Search Results for author:
Found 11 papers, 6 papers with code
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech
Although there is an unprecedented effort to provide adequate responses in terms of laws and policies to hate content on social media platforms, dealing with hatred online is still a tough problem.
Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech
Tackling online hatred using informed textual responses - called counter narratives - has been brought under the spotlight recently.
Multilingual Counter Narrative Type Classification
The growing interest in employing counter narratives for hatred intervention brings with it a focus on dataset creation and automation strategies.
Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data
These `cheaper' learning techniques hold significant potential for the social sciences, where development of large labelled training datasets is often a significant practical impediment to the use of machine learning for analytical tasks.
Generating Counter Narratives against Online Hate Speech: Data and Strategies
Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online.
Empowering NGOs in Countering Online Hate Messages
In this paper, we introduce a novel ICT platform that NGO operators can use to monitor and analyze social media data, along with a counter-narrative suggestion tool.
Understanding Counterspeech for Online Harm Mitigation
Counterspeech offers direct rebuttals to hateful speech by challenging perpetrators of hate and showing support to targets of abuse.
DoDo Learning: DOmain-DemOgraphic Transfer in Language Models for Detecting Abuse Targeted at Public Figures
We find that (i) small amounts of diverse data are hugely beneficial to generalisation and model adaptation; (ii) models transfer more easily across demographics but models trained on cross-domain data are more generalisable; (iii) some groups contribute more to generalisability than others; and (iv) dataset similarity is a signal of transferability.
Basque and Spanish Counter Narrative Generation: Data Creation and Evaluation
Being a parallel corpus, also with respect to the original English CONAN, it allows to perform novel research on multilingual and crosslingual automatic generation of CNs.
NLP for Counterspeech against Hate: A Survey and How-To Guide
In recent years, counterspeech has emerged as one of the most promising strategies to fight online hate.
