This study examined the relationship between exposure to COVID-19 fake news, right-wing authoritarianism (RWA), sociodemographic factors (i.e., race, biological sex), and xenophobia along with anti-Asian sentiment during the pandemic. Participants included 133 female and male college students. Participants were randomly assigned to one of three conditions: (1) exposed to COVID-19 fake news, (2) exposed to real news related to COVID-19 news, and (3) no news exposure. All participants from three groups were then asked to complete a series of measures regarding their attitudes toward Asian Americans and xenophobia. Participants levels of RWA and sociodemographic variables of race and biological sex were also examined. There was no significant difference in participants' attitudes toward Asian Americans based on which experimental group they were in. Participant's level of RWA and xenophobia significantly predicted participants attitudes toward Asian Americans. Participants race marginally predicted levels of comfort and kinship with, as well as enthusiasm for Asian Americans. Implications and directions for future research are discussed.

The scarce internet access in Ethiopia puts heavy weight on traditional media and people to spread news and information. By testing if the marketing strategy Word of Mouth is applicable on informative content, rather than just brands and products, this thesis explore the motivation to spread news as well as how it is received by a non-internet using group. As with brands, a common trigger for WOM was the subject being brought up in a discussion, both offline and online. Conditions that increased WOM in marketing such as being sociable and feeling a responsibility also increased WOM for more political content. The study also found that there is a low trust for internet as a source, but a high trust for the word of many. If the message was heard multiple times it was more believable, even though a primary source was lacking. In general both internet users and non-internet users were actively spreading information with the reason that it needed to be spread, something that was concluded as a collectivist action where information is spread quickly through social ties. Non-internet users were considered to be extra fragile and exposed to fake news due to the unequal distribution of information and technology. Since trust was based on the message of many, echo chambers and confirmation bias is discussed, as well as how Ethiopia might tackle the segregation of technology in the country in order to decrease inequality in the future. / Den svaga tillgången till internetuppkoppling i Etiopien lägger ett stort ansvar på traditionell media, och människor, för att sprida nyheter och information till landets stora befolkning. Genom att testa om marknadsföringsstrategin Word of Mouth också är applicerbar på informativa budskap undersöker denna uppsats motivationen bakom att sprida nyheter, samt hur denna mottas av en grupp som inte använder internet. Liksom med varumärken var en vanlig trigger för WOM med informativ kontext att ämnet nämndes i en pågående diskussion, både online och offline. Förutsättningar som stärkte WOM vid marknadsföring, såsom att vara social och känna ett samhällsansvar ökade också WOM för nyheter. Studien fann också att det var låg tillit för internet som källa, men att det fanns stor tilltro till information som upprepades av olika personer. Ett budskap som hördes från flera olika var mer trovärdigt, oavsett vilken den primära källan var eller om den saknades helt. Generellt spred både internetanvändare och icke-användarna information av anledningen att det behövdes spridas och höras av alla invånare, vilket tolkades som en kollektivistisk handling där nyheter snabbt spreds i sociala nätverk. De som inte använder internet ansågs vara extra exponerade för fake news på grund av den ojämställda distributionen av information och tillgången till teknologi. Då tillit var baserat på upprepning från många diskuterar uppsatsen även echo chambers och confirmation bias, samt hur Etiopien i framtiden kan tackla den tekniska segregationen.

På senare år har dokumentärfilmsgenren hamnat i skottlinjen för en kritik som riktar sig mot sanningsrelativism och falska nyheter. Den här uppsatsen undersöker hur svenska dokumentärfilmare förhåller sig till de krav på äkthet och transparens som förväntas av dem, och i vilken mån dessa krav avspeglar sig i filmarnas arbete. Intervjuer med fyra svenska dokumentärfilmare ligger till grund för uppsatsen och frågorna som ställts står i direkt koppling till den debatt som florerat de senaste åren kring huruvida dokumentära berättelser i likhet med journalistik och nyheter riskerar att bli sanningsrelativa. Forskning visar på ett samband mellan dagens medieklimat och urvattnandet av sanningsbegreppet genom framväxten av exempelvis trollsidor och ”fake news”, vilket delvis också förändrat samtalet om sant och falskt på den dokumentära arenan. Resultatet av undersökningen visar att filmarna tagit intryck av den förändrade diskurs som på senare år växt fram kring dokumentärfilmens autenticitetskrav, men att detta inte förändrat deras sätt att närma sig sitt material och hur de ser på sin yrkesroll. Däremot finns en stor medvetenhet kring vikten av en öppen dialog, transparens kring den kreativa processen och värdet i en informerad publik.

