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AUC Undergraduate Journal of Liberal Arts & Sciences Capstone Issue Vol. 8 2017 1 AUC Undergraduate Journal of Liberal Arts & Sciences Capstone Issue Vol. 8 2017 Published by AUC Undergraduate Journal of Liberal Arts & Sciences Capstone Issue Vol. 6 2017, published by InPrint The AUC Undergraduate Journal of Liberal Arts and Sciences is a biannual, interdisciplinary publication showcasing out- standing undergraduate academic papers. The Journal aims to demonstrate the strength of undergraduate scholarship at AUC, to reflect the intellectual diversity of its academic programme, to encourage best research and writing practices, to facilitate collaboration between students and faculty across the curriculum, and to provide students with opportunities to gain experience in academic reviewing, editing and publishing. The Editorial of the Journal is constituted of members of the InPrint board, a registered AUCSA committee. Editorial board (InPrint) Philip Hartout Editor-in-Chief Stefanie Saddey Head Editor Social Sciences Lyanne van der Plank Head Editor Humanities Christine Moene Head Editor Sciences & Secretary Aisha Erenstein Editor Social Sciences Linnea Sinharoy Editor Social Sciences Catherine Andersen Editor Humanities Arent Boon Editor Humanities Lanie Preston Editor Sciences Steinar Laenen Editor Sciences & Treasurer Capstone coordinators Dr. Bob Kardolus Dr. Maurits de Klepper 2016 Capstone Awards Committee Dr. Jan Pieter van der Schaar Dr. Sennay Ghebreab Prof. Dr. Henk Scholten Dr. Vessela Chakarova Prof. Dr. Louis Akkermans Dr. Rebecca Lindner Dr. Francesca Scott Dr. Niels van Manen 2017 Capstone Awards Committee Dr. Mariëtte Willemsen Dr. Michiel van Drunen Dr. Rob Wüst Dr. Daniel Pinéu Dr. Roland Luttens Dr. Michael McAssey Dr. Erinç Salor Series Editing Prof. Dr. Murray Pratt Dr. Belinda Stratton i Copyright: All texts are published here with the full consent of their authors. Every effort has been made to contact the rightful owners of all content with regards to copyrights and permissions. We apologise for any inadvertent errors or omissions. If you wish to use any content please contact the copyright holder directly. For any queries regarding copyright please contact [email protected]. Foreword In the semester prior to graduation, every AUC student is invited to carry out an independent research project within their intended majors – Sciences, Social Sciences and Human- ities –, referred to as the Capstone. This project is meant to have students engage with the current academic dialogue within their fields. This year’s Capstone Issue, including the- ses from the classes of 2016 and 2017, not only illustrates outstanding pieces of scholarship written by graduated AUC students, but also showcases AUC students’ ability to go be- yond disciplinary boundaries with skill, insight, and creativity. That is why all Capstones in this issue were awarded ‘Thesis of High Distinction’ or ‘Thesis of Highest Distinction’ prizes by the Capstone Awards Committee. Additionally, all Capstones published in this issue have undergone a meticulous editing process carried out by the Editorial Board of InPrint to further improve clarity and con- ciseness. A systematic approach to editing was made possi- ble thanks to the tools provided by lecturer and linguist Dr. Lotte Tavecchio. Capstone coordinator Dr. Maurits de Klep- per communicated to us the selected Capstones. Dean Prof. Dr. Murray Pratt along with Managing Director Dr. Belinda Stratton provided the Editorial Board with council regarding the format of the publication. Finally, a revised layout was made possible with the community-driven LATEX 2ε typeset- ting system, mirroring AUC values. Together, these integrative works reflect the ambition of graduating AUC students and the guidance offered by AUC faculty members and supervisors, allowing for an academic appreciation of the AUC community. Philip Hartout, on behalf of InPrint ii Contents 2017 Sciences Automated Controversy Prediction of Online News Articles from The Guardian – Bas Straathof (1-24) Social Sciences The Adult Child Soldier: Critical Reflections on the ICC’s Case Against Dominic Ongwen and the Moral Development of Child Soldiers – Nini Pieters (25-46) Humanities Fighting Oppression on Their Own Terms: South Africa’s Black Women Take the Lead in Student Protests – Ruta Butkute (47-71) 2016 Sciences Simulating Cascading Failure in the Charging Infrastructure of Electric Vehicles – A Novel Approach for Improving Charging Station Deployment – Marieke Glombek (72-86) Social Sciences An analysis of the consequences that methods currently recommended for NGDO adver- tising have on viewers’ perception of poverty and development in the global south – Carolin Vahar-Matiar (87-112) Humanities Crowdfunding or crowdfinding? The Role and Value of Crowdfunding from the Perspec- tive of Visual Artists and Institutions in the Netherlands – Marlies Augustijn (113-145) Appendices The authors’ appendices can be found using this link: https://www.dropbox.com/s/ulq72cxm9z9ziki/capstone-issue-vol.