kind of research in a new and bigger context, in order to see if the results from this thesis are recurring throughout the population at large. Another suggestion would be to combine a larger qualitative study with an additional quantitative approach, in order to present a more mathematical overview. Lastly, further research could be done to see if Generation X-ers are as private with their political views in real life as they are on Facebook. This in order to see if the behavior is something they have brought from their disconnected life to Facebook, or if it is just an online behavior.

Fake news has long been a part of the news landscape but has in recent times, as the market for online news has grown, become an increasingly occurring problem in contemporary news media and society. By analyzing the content published by the largest American news organizations this thesis provides an overview of the occurrence of fake news and clickbait on the competitive news market. Competition for the readers' limited attention has been known to create a thirst to be first and may lead to the consequences of unethical behavior by the organizations. But what does that mean? Fake news can be categorized as biased information which in turn can be divided into two parts, misinformation and disinformation. Disinformation is the intentional spread of wrong or biased information and in the scope of this thesis a news site’s intention is determined through its use of clickbait. This study also evaluates the fake news and clickbait present in the most popular news articles by readers in order to see if the competition is increasing the use of tools for competitive advantages within the market for news. With this information it is possible to categorize the news article as one of three stages of morality which in turn is used to evaluate to what degree the organization is fulfilling the ethical aspect of their corporate social responsibility. The study finds that all but one of the explored organizations are fulfilling their ethical responsibility.

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.