Trovärdighet – en allt viktigare del att upprätthålla för journalister då falska nyheter blir vanligare. På senare tid har begreppet fake news används frekvent, speciellt i nyheter angående amerikanska presidentvalet 2017. Det blir således intressant att undersöka hur journalister arbetar för trovärdighet och hur läsare upplever den. Detta är en kvalitativ studie som undersöker hur journalister arbetar med trovärdighet i flerkanalspublicering på två lokala nyhetsredaktioner. Uppsatsen tar även upp hur journalister arbetar med sina kollegor för att öka trovärdigheten vid flerkanalspublicering. Journalisterna som har intervjuats för uppsatsen har flera års erfarenhet av nyhetspublicering i lokala sammanhang. Uppsatsen undersöker även läsare av de lokala nyhetsredaktionerna som interagerar med journalisterna på sociala medier. Slutsatsen är att journalisterna tar trovärdighet på allvar. Majoriteten av journalisterna anser att nyheter ska innehålla underbyggda fakta, äkthet och ett korrekt språk. Journalisterna arbetar på ett tydligt sätt tillsammans för att skapa en hög trovärdighet men har även olika tillvägagångssätt i hur de arbetar med läsarna på sociala plattformar. Detta kan bekräftas av läsarnas erfarenhet av interaktion med journalisterna, dock är läsarna av åsikten att trovärdighet skapas via närvaro i sociala medier där journalisten är beredd att bemöta sina läsare men även rimliga rubriksättningar är avgörande för trovärdigheten. / Credibility - an increasingly important part to maintain for journalists as fake news becomes more common. Recently, the term fake news has been used frequently, especially in news about the US presidential election in 2017. Thus, it becomes interesting to investigate how journalists work for credibility and how readers perceive it. This is a qualitative study that investigates how journalists work with credibility in multi-channel publishing on two local news releases. The essay also addresses how journalists work with their colleagues to increase the credibility of multi-channel publishing. The journalists interviewed for the paper have several years of experience in publishing news in local contexts. The essay also examines readers of local news editors that interact with journalists on social media. The conclusion is that the journalists take credibility seriously. The majority of journalists believe that news should contain substantiated facts, authenticity and a correct language. The journalists work in a clear way together to create high credibility, but also have different approaches to how they work with readers on social platforms. This can be confirmed by readers' experience of interaction with journalists, but readers believe that credibility is created through presence in social media where journalists are prepared to respond to their readers, but even reasonable rankings are crucial to credibility.

Fake news detection gained an interest in recent years. This made researchers try to findmodels that can classify text in the direction of fake news detection. While new modelsare developed, researchers mostly focus on the accuracy of a model. There is little researchdone in the subject of explainability of Neural Network (NN) models constructed for textclassification and fake news detection. When trying to add a level of explainability to aNeural Network model, allot of different aspects have to be taken under consideration.Text length, pre-processing, and complexity play an important role in achieving successfully classification. Model’s architecture has to be taken under consideration as well. Allthese aspects are analyzed in this thesis. In this work, an analysis of attention weightsis performed to give an insight into NN reasoning about texts. Visualizations are usedto show how 2 models, Bidirectional Long-Short term memory Convolution Neural Network (BIDir-LSTM-CNN), and Bidirectional Encoder Representations from Transformers(BERT), distribute their attentions while training and classifying texts. In addition, statistical data is gathered to deepen the analysis. After the analysis, it is concluded thatexplainability can positively influence the decisions made while constructing a NN modelfor text classification and fake news detection. Although explainability is useful, it is nota definitive answer to the problem. Architects should test, and experiment with differentsolutions, to be successful in effective model construction.

Med deepfake-teknologi kan det skapas manipulerade videor där det produceras falska bilder och ljud som framställs vara verkliga. Deepfake-teknologin förbättras ständigt och det kommer att bli svårare att upptäcka manipulerade videor online. Detta kan innebära att en stor del mediekonsumenter omedvetet exponeras för tekniken när de använder sociala medier. Studiens syfte är att undersöka unga vuxnas medvetenhet, synsätt och påverkan av deepfake videor. Detta eftersom deepfake-teknologin förbättras årligen och problemen med tekniken växer samt kan få negativa konsekvenser i framtiden om den utnyttjas på fel sätt. Insamlingen av det empiriska materialet skedde genom en kvantitativ metod i form av en webbenkät och en kvalitativ metod med tre fokusgrupper. Slutsatsen visade på att det finns ett större antal unga vuxna som inte är medvetna om vad en deepfake video är, dock existerar det en viss oro för deepfake-teknologin och dess utveckling. Det upplevs att det finns risker för framtiden med teknologin i form av hot mot demokratin och politik, spridning av Fake news, video-manipulation samt brist på källkritik. De positiva aspekterna är att tekniken kan användas i sammanhang av humor, inom film- och TV-industrin samt sjukvård. Ytterligare en slutsats är att unga vuxna kommer att vara mer källkritiska till innehåll de exponeras av framöver, dock kommer de med stor sannolikhet ändå att påverkas av deepfake-teknologin i framtiden. / Manipulated videos can be created with deepfake technology, where fake images and sounds are produced and seem to be real. Deepfake technology is constantly improving and it will be more problematic to detect manipulated video online. This may result in a large number of media consumers being unknowingly exposed to deepfake technology while using social media. The purpose of this study is to research young adults' awareness, approach and impact of deepfake videos. The deepfake technology improves annually and more problems occur, which can cause negative consequences in the future if it’s misused. The study is based on a quantitative method in the form of a web survey and a qualitative method with three focus groups. The conclusion shows that there’s a large number of young adults who are not aware of what a deepfake video is, however there’s some concern about deepfake technology and its development. It’s perceived that there can be risks in the future with the technology in terms of threats to democracy and politics, distribution of Fake news, video manipulation and lack of source criticism. The positive aspects are that the technology can be used for entertainment purposes, in the film and television industry also in the healthcare department. Another conclusion is that young adults will be more critical to the content they are exposed to in the future, but likely be affected by deepfake technology either way.