-8-2017-supplementary-materials.pdf? dl=0. iii Sciences Automated Controversy Prediction of Online News Articles from The Guardian Supervisor Author Dr. Kaspar Beelen (UvA) Bas T. Straathof (AUC) Reader Dr. Evangelos Kanoulas 1 May 31, 2017 Capstone Issue Vol. 8, 2017 Bas Straathof Abstract During the last decade, the amount of available news resources has surged due to the emergence of the Internet and the rise of social media. Some have suggested that information retrieval via online search engines and the personalization of social media news feeds lead to so-called echo chambers in which Internet users receive polarized, one-sided information that conforms to their pre-existing beliefs and ideologies. The implementation of automatic controversy detectors in browser extensions and search engines on the Web can raise awareness about the contentiousness of news stories, evoke critical literacy, and stimulate versatile news diets. This study introduces supervised classifiers capable of binary news article controversy prediction. These classifiers are trained on a dataset of 4,847 online news articles from The Guardian, which have been assigned a binary controversy label based on the results of a crowdsourc- ing survey. Separate classifiers are trained and tested on three subsets of this dataset to provide insight into the effectiveness of employing features extracted from Facebook, Twitter and the articles’ comment sections. A feature space including metadata, discussion, linguistic, sentiment, lexical and social media features is engineered in this study, as well as an optimized TF-IDF baseline feature space. The findings of this study show that features extracted from readers’ discussions around the newspaper articles is essen- tial for the performance of the classifiers. Moreover, this study demonstrates that incorporating Facebook features positively contributes to the performance of the classifiers. Both Support Vector Machine (SVM) and Random Forest (RF) classifiers trained and tested with optimized feature subspaces, consistently ex- hibit F1 score improvements over their counterpart classifiers applied to TF-IDF baselines. The highest achieved F1 score on the testing set of the complete dataset is 76.6. 1 Introduction selective exposure, massively shared news arti- cles on Facebook are flagged as disputed if verified News media cover many controversial subjects; by a collective of independent fact-checkers (Hunt that is to say, issues that provoke drastically vary- 2017; Weedon et al. 2017). Researchers strongly ing opinions among readers. The content and prop- advocate the idea of automating the processes of agation of the news used to be dictated and domi- combating false news, dismantling echo chambers, nated by newspapers, but presently Web 2.0 web- stimulating critical literacy, and search diversifica- sites such as Facebook, Twitter and Wikipedia en- tion; for example, through the implementation of able Internet users to actively participate in the controversy detection algorithms in search engines creation and distribution of news (Brandes 2008). or browser widgets (Aktolga and Allan 2013; Dori- The fact that people have a tendency to access and Hacohen et al. 2016; Ennals et al. 2010; Kacimi and share news that fits their personal beliefs and ideas Gamper 2011; Munson et al. 2013; Vydiswaran et (An 2014), combined with the incorporation of per- al. 2012; Yom-Tov et al. 2014). sonalization algorithms in social media news feeds To contribute to the growing body of scientific and search engines (Beam 2014), increases selec- research on automatic controversy detection, this tive exposure and confirmation bias (Dvir-Gvirsman study develops a supervised controversy predictor et al. 2016; Lazarsfeld et al. 1944; Nickerson trained on online news articles that appeared in the 1998). This can create "echo chambers" that rever- international edition of The Guardian. A dataset of berate and amplify the reader’s pre-existing ide- 4,847 news articles is used, which have been as- ologies and opinions, resulting in certainsides of signed binary controversy labels corresponding to multifaceted issues to be underrepresented (Dori- crowdsourcing annotations to all the articles. The Hacohen et al. 2016; Pariser 2011; Resnick et news articles in the dataset are accompanied by al. 2013). For example, an echo chamber can their associated comment sections and metadata, form when a reader receives news without being in- from which features are extracted to train various