7 research outputs found
Detecting Toxicity in News Articles: Application to Bulgarian
Online media aim for reaching ever bigger audience and for attracting ever
longer attention span. This competition creates an environment that rewards
sensational, fake, and toxic news. To help limit their spread and impact, we
propose and develop a news toxicity detector that can recognize various types
of toxic content. While previous research primarily focused on English, here we
target Bulgarian. We created a new dataset by crawling a website that for five
years has been collecting Bulgarian news articles that were manually
categorized into eight toxicity groups. Then we trained a multi-class
classifier with nine categories: eight toxic and one non-toxic. We experimented
with different representations based on ElMo, BERT, and XLM, as well as with a
variety of domain-specific features. Due to the small size of our dataset, we
created a separate model for each feature type, and we ultimately combined
these models into a meta-classifier. The evaluation results show an accuracy of
59.0% and a macro-F1 score of 39.7%, which represent sizable improvements over
the majority-class baseline (Acc=30.3%, macro-F1=5.2%).Comment: Fact-checking, source reliability, political ideology, news media,
Bulgarian, RANLP-2019. arXiv admin note: text overlap with arXiv:1810.0176
Detecting Abusive Language on Online Platforms: A Critical Analysis
Abusive language on online platforms is a major societal problem, often
leading to important societal problems such as the marginalisation of
underrepresented minorities. There are many different forms of abusive language
such as hate speech, profanity, and cyber-bullying, and online platforms seek
to moderate it in order to limit societal harm, to comply with legislation, and
to create a more inclusive environment for their users. Within the field of
Natural Language Processing, researchers have developed different methods for
automatically detecting abusive language, often focusing on specific
subproblems or on narrow communities, as what is considered abusive language
very much differs by context. We argue that there is currently a dichotomy
between what types of abusive language online platforms seek to curb, and what
research efforts there are to automatically detect abusive language. We thus
survey existing methods as well as content moderation policies by online
platforms in this light, and we suggest directions for future work