El periodismo siempre ha necesitado de la verificación de información para poder contar y desarrollar las historias que informa. Ahora, con el acceso a las redes sociales y la facilidad con la que la información viaja y es compartida, la necesidad de comprobar lo que sucede, se ha vuelto esencial en las redacciones para no dejarse llevar por los fake news. Esta investigación pretende contribuir a los estudios sobre el empleo del fact checking como estrategia de verificación de información. Además, busca proporcionar información teórica para analizar y explicar la manera en la que el fact checking y su escala de verificación han permitido que este pase a ser una estrategia de verificación de datos utilizada por distintos medios. Mis preguntas de investigación son: ¿Cuáles son las estrategias utilizadas por el fact checking para combatir los fake news? ¿Por qué es importante el fact checking durante las elecciones presidenciales?  y la hipótesis que manejo es la siguiente: En elecciones presidenciales, el volumen de información y la rapidez con la que esta viaja hace difícil su verificación. Es por eso que el fact checking es una estrategia válida para verificar la información durante elecciones presidenciales. Finalmente, he decidido utilizar como caso de estudio las elecciones presidenciales de Perú del año 2016 y México de 2018 porque ambos fueron objeto de verificación en sus respectivos países por los medios Ojo Público en Perú y Animal Político en México. / Journalism has always needed to verify the information it gets to write the histories it reports. Nowadays, the free access to social media and the way information without verification is being shared has made journalists to fact check what they write to stop the spread of fake news. This research aims to contribute to studies on the use of fact checking as an information verification strategy. In addition, it seeks to provide theoretical information to analyze and explain the way fact checking and its verification scale have allowed it to become a information verification strategy used by different media. My research questions are: What are the strategies used by fact checking to stop the spread of fake news? Why is fact checking important during presidential elections? The hypothesis is: in presidential elections, the volume of information and the speed with which it travels makes it difficult to verify. That is why fact checking is a good information verification strategy during presidential elections. Finally, I have decided to use the presidential elections of Peru in 2016 and Mexico in 2018 as a case study because both were verified in their respective countries by the news site: Ojo Publico in Peru and Animal Politico in Mexico. / Trabajo de investigación

In recent years, disinformation circulating the internet and especially social media has become a widespread concern. The urgency of the fake news problem lies in the fact that decisions that are taken on false or misleading information risk impacting democratic processes negatively. This is especially true during a global health crisis when the misinformation in question concerns scientific facts and informs the way people act in society. Focusing on the relational aspect of fake news, new insight and hypothesis generation can be explored with a relatively novel method, social network analysis. This research provides with an example of the method applied to political problems by analysing the misinformation and fact-checking diffusion network on the Italian Twitterverse during the second wave of COVID19. The network shows a tight core of misinformation and a peripheral fact-checking region approximating a spanning tree. Although some levels of polarization are observed, the resulting network shows no evidence of echo chambers that hinder interaction between the misinformation and the fact-checking clusters. Actor-level analysis revealed that the majority of the users interacting in the network are humans and that influential and active users share misinformation only. The findings of this work are presented to show how network analysis can contribute both mitigation strategies in particular and to social and political sciences research in general.

No description available.

No description available.

