Analyzing Communal Violence Expressions in Bangla Social Media Interactions: A Comprehensive Framework and Dataset

Core Concepts

This work presents a novel framework and dataset for the computational analysis of communal violence expressions in Bangla social media interactions.

Abstract

The authors developed a comprehensive framework and dataset for analyzing communal violence in Bangla social media content. The framework categorizes communal violence into four identity-based dimensions (Religio-communal, Ethno-communal, Nondenominational Communal, and Noncommunal) and four expressions of violence (Derogation, Antipathy, Prejudication, and Repression).

The authors constructed a dataset of 13,000 Bangla social media comments annotated by a team of experts using this framework. Exploratory data analysis revealed imbalances in the dataset, with Religio-communal violence being the most prevalent.

The authors benchmarked the dataset using a BERT-based model, which achieved reasonable performance on the 4-class classification task but struggled on the more granular 16-class classification. This highlights the computational challenges in accurately detecting the nuanced expressions of communal violence in Bangla text.

The authors discuss the limitations of their work, including the linguistic complexity of Bangla, the subjectivity in annotating subtle forms of violence, and the data imbalance. They emphasize the importance of this research in monitoring and addressing the surge of communal hatred in online Bangla spaces.

Stats

The dataset contains 13,000 diverse Bangla social media comments.
The average length of the comments is 17 words, with the longest comment being 459 words.
The dataset is imbalanced, with Religio-communal violence being the most prevalent category and Nondenominational Communal violence being the least represented.

Quotes

"Communal violence in online forums has become extremely prevalent in South Asia, where many communities of different cultures coexist and share resources."
"The internet can also foster polarization and tribalism, with individuals strongly identifying with their social group and viewing other groups as adversaries."
"Acknowledging online violence as a spectrum allows for a more nuanced approach to addressing harmful online issues."

Key Insights Distilled From

Mapping Violence: Developing an Extensive Framework to Build a Bangla Sectarian Expression Dataset from Social Media Interactions

by Nazia Tasnim... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11752.pdf

Deeper Inquiries

How can the dataset be further expanded to include more diverse examples of communal violence expressions in Bangla social media?

Expanding the dataset to include more diverse examples of communal violence expressions in Bangla social media can be achieved through several strategies:

Targeted Data Collection: Actively seek out online platforms and forums where communal violence expressions are prevalent. This could include specific social media groups, forums, or websites known for hosting such content.

Inclusion of Underrepresented Categories: Identify the underrepresented categories, such as Ethno-communal and Non-denominational communal violence, and focus on collecting more samples that fall into these categories. This will help in balancing the distribution of different types of communal violence expressions in the dataset.

Crowdsourcing Annotations: Consider leveraging crowdsourcing platforms to annotate a larger volume of data. This can help in scaling up the dataset quickly and efficiently, ensuring a more diverse range of examples.

Active Monitoring: Continuously monitor online platforms for new instances of communal violence expressions. This proactive approach can help in capturing real-time data and staying updated on evolving trends in online hate speech.

Collaboration with Local Experts: Partner with local experts, researchers, or organizations working in the field of communal violence to gain insights and access to relevant data sources. Their expertise can guide the collection process and ensure the dataset's authenticity and relevance.

By implementing these strategies, the dataset can be enriched with a broader spectrum of communal violence expressions, providing a more comprehensive resource for research and analysis.

How can the insights from this research on Bangla communal violence be applied to understand and address similar challenges in other multilingual, conflict-prone regions?

The insights gained from research on Bangla communal violence can be valuable in addressing similar challenges in other multilingual, conflict-prone regions through the following approaches:

Cross-cultural Analysis: Conduct comparative studies across different regions to identify common patterns and unique characteristics of communal violence expressions. This comparative analysis can help in understanding the underlying factors driving such conflicts in diverse cultural contexts.

Development of Multilingual Models: Utilize the learnings from the Bangla communal violence research to develop multilingual models capable of detecting and categorizing hate speech and violent expressions in other languages. This can facilitate the adaptation of existing frameworks to new linguistic contexts.

Collaborative Research Initiatives: Foster collaborations with researchers and organizations working on communal violence in other regions to share insights, methodologies, and best practices. By building a network of experts, knowledge exchange can lead to more effective strategies for addressing online hate speech.

Policy Recommendations: Translate the research findings into actionable policy recommendations that can be tailored to the specific socio-political landscapes of conflict-prone regions. These recommendations can guide policymakers in implementing targeted interventions to mitigate communal violence online.

Capacity Building: Offer training programs and capacity-building initiatives based on the research outcomes to empower local stakeholders, including civil society organizations, law enforcement agencies, and community leaders. Building local expertise can enhance the response to communal violence challenges in multilingual settings.

By applying the insights from Bangla communal violence research in a broader context, stakeholders can develop more informed strategies to combat online hate speech and promote social cohesion in conflict-prone regions worldwide.