Combating Gendered Abuse in Indic Language Online Spaces: An Ensemble Approach for Effective Detection

Core Concepts

The authors developed an ensemble CNN-BiLSTM model that effectively captures semantic and sequential patterns in textual data to detect gendered abuse in Hindi, Tamil, and Indian English online content.

Abstract

The paper presents the authors' approach and results for the ICON2023 shared task on identifying gendered abuse in online content across three Indic languages - Hindi, Tamil, and Indian English.
The key highlights are:

The authors used an ensemble model combining Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) architectures to capture localized textual features and long-range dependencies in the data.

The CNN layers extracted patterns indicative of abusive language, while the BiLSTM layers analyzed the sequence for context-based offensiveness.

The models were trained using pre-trained FastText and GloVe word embeddings for the respective languages, along with a 5-fold cross-validation strategy.

The authors also leveraged external datasets like MACD and MULTILATE to improve the models' performance through transfer learning in one of the subtasks.

The ensemble models achieved strong performance, ranking 1st in the competition, with the English model scoring an F1-measure of 0.84 on the validation set.

The analysis revealed the impact of factors like embedding techniques and input preprocessing on the models' capabilities to handle noisy, code-switched text effectively.

The authors open-sourced the datasets and model code to enable further research towards mitigating gendered cyber harassment.

Stats

The dataset consists of 7638 posts in English, 7714 in Hindi, and 7914 in Tamil, with each post annotated for three labels related to gendered abuse and explicit language.
The authors used the MACD dataset (33k Hindi, 30k Tamil) and the MULTILATE dataset (English) as external resources for transfer learning in one of the subtasks.

Quotes

"Our ensemble models using CNN-BiLSTMs and contextual embeddings like FastText proved effective, achieving top ranks on the leaderboard across multiple languages."
"The models could capture nuanced abusive language through localized feature learning and sequence modelling."

Key Insights Distilled From

Breaking the Silence Detecting and Mitigating Gendered Abuse in Hindi, Tamil, and Indian English Online Spaces

by Advaitha Vet... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.02013.pdf

Deeper Inquiries

How can the proposed ensemble approach be extended to incorporate multimodal data (text, images, videos) for a more comprehensive detection of gendered abuse?

Incorporating multimodal data, such as text, images, and videos, into the proposed ensemble approach can significantly enhance the detection of gendered abuse in online content. To extend the ensemble approach to handle multimodal data, a few key steps can be taken:

Data Fusion: The first step would involve integrating different modalities of data into a unified representation. This can be achieved by developing a fusion mechanism that combines the features extracted from text, images, and videos.

Multimodal Feature Extraction: Each modality requires specific feature extraction techniques. For text data, natural language processing (NLP) methods can be used, while images and videos may require computer vision and deep learning techniques to extract relevant features.

Multimodal Model Architecture: Designing a model architecture that can effectively process and analyze the combined features from different modalities is crucial. This architecture should be able to capture the relationships and dependencies between different types of data.

Training and Optimization: Training the multimodal model requires optimizing the parameters for each modality and fine-tuning the overall model to ensure optimal performance across all modalities.

Evaluation and Validation: The performance of the multimodal ensemble approach should be evaluated using appropriate metrics for each modality and the combined model to ensure accurate detection of gendered abuse.

By integrating text, images, and videos into the ensemble approach, a more comprehensive understanding of online content can be achieved, leading to improved detection of gendered abuse.

How can the potential challenges in deploying such models in real-world scenarios be addressed?

Deploying models for detecting gendered abuse in real-world scenarios comes with several challenges that need to be addressed:

Data Privacy and Ethics: Ensuring that the data used for training the models is ethically sourced and that privacy concerns are addressed is crucial. Implementing robust data privacy measures and obtaining consent for data usage is essential.

Bias and Fairness: Addressing bias in the data and models is critical to prevent discriminatory outcomes. Regularly auditing the models for bias and fairness and implementing mitigation strategies is necessary.

Scalability and Efficiency: Real-world deployment requires models to be scalable and efficient. Optimizing the model architecture and training process to handle large volumes of data in real-time is essential.

Interpretability and Explainability: Models should be interpretable and provide explanations for their predictions to build trust with users and stakeholders. Implementing techniques for model interpretability can help address this challenge.

Continuous Monitoring and Updates: Regularly monitoring model performance in real-world settings and updating the models with new data and insights is crucial to ensure their effectiveness over time.

By addressing these challenges through a combination of technical solutions, ethical considerations, and ongoing monitoring, the deployment of models for detecting gendered abuse can be more effective and reliable in real-world scenarios.

How can the insights from this work be leveraged to develop educational interventions and awareness campaigns to tackle the root causes of online gender-based violence?

The insights from this work can be instrumental in developing educational interventions and awareness campaigns to address the root causes of online gender-based violence:

Education on Online Behavior: Educating individuals on appropriate online behavior, respectful communication, and the impact of gender-based violence can help prevent such behaviors from occurring.

Promoting Digital Literacy: Enhancing digital literacy skills, including critical thinking, media literacy, and online safety, can empower individuals to navigate online spaces responsibly and identify and report abusive content.

Creating Safe Online Spaces: Establishing safe and inclusive online spaces through community guidelines, moderation policies, and reporting mechanisms can help combat gender-based violence and create a supportive environment for all users.

Collaboration with Stakeholders: Collaborating with social media platforms, policymakers, educators, and advocacy groups to raise awareness, implement policies, and support victims of online gender-based violence can create a multi-faceted approach to tackling the issue.

Empowering Bystanders: Encouraging bystander intervention and providing resources for bystanders to support victims and challenge abusive behavior can help create a culture of accountability and solidarity against online gender-based violence.

By leveraging the insights from this work to develop targeted educational interventions and awareness campaigns, we can work towards addressing the root causes of online gender-based violence and fostering a safer and more inclusive online environment.