Desinformation, eller fake news som det kallas i vissa sammanhang, är ett problem som i samband med internet blivit allt större. Vare sig det gäller politiska val, falska påståenden om Covid-19 eller krigspropaganda så lever vi idag i vad vissa kallar för en infodemi. Begreppet syftar på det hav av både sann och falsk information som finns inom den digitala världen idag som det är väldigt lätt att drunkna i (World Health Organisation, 2020). Det ökade hotet har inte bara gjort det mer relevant att forska om desinformation som problem men även för forskning som ämnar att lösa problemet. Det finns flera olika typer av lösningar men maskininlärningsalgoritmer är en av de vanligaste samt det som detta arbete har undersökt (Gradon et al., 2021). Den frågeställning som arbetet har haft som mål att besvara är följande:“Vad är det som gör maskininlärning till ett effektivt skydd när det används för att identifiera desinformation?” Genom en kvalitativ fallstudie har semistrukturerade intervjuer genomförts med målet att besvara frågan mer på djupet och i detalj. Med metoden genererades ett resultat som förhoppningsvis underlättar att avgöra vad som gör maskininlärningsalgoritmer effektiva på att identifiera desinformation. I resultatet framkom det totalt 11 olika delområden där flera kategoriseras som argument till varför det är en effektiv lösning, samt argument som talar för att de inte är det. Det gjordes även fynd i form av förbättringsförslag som skulle kunna öka effektiviteten. Positiva argument som hittades är följande: 1) Maskininlärning är automatiserat, 2) Maskininlärningsalgoritmer arbetar snabbare än människor, 3) Maskininlärning kan agera snabbt mot nya hot, 4) Maskininlärning hittar mönster enklare än vad människor gör, 5) Maskininlärningsalgoritmer blir bättre ju mer de används. Negativa arguments som hittades är: 6) Maskininlärningsalgoritmer kan manipuleras, 7) Maskininlärningsalgoritmer kan missbrukas, 8) Hur processen går till med maskininlärning kan vara otydlig. Förbättringsförslag som hittades är: 9) Manuellövervakning av maskininlärningsalgoritmer, 10) Nya tekniker inom maskininlärning, 11) Att kombinera maskininlärningsalgoritmer med filter.

Fake news has great potential to cause damage to brand reputations and finances. Given the technical challenges of detecting fake news in time, it is inevitable that social media platforms will end up hosting fake news. The competition for attention and advertising revenue is intense. Many consumers read only the headlines. Fake news stories that mention brands in headlines can help news publishers garner social media engagement but can also hurt brands, raising concerns about brand protection. In this research, I focus on the first two stages of the information processing model – attending to information and encoding information (Berk 2018; Miller 1988).In Chapter 2, I investigate whether mentions of human and product brands are associated with news consumption and news retransmission (how brand mentions attract attention; the first stage of information processing). Using data from a news platform that generated both traditional and satirical (fake) news stories, I quantify the effects of brand mentions on social media engagement for both traditional and fake news. The analysis encompasses mentions of popular product brands, such as Apple, and mentions of human brands, such as famous politicians and actors. A framework based on uses and gratifications theory (UGT) aids in variable selection and the interpretation of results. My results imply that human brand mentions generally have a positive effect on news consumption and retransmission for both news formats, and product brand mentions affect engagement of satirical news via an interaction with news categories. Results provide further insights on the roles of sentiment, narrative style, and writing quality of news stories. The high potential of human and product brands in the headlines, especially human brands in satirical news, may be indicative of their potential to be misused by unscrupulous news media publishers. This reminds social media platforms of their responsibility to protect brands and consumers from fake news. Next, in Chapter 3, I examine the effectiveness of before-warnings (BWs) and after-warnings (AWs) in alerting consumers and reducing the persuasive influence of fake news on brand attitudes (how warning timing affects encoding; the second stage of information processing). Results reveal that for both negative and positive fake news, BWs are sometimes no more effective than no-warnings. Although BWs do encourage more critical processing of misinformation, this can distract consumers from the warning message. More importantly, Chapter 3 demonstrates a robust after-warning effect (AWE). Warning consumers after they have read fake news with AWs consistently leads to a higher reduction of persuasive influence (negative or positive) than BWs. AWs are more salient and arouse greater reactance to the false information than BWs. The resulting loss in control over how the news influenced attitudes and increased anger lead to the observed after-warning effect. News valence also matters since positive news is perceived to be more credible and processed less critically than negative news. AWs relative to BWs thus arouse feelings of being tricked when fake news is positive but not when it is negative, also leading to the after-warning effect. The findings have several theoretical and managerial implications. / Business Administration/Marketing

The purpose of the current study was to examine how the media's positive and negative portrayals related to racism impact the viewer's attitudes regarding African Americans. Previous research has shown how misinformation in the media can implicitly affect one's level of racism. Previous research has also shown that gender and one's sociodemographic status can affect the way individuals perceive misinformation. This study aimed to address the relationship between misinformation depicting racist views directed toward African Americans and consumer's attitudes toward African Americans. It was hypothesized that exposure to misinformation will have a significant impact on participants' level of racism. A Series of linear regression analyses were conducted to determine how race, sex, social class, right-wing authoritarianism, religious involvement, political preference, and exposure to real and fake news combined predict the pro-black and anti-black views of participants. Results indicated that exposure to fake news did have a significant negative impact on a pro-black viewpoint. However, the results of the study indicated that real or fake news did not significantly impact anti-black views.