Canada

As pointed out by several scholars, current research on hate speech (HS) recognition is characterized by unsystematic data creation strategies and diverging annotation schemata. Subsequently, supervised-learning models tend to generalize poorly to datasets they were not trained on, and the performance of the models trained on datasets labeled using different HS taxonomies cannot be compared. To ease this problem, we propose applying extremely weak supervision that only relies on the class name rather than on class samples from the annotated data. We demonstrate the effectiveness of a state-of-the-art weakly-supervised text classification model in various in-dataset and cross-dataset settings. Furthermore, we conduct an in-depth quantitative and qualitative analysis of the source of poor generalizability of HS classification models.

ACL 2023

Towards Weakly-Supervised Hate Speech Classification Across Datasets

hate speech classification

weak supervision

domain adaptation

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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workshop paper

The ground truth in classification tasks is often approximated by the fraction of annotators who classified an item as belonging to the positive class. Instances for which this fraction is equal to or above 50% are considered positive, including however ones that receive polarized opinions. This is a problematic encoding convention that disregards the potentially polarized nature of opinions and which is often employed to estimate abusive language. We present the distance from unimodality (DFU), a measure that estimates the extent of polarization on the distribution of opinions and which correlates well with human judgment. By applying DFU to posts crowd-annotated for toxicity, we found that polarized opinions are more likely by annotators originating from different countries. Also, we show that DFU can be exploited as an objective function to train models to predict whether a post will provoke polarized opinions in the future.

Distance from Unimodality: Assessing Polarization in Abusive Language

Hate speech detection faces two significant challenges: 1) the limited availability of labeled data and 2) the high variability of hate speech across different contexts and languages. Prompting brings a ray of hope to these challenges. It allows injecting a model with task-specific knowledge without relying on labeled data. 
This paper explores zero-shot learning with prompting for hate speech detection. We investigate how well zero-shot learning can detect hate speech in 3 languages with limited labeled data. We experiment with various large language models and verbalizers on 8 benchmark datasets. Our findings highlight the impact of prompt selection on the results. They also suggest that prompting, specifically with recent large language models, can achieve performance comparable to and surpass fine-tuned models, making it a promising alternative for under-resourced languages. 
Our findings highlight the potential of prompting for hate speech detection and show how both the prompt and the model have a significant impact on achieving more accurate predictions in this task.

Respectful or Toxic? Using Zero-Shot Learning with Language Models to Detect Hate Speech

We present an extensive evaluation of different fine-tuned models to detect instances of offensive and abusive language in Dutch across three benchmarks: a standard held-out test, a task- agnostic functional benchmark, and a dynamic test set. We also investigate the use of data cartography to identify high quality training data. Our results show a relatively good quality of the manually annotated data used to train the models while highlighting some critical weakness. We have also found a good portability of trained models along the same language phenomena. As for the data cartography, we have found a positive impact only on the functional benchmark and when selecting data per annotated dimension rather than using the entire training material.

Benchmarking Socially Unacceptable Language in Dutch

We draw from the framework of relationality as a pathway for modeling social relations to address gaps in text classification, generally, and offensive language classification, specifically. We use minoritized language, such as queer speech, to motivate a need for understanding and modeling social relations-- both among individuals and among their social communities. We then point to socio-ethical style as a research area for inferring and measuring social relations as well as propose additional questions to structure future research on operationalizing social context.



A Relational Approach to Offensive Speech: A Research Agenda

The prevalence of abusive language on different online platforms has been a major concern that raises the need for automated cross-platform abusive language detection. However, prior works focus on concatenating data from multiple platforms, inherently adopting Empirical Risk Minimization (ERM) method. In this work, we address this challenge from the perspective of domain generalization objective. We design SCL-Fish, a supervised contrastive learning integrated meta-learning algorithm to detect abusive language on unseen platforms. Our experimental analysis shows that SCL-Fish achieves better performance over ERM and the existing state-of-the-art models. We also show that SCL-Fish is data-efficient and achieves comparable performance with the large-scale pre-trained models upon finetuning for the abusive language detection task.

Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning

While there has been increasing attention paid to the potential harms perpetuated by online platforms, most academic work on the subject centers on one narrow context: Western communities in primarily English language settings. Yet, social media platforms like YouTube support users globally and provide content in
several languages, including low-resourced languages. In this study, we investigate this context via a mixed methods approach: collecting and analysing search and recommendation data from YouTube in low-resource language settings and conducting semi-structured interviews with YouTube users who speak low-resourced languages in Ethiopia. Our early findings indicate the failure of current content moderation schemes for low-resource languages and the further infliction and distribution of harm to marginalized communities through recommendation systems.

Auditing YouTube Content Moderation in Low Resource Language Settings

We introduce ExtremeBB, a textual database of over 53.5M posts made by 38.5k users on 12 extremist bulletin board forums promoting online hate, harassment, the manosphere and other forms of extremism. It enables large-scale analyses of qualitative and quantitative historical trends going back two decades: measuring hate speech and toxicity; tracing the evolution of different strands of extremist ideology; tracking the relationships between online subcultures, extremist behaviours, and real-world violence; and monitoring extremist communities in near real time. This can shed light not only on the spread of problematic ideologies but also on the effectiveness of interventions. ExtremeBB comes with a robust ethical data-sharing regime that allows us to share data with academics worldwide. Since 2020, access has been granted to 49 licensees in 16 research groups from 12 institutions.

ExtremeBB: A Database for Large-Scale Research into Online Hate, Harassment, the Manosphere and Extremism

While many types of hate speech and online toxicity have been the focus of extensive research in NLP, toxic language stigmatizing poor people has been mostly disregarded. Yet, aporophobia, a social bias against the poor, is a common phenomenon online, which can be psychologically damaging as well as hindering poverty reduction policy measures. We demonstrate that aporophobic attitudes are indeed present in social media and argue that the existing NLP datasets and models are inadequate to effectively address this problem. Efforts toward designing specialized resources and novel socio-technical mechanisms for confronting aporophobia are needed.

Aporophobia: An Overlooked Type of Toxic Language Targeting the Poor

In this paper, we introduce a fine-tuned transformer-based model focused on problematic webpage classification to identify web pages promoting hate and violence of various forms. Due to the unavailability of labelled problematic webpage data, first we propose a novel webpage data collection strategy which leverages well-studied short-text hate speech datasets. We have introduced a custom GPT-4 few-shot prompt annotation scheme taking various webpage features to label the prohibitively expensive webpage annotation task. The resulting annotated data is used to build our problematic webpage classification model. We report the accuracy (87.6% F1-score) of our webpage classification model and conduct a detailed comparison of it against other state-of-the-arthate speech classification model on problematic webpage identification task. Finally, we have showcased the importance of various webpage features in identifying a problematic webpage.

Problematic Webpage Identification: A Trilogy of Hatespeech, Search Engines and GPT

Classifiers tend to learn a false causal relationship between an over-represented concept and a label, which can result in over-reliance on the concept and compromised classification accuracy. It is imperative to have methods in place that can compare different models and identify over-reliances on specific concepts. We consider three well-known abusive language classifiers trained on large English datasets and focus on the concept of negative emotions, which is an important signal but should not be learned as a sufficient feature for the label of abuse. Motivated by the definition of global sufficiency, we first examine the unwanted dependencies learned by the classifiers by assessing their accuracy on a challenge set across all decision thresholds. Further, recognizing that a challenge set might not always be available, we introduce concept-based explanation metrics to assess the influence of the concept on the labels. These explanations allow us to compare classifiers regarding the degree of false global sufficiency they have learned between a concept and a label.

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Distance from Unimodality: Assessing Polarization in Abusive Language

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Robust Hate Speech Detection: A Cross-Dataset Empirical Comparison