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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.

In the past decade, a fundamental research topic in the information systems (IS) discipline has been to examine the value of online platforms on businesses, society, and consumers, notably in the form of improved efficiency in information sharing, consumer engagement, and increased sales. However, the risks rooted in online platforms may cannibalize the value created, which have received limited attention in the literature and practice. In my dissertation, I attempt to fill this gap in the literature by providing a comprehensive analysis of the risks of online platforms from the angle of these three main entities in the ecosystem with three separate yet related essays. The first essay focuses on the risks for businesses that leverage social media platforms, and assesses how their posting on social media fan pages affects consumers’ decision to purchase and unfollow from the firms. The second essay focuses on the risks of fake news on social media and how social media platforms may use identity verification to reduce online anonymity and combat this increasingly critical social problem. The third essay focuses on estimating the risks of using monetary incentives to motivate consumers to write online product reviews, and examines how such strategy may affect product sales. Our studies have theoretical and practical implications for designing effective online platform information systems. / Business Administration/Management Information Systems

This thesis presents a qualitative study of fake news disseminated in Poland as a result of the outbreak of the war between Russia and Ukraine in February 2022. It aims to contribute to the public knowledge about the most prominent themes of the Polish fake news and the topics prone to be used as disinformation and as a weapon of war in a conflict setting. Through the use of framing theory, this research explains how the conflict in Ukraine has been framed within the Polish fake news. To understand the tactics of the fake news spreaders, culture links and culture pegs used to attract the Polish audience have been identified. The study was conducted using content analysis and coding of 125 fake news items sourced from six fact-checking organisations in Poland. The findings revealed 6 main themes: socioeconomic, war reporting, politics, commentary, conspiracy theory and ideology. The framing of the conflict has been identified as presenting Ukrainian refugees in Poland as economic migrants, the alleged ‘Ukrainisation’ of Poland, and a narration that the war is a lie. Theoretical approach of interpreting fake news as a weapon of war revealed the pro-Russian narrative in the majority of the fake news in Poland within the analysed timeframe. The study concludes by addressing the need for a continuous research of the topic of fake news to alert the public on the tactics of the fake news spreaders and characteristics of fake news in the Polish infosphere.

Yes / Deepfakes constitute fake content -generally in the form of video clips and other media formats such as images or audio- created using deep learning algorithms. With the rapid development of artificial intelligence (AI) technologies, the deepfake content is becoming more sophisticated, with the developed detection techniques proving to be less effective. So far, most of the detection techniques in the literature are based on AI algorithms and can be considered as passive. This paper presents a proof-of-concept deepfake detection system that detects fake news video clips generated using voice impersonation. In the proposed scheme, digital watermarks are embedded in the audio track of a video using a hybrid speech watermarking technique. This is an active approach for deepfake detection. A standalone software application can perform the detection of robust and fragile watermarks. Simulations are performed to evaluate the embedded watermark's robustness against common signal processing and video integrity attacks. As far as we know, this is one of the first few attempts to use digital watermarking for fake content detection. / EIG CONCERT-Japan call to the project entitled “Detection of fake newS on SocIal MedIa pLAtfoRms” (DISSIMILAR) through grants PCI2020-120689-2 (Ministry of Science and Innovation, Spain) and JPMJSC20C3 (JST SICORP, Japan). In addition, the work of the first two authors was partly funded by the Spanish Government through RTI2018-095094-B-C22 “CONSENT”