Detecting harmful content on online platforms: what platforms need vs. where research efforts go
The proliferation of harmful content on online platforms is a major societal problem, which
comes in many different forms, including hate speech, offensive language, bullying and …
comes in many different forms, including hate speech, offensive language, bullying and …
ConvFiT: Conversational fine-tuning of pretrained language models
Transformer-based language models (LMs) pretrained on large text collections are proven
to store a wealth of semantic knowledge. However, 1) they are not effective as sentence …
to store a wealth of semantic knowledge. However, 1) they are not effective as sentence …
Explainable abuse detection as intent classification and slot filling
To proactively offer social media users a safe online experience, there is a need for systems
that can detect harmful posts and promptly alert platform moderators. In order to guarantee …
that can detect harmful posts and promptly alert platform moderators. In order to guarantee …
How does counterfactually augmented data impact models for social computing constructs?
As NLP models are increasingly deployed in socially situated settings such as online
abusive content detection, it is crucial to ensure that these models are robust. One way of …
abusive content detection, it is crucial to ensure that these models are robust. One way of …
Unsupervised domain adaptation for hate speech detection using a data augmentation approach
Online harassment in the form of hate speech has been on the rise in recent years.
Addressing the issue requires a combination of content moderation by people, aided by …
Addressing the issue requires a combination of content moderation by people, aided by …
Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data
L Castro-Gonzalez, YL Chung, HR Kirk… - arXiv preprint arXiv …, 2024 - arxiv.org
The field of machine learning has recently made significant progress in reducing the
requirements for labelled training data when building new models. Thesecheaper'learning …
requirements for labelled training data when building new models. Thesecheaper'learning …
Half-Day Tutorial on Combating Online Hate Speech: The Role of Content, Networks, Psychology, User Behavior, etc.
While the rise in popularity of social media is seen as a hugely positive development, it is
also accompanied by a proliferation of hate speech, which has recently become a major …
also accompanied by a proliferation of hate speech, which has recently become a major …
Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems
End-to-end (E2E) task-oriented dialogue (ToD) systems are prone to fall into the so-called"
likelihood trap", resulting in generated responses which are dull, repetitive, and often …
likelihood trap", resulting in generated responses which are dull, repetitive, and often …
Transferring knowledge via neighborhood-aware optimal transport for low-resource hate speech detection
The concerning rise of hateful content on online platforms has increased the attention
towards automatic hate speech detection, commonly formulated as a supervised …
towards automatic hate speech detection, commonly formulated as a supervised …
[PDF][PDF] Identifying, characterizing, and mitigating errors in the automated measurement of social constructs from text data
I Sen - 2024 - publications.rwth-aachen.de
As the internet has become a fixture in the lives of people around the world, the traces they
leave on it through visits to websites and social media activity represent a new forge of data …
leave on it through visits to websites and social media activity represent a new forge of data …