Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction

We present a preliminary study of a knowledge graph created from season one of the television show Veronica Mars, which follows the eponymous young private investigator as she attempts to solve the murder of her best friend Lilly Kane. We discuss various techniques for mining the knowledge graph for clues and potential suspects. We also discuss best practice for collaboratively constructing knowledge graphs from television shows

Attitudes towards the Covid-19 vaccine on Twitter in Norway

The goal of this thesis is to characterize the distribution of attitudes present on Norwegian Twitter concerning the Covid-19 vaccine by implementing methods for text analysis and social media network analysis. The first analysis performed was manually classifying a sample of the dataset into four categories: irrelevant, neutral, vaccine hesitancy and anti-vaccine hesitancy. This sample dataset was used to train a supervised machine learning model, using BoW and SVM, in order to classify the total dataset. Furthermore, two methods for topic modeling were implemented: Latent Dirichlet Allocation and Biterm. Lastly, three main social networks were created: a mentioning-network containing users mention or mentioning in the dataset, a retweet-network containing users retweeted/quoted or retweeting/quoting and a sentiment network only including users classified as vaccine hesitancy and anti-vaccine hesitancy in the sample network. The ten users with highest scores for in-degree, out-degree and betweenness from the retweet network were analyzed to determine sentiment. The main findings are that the methods for topic modeling did not fit expectations and gave limited findings concerning topics in the theme, but topic modeling illustrated the amount of noise in the dataset. The manual classification resulted in approximately 30% vaccine hesitancy, while the trained supervised machine learning model resulted in only 10% vaccine hesitancy. The mentioning-network illustrated that the debate evolved and then stabilized through the autumn/winter of 2020. The most mentioned users were positive towards the vaccine. There was a separation regarding sentiment for the most retweeted and users retweeting most. Users displaying vaccine hesitancy sentiment tended to retweet slightly more than users displaying anti-vaccine hesitancy sentiment, and there were signs of echo chambers.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Modelling and inferring connections in complex networks

Network phenomena are of key importance in the majority of scientific disciplines. They motivate the desire to better understand the implications of interactions between connected entities. In the focus of this thesis are two of the most prominent tasks in the research of such phenomena: the modelling and the inference of connections within networks. In particular, I provide a systematic framework for using the topology and unifying characteristics of networks from fields as diverse as biology, sociology, and economics to predict and validate connections. I build on existing random graph models and node similarity measures, which I then employ in both unsupervised and supervised machine learning approaches. Furthermore, I present novel methods for identifying the statistically significant connections in network settings that involve multiple types of entities and connections — a crucial element of modelling, which most available methods fail to address. To demonstrate the potential of these new tools, I use them to filter networks that were constructed from large-scale noisy data generated by biological experiments as well as records of online social activity. Subsequently, I predict previously unobserved connections within these networks and evaluate the performance of the developed tools based on ground truth data. In further data sets without direct evidence for the connections in the network, a second, bipartite network serves as proxy for the analysis. Specifically, in an e-commerce setting I use connections between products and customers to deduce similarities between the products based on customer behaviour. In an analysis of high-throughput screening data on the other hand, I utilize relations between proteins and experimental conditions to identify potential functional affinities among the proteins. The findings presented here show that the computational prediction of connections can both help researchers gain a better understanding of costly large-scale data and guide further experimental design. The thesis demonstrates the potential of a network analytic approach to modelling and inference on multiple applications, such as the uncovering of possible privacy issues in the context of online social networking platforms and the optimization of drug development in cancer treatment