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China is accused of conducting disinformation campaigns on Taiwan's social media. Existing studies on foreign interventions in democratic societies predict that such disinformation campaigns should lead to increasing partisan polarization within Taiwan. We argue that a backlash effect, making Taiwan's citizens more united against China, is equally plausible. We conduct a survey experiment exposing participants to a real-life rumour and rebuttal to test these competing hypotheses. We find, at best, mixed evidence for polarization. Although neither rumour nor rebuttal mention China, there is consistent evidence of backlash against China. Most notably, participants across the political spectrum are more inclined to support Taiwanese independence after viewing the rumour rebuttal. These findings indicate that citizens may put aside partisanship when confronted with false news that is plausibly linked to an external actor. We conclude by discussing the broader applicability of our theory and implications for cross-Strait relations.

This work examined the relative effectiveness of multilayer perceptron, random forest, and multinomial naïve Bayes classifiers, trained using bag of words and term frequency-inverse dense frequency transformations of documents in the Fake News Corpus and Fake and Real News Dataset. The goal of this work was to help meet the formidable challenges posed by proliferation of fake news to society, including the erosion of public trust, disruption of social harmony, and endangerment of lives. This training included the use of context-categorized fake news in an effort to enhance the tools’ effectiveness. It was found that term frequency-inverse dense frequency provided more accurate results than bag of words across all evaluation metrics for identifying fake news instances, and that the Fake News Corpus provided much higher result metrics than the Fake and Real News Dataset. In comparison to state-of-the-art methods the models performed as expected.

This study inspected three linguistic features, specifically the percentage of nouns per sentence, the percentage of verbs per sentence, as well as the mean of dependency distance of the sentence, and observed their respective influence on the fake news classification accuracy. In comparison to the previous studies where linguistic features are combined as a set to be leveraged, this study attempted to untangle the effective individual features from the previously proposed optimal sets. In order to keep the influence of each individual feature independent from the other inspected features, the other feature is held constant in the experiments of observing each target feature. The FEVER dataset is utilized in this study, and the study incorporates the weighted random baselines and Macro F1 scores to mitigate the probable bias caused by the imbalanced distribution of labels in the dataset. GPT-2 and DistilGPT2 models are both fine-tuned to measure the performance gap between the models with different numbers of parameters. The experiment results indicate that the fake news classification accuracy and the features are not always correlated as hypothesized. Nevertheless, having attended to the challenges and limitations imposed by the dataset, this study has paved the way for future studies with similar research purposes. Future works are encouraged to extend the scope and include more linguistic features for the inspection, to eventually achieve more effective fake news classification that leverages only the most relevant features.

The great advance of communication technology comes with a rapid increase of disinformation in many kinds and shapes; manipulated images are one of the primary examples of disinformation that can affect many users. Such activity can severely impact public behavior, attitude, and belief or sway the viewers' perception in any malicious or benign direction. Additionally, adversarial attacks targeting deep learning models pose a severe risk to computer vision applications. This dissertation explores ways of detecting and resisting manipulated or adversarial attack images. The first contribution evaluates perceptual hashing (pHash) algorithms for detecting image manipulation on social media platforms like Facebook and Twitter. The study demonstrates the differences in image processing between the two platforms and proposes a new approach to find the optimal detection threshold for each algorithm. The next contribution develops a new pHash authentication to detect fake imagery on social media networks, using a self-supervised learning framework and contrastive loss. In addition, a fake image sample generator is developed to cover three major image manipulating operations (copy-move, splicing, removal). The proposed authentication technique outperforms the state-of-the-art pHash methods. The third contribution addresses the challenges of adversarial attacks to deep learning models. A new adversarial-aware deep learning system is proposed using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. The proposed approach outperforms current state-of-the-art adversarial defense systems. Finally, the fourth contribution fuses big data from Extra-Military resources to support military decision-making. The study proposes a workflow, reviews data availability, security, privacy, and integrity challenges, and suggests solutions. A demonstration of the proposed image authentication is introduced to prevent wrong decisions and increase integrity. Overall, the dissertation provides practical solutions for detecting manipulated and adversarial attack images and integrates our proposed solutions in supporting military decision-making workflow.

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Studien belyser dagens spridning av falsk information på sociala medier och vad som händer när sådan information dementeras av användare på sociala medier. Denna uppsats är en fallstudie och undersöker vad reaktionerna blir från Facebook-användare när en publicerings osanning belyses i kommentarsfältet. En netnografisk metod har tillämpats för att samla in data. Kommentarer vid tre olika fall där falska publiceringar har spridits har undersökts. Kommentarer har samlats in från det sociala nätverket Facebook för analys. Studien konkluderar att kommentarer som belyser falsk information genererar en förhållandevis liten mängd interaktion. Samtidigt får dessa kommentarer i högre grad positiv respons än negativ. / This essay highlights the current distribution of false information on social media and what happens when users on social media dismisses such information. This essay is a case study and investigates what the reactions are from Facebook users when a published falsehood is highlighted in the comment section. A netnographic method has been used to collect data. Comments on three different cases where false information have been spread have been investigated. Comments have been collected from online social community Facebook for analysis. The study concludes that comments that highlight false information generate a relatively small amount of interaction. At the same time, these comments are more often positive than negative.