This thesis studies how did top digital news media channels, CNN, and Fox News, frame news content pertaining to the U.S.-Mexico border crisis of 2019 via social media platform Twitter, making a...Show moreThis thesis studies how did top digital news media channels, CNN, and Fox News, frame news content pertaining to the U.S.-Mexico border crisis of 2019 via social media platform Twitter, making a comparison between the two media channels. Furthermore, this thesis analyzes to what extent, if at all, do the CNN and Fox News portrayals of the topic reflect the rising political polarization in American society. The primary objective of this study is to examine the difference in news framing between news outlets on the aforementioned topic, and understand their role and implications for political polarization in the U.S. This objective will first be addressed through a systematic review of the available literature, aiming to provide a comprehensive insight into the matter from a purely theoretical standpoint, and secondly by means of a comparative case study examining digital news content based on news framing analysis theory. This thesis aims to contribute to the fields of politics, political communications, and mass communications, with a particular focus on the study of news framing.Show less
Electroencephalography (EEG) has been used for over a century to measure electric potentials in the brain. The brain signals can be used in a Brain Computer Interface (BCI) to control machines,...Show moreElectroencephalography (EEG) has been used for over a century to measure electric potentials in the brain. The brain signals can be used in a Brain Computer Interface (BCI) to control machines, like prostheses or exoskeletons. In the last decades, consumer-grade EEG devices have become available. Their performance and reliability, however, does not match that of medical EEG equipment. In this project, the possibilities for classification with such a consumer-grade EEG device, the EMOTIV EPOC+, were explored. The main challenge was to overcome the large noise and nuisances in the EEG signal to be able to classify brain states induced by motoric tasks and sensory stimuli. The effect of several preprocessing techniques was studied: Independent Component Analysis, a denoising autoencoder, and adaptive filtering with an autoencoder. For the classification, various machine learning approaches were proposed to analyse the EEG signals, ranging from a naive Bayes classifier to Deep Convolutional Neural Networks. Furthermore, the effect of motivating the structure of a neural network for this classification task was investigated. It was found that using the spectral features of the EEG signal as a motivation for the structure of neural networks helps classifiers identify brain states that are not classified by other types of neural networks.Show less
