approfondimento - Multimodal Dataset - # Hateful Meme Detection

Deciphering Hate: Identifying Hateful Memes and Their Targets in Bengali

Q: How can the findings from this study be applied to improve online moderation practices?

The findings from this study can significantly enhance online moderation practices by providing a more effective and accurate way to detect hateful memes and their targeted entities. By utilizing multimodal frameworks like DORA, platforms can better identify harmful content that may otherwise go unnoticed. This can lead to quicker removal of offensive material, creating a safer online environment for users. Additionally, the insights gained from this study can help in developing more robust algorithms for automated content moderation, reducing the burden on human moderators and improving response times to harmful content.

Q: What are the ethical considerations when developing datasets for hate speech detection?

When developing datasets for hate speech detection, several ethical considerations must be taken into account: User Privacy: Ensure that data collection complies with privacy regulations and does not infringe upon user rights. Bias Mitigation: Address any inherent biases in the dataset through diverse annotation teams and comprehensive guidelines. Intended Use: Clearly define how the dataset will be used and ensure it aligns with ethical standards. Transparency: Be transparent about data sources, collection methods, and annotation processes to maintain trust. Fair Representation: Ensure that all groups are represented fairly in the dataset to avoid perpetuating stereotypes or discrimination.

Q: How can multimodal frameworks like DORA be adapted for other languages with similar challenges?

Multimodal frameworks like DORA can be adapted for other languages facing similar challenges by following these steps: Data Collection: Gather a diverse set of memes in different languages with code-mixed captions similar to those in Bengali. Annotation Guidelines: Develop clear annotation guidelines tailored to each language's cultural nuances and context-specific references. Model Training: Fine-tune pre-trained models on multilingual data sets incorporating both visual features (images) and textual features (captions). Evaluation Metrics: Use weighted F1 scores or other relevant metrics suitable for evaluating performance across multiple languages. 5.Transfer Learning: Utilize transfer learning techniques to leverage knowledge learned from one language dataset onto another language while fine-tuning specific aspects related to linguistic differences. By adapting these strategies, multimodal frameworks like DORA can effectively address hate speech detection challenges across various languages while maintaining high performance levels comparable to those achieved in Bengali datasets such as BHM (Bengali Hateful Memes).

Concetti Chiave

Identifying hateful memes and their targeted entities in low-resource languages like Bengali is crucial for understanding social dynamics and countering hate speech.

Sintesi

Internet memes, often a source of humor, can also spread hateful content targeting individuals or communities. Existing research on high-resource languages overlooks challenges in low-resource languages like Bengali. A novel dataset, BHM (Bengali Hateful Memes), is introduced for detecting hateful memes and their targets. DORA, a multimodal deep neural network, outperforms state-of-the-art baselines on identifying hateful memes and their targets.

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Statistiche

The dataset consists of 7,148 Bengali memes.
DORA outperforms several state-of-the-art rivaling baselines.
The dataset includes two sets of labels for detecting hateful memes and identifying targeted entities.

Citazioni

"Despite being the seventh most widely spoken language globally, Bengali is considered one of the notable resource-constrained languages."
"Memes have gained significant traction in social media, reaching a broad audience and influencing public sentiment."
"DORA is generalizable on other low-resource hateful meme datasets."

Approfondimenti chiave tratti da

Deciphering Hate

by Eftekhar Hos... alle arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10829.pdf

Domande più approfondite

How can the findings from this study be applied to improve online moderation practices?

The findings from this study can significantly enhance online moderation practices by providing a more effective and accurate way to detect hateful memes and their targeted entities. By utilizing multimodal frameworks like DORA, platforms can better identify harmful content that may otherwise go unnoticed. This can lead to quicker removal of offensive material, creating a safer online environment for users. Additionally, the insights gained from this study can help in developing more robust algorithms for automated content moderation, reducing the burden on human moderators and improving response times to harmful content.

What are the ethical considerations when developing datasets for hate speech detection?

When developing datasets for hate speech detection, several ethical considerations must be taken into account:

User Privacy: Ensure that data collection complies with privacy regulations and does not infringe upon user rights.
Bias Mitigation: Address any inherent biases in the dataset through diverse annotation teams and comprehensive guidelines.
Intended Use: Clearly define how the dataset will be used and ensure it aligns with ethical standards.
Transparency: Be transparent about data sources, collection methods, and annotation processes to maintain trust.
Fair Representation: Ensure that all groups are represented fairly in the dataset to avoid perpetuating stereotypes or discrimination.

How can multimodal frameworks like DORA be adapted for other languages with similar challenges?

Multimodal frameworks like DORA can be adapted for other languages facing similar challenges by following these steps:

Data Collection: Gather a diverse set of memes in different languages with code-mixed captions similar to those in Bengali.
Annotation Guidelines: Develop clear annotation guidelines tailored to each language's cultural nuances and context-specific references.
Model Training: Fine-tune pre-trained models on multilingual data sets incorporating both visual features (images) and textual features (captions).
Evaluation Metrics: Use weighted F1 scores or other relevant metrics suitable for evaluating performance across multiple languages.
5.Transfer Learning: Utilize transfer learning techniques to leverage knowledge learned from one language dataset onto another language while fine-tuning specific aspects related to linguistic differences.

By adapting these strategies, multimodal frameworks like DORA can effectively address hate speech detection challenges across various languages while maintaining high performance levels comparable to those achieved in Bengali datasets such as BHM (Bengali Hateful Memes).