Discussing the Value of Automatic Hate Speech Detection in Online Debates

Köffer Sebastian, Riehle Dennis M, Höhenberger Steffen, Becker Jörg


Zusammenfassung
This study discusses the potential value of automatic analytics of German texts to detect hate speech. In the course of a preliminary study, we collected a dataset of user comments on news articles, focused on the refugee crisis in 2015/16. A crowdsourcing approach was used to label a subset of the data as hateful and non-hateful to be used as training and evaluation data. Furthermore, a vocabulary was created containing the words that are indicating hate and no hate. The best performing combination of feature groups was a Word2Vec approach and Extended 2-grams. Our study builds upon previous research for English texts and demonstrates its transferability to German. The paper discusses the results with respect to the potential for media organizations and considerations about moderation techniques and algorithmic transparency.

Schlüsselwörter
Natural Language Processing (NLP); Hate Speech; Text Analytics



Publikationstyp
Aufsatz (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2018

Konferenz
Multikonferenz Wirtschaftsinformatik (MKWI 2018)

Konferenzort
Leuphana, Germany

Buchtitel
Multikonferenz Wirtschaftsinformatik 2018: Data Driven X - Turning Data in Value

Herausgeber
Drews Paul; Burkhardt Funk; Niemeyer Peter; Xie Lin

Seiten
83-94

ISBN
978-3-935786-72-0

Gesamter Text