Language:
English
Pages:
1 Online-Ressource (xi, 135 Seiten, 3240 KB)
,
Illustrationen, Diagramme
Dissertation note:
Dissertation Universität Potsdam 2020
DDC:
303.483
Keywords:
Hochschulschrift
;
Hochschulschrift
Abstract:
Comment sections of online news platforms are an essential space to express opinions and discuss political topics. However, the misuse by spammers, haters, and trolls raises doubts about whether the benefits justify the costs of the time-consuming content moderation. As a consequence, many platforms limited or even shut down comment sections completely. In this thesis, we present deep learning approaches for comment classification, recommendation, and prediction to foster respectful and engaging online discussions. The main focus is on two kinds of comments: toxic comments, which make readers leave a discussion, and engaging comments, which make readers join a discussion. First, we discourage and remove toxic comments, e.g., insults or threats. To this end, we present a semi-automatic comment moderation process, which is based on fine-grained text classification models and supports moderators. Our experiments demonstrate that data augmentation, transfer learning, and ensemble learning allow training robust classifiers even on small ...
DOI:
10.25932/publishup-48922
URN:
urn:nbn:de:kobv:517-opus4-489222
URL:
https://d-nb.info/1225792584/34
URL:
https://d-nb.info/1225792584/34