Social media is becoming a popular disclosure channel with higher speed, reach, and extensive network effects. A negative information role of social media is to increase the spread of information disorder defined as false information or truth presented with the intent to harm. This study investigates whether and how auditors react to information disorder on social media, in the context of clients' merger and acquisition (M&A) rumors. I document that information disorder is positively associated with audit fees but not associated with audit delay and the likelihood of audit resignations. Additionally, increased social media attention can elevate the level of risk associated with rumors. I therefore predict that the associations will be more pronounced with greater social media influence. Using a manually collected Twitter rumor subsample, I find mixed results. This dissertation contributes to the auditing literature by documenting how external auditors incorporate social media-induced information disorder into client retention and pricing decisions.

Denna studie bygger på Research through Design (Frayling, 1993), d.v.s att undersökningen syftar till att generera ny kunskap genom analys av datorspel och design av en gestaltning. För att uppnå detta används ett annoterat portfolio och dess underliggande principer (Gaver, 2012; Bowers, 2012). Det annoterade portfoliot består av fyra utvalda datorspel och en egenutvecklad gestaltning. Detta portfolio analyseras för att definiera vilka gemensamma och icke-gemensamma designegenskaper som kan observeras. Efter detta presenteras argument för varför utvalda egenskaper kan göra pedagogiskt inriktade datorspel mer effektiva i syftet att utbilda kring-eller minska påverkan av mis-/desinformation i det specifika inlärningssammanhanget. Faran med mis-/desinformation i samhället illustreras genom kontexten av arbetet med den utvecklade gestaltningen, som bygger på COVID-19 mis-/desinformation och dess konsekvenser. Resultat uppnås efter analysering av samtliga designexempel har genomförts och data har genererats för att utveckla vår egna gestaltning baserat på denna information. / This case study is based on Research through Design (Frayling, 1993), i.e. the purpose of the study is to generate new knowledge through analysis and design of computer games. To achieve this an annotated portfolio and its underlying principles are used (Gaver, 2012; Bowers, 2012). The annotated portfolio consists of four selected computer games and one designed by the authors. This portfolio is analysed to define what design features are shared or not shared between the games. After this, arguments are presented as to why the specified features can make educational computer games more effective in the purpose of educating about or to reduce susceptibility to mis-/disinformation in the specific learning context. The danger of mis-/disinformation is illustrated through the context of the work with the designed computer game, which is based on COVID-19 mis-/disinformation and its consequences. Results are achieved after analysis of the four selected computer games has been carried out and data has been generated to enable the development of the authors own game.

In today’s increasingly digital society, the rampant creation and spread of misinformation and disinformation poses a critical problem in our lives. As a society, we are suffering heightened panic facing Information Disorder (ID). This is especially true in the context of Indonesia, a highly Internet-penetrated country with low levels of literacy. A prominent solution to the problem at hand is to develop Media and Information Literacy skills (MIL). To gain in-depth understanding of the context and phenomenon at hand, three things need to be explored; conceptions of the problem, conceptions of the solution, and conceptions of possible issues with the solution. This study aims to explore conceptions and experiences of ID, MIL and discussions of possible issues with advancing MIL skills in Indonesia across a variety of stakeholders. This extensive insight will give an opportunity to reflect upon current efforts and help design sustainable and adept solutions. In order to undertake the aim of collecting and analyzing conceptions and experiences of a variety of stakeholders, a theoretical approach of phenomenography is taken using semi-structured qualitative interviews as a method of empirical data collection.  The varied conceptions and experiences of stakeholders paints a picture of how Indonesian society conceives ID, MIL skills and possible issues with the solution. A solution broadly encouraged by stakeholders is for all stakeholders to work together to create comprehensive, context-relevant, and sustainable solutions.

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.