Analyzing Male Domestic Violence in Bangladesh: Insights from Data Analysis and Machine Learning

Centrala begrepp

Male victims of domestic abuse are often overlooked, but a study in Bangladesh sheds light on the prevalence, patterns, and factors of male domestic violence.

Sammanfattning

The study explores male domestic violence (MDV) in Bangladesh, highlighting the underexplored realm of male victims. It challenges prevailing notions by using data analysis and machine learning to understand MDV dynamics. The content covers:

Introduction to domestic violence and its forms.
Societal norms in Bangladesh perpetuating male victimization.
Research methodology involving data collection and exploratory data analysis (EDA).
Implementation of machine learning models for predictive modeling.
Integration of eXplainable Artificial Intelligence (XAI) techniques for model interpretability.
Contributions of the study and policy recommendations.
Literature review on DV against men globally.
Detailed methodology including dataset collection, preprocessing, and model development.
Results from EDA showcasing insights into MDV dynamics based on demographic factors.
Statistical tests like Cramer’s V correlation and Chi-square analysis for association between variables.
Model development with traditional classifiers like Logistic Regression, Decision Trees, SVM, Naïve Bayes, Random Forests, K-nearest Neighbors, Gradient Boosting, as well as DL models like Artificial Neural Networks and ensemble models like XGBoost, LightGBM, CatBoost.

Statistik

We implemented 11 traditional machine learning models with default and optimized hyperparameters, 2 deep learning models, and 4 ensemble models.
Despite various approaches, CatBoost emerged as the top performer achieving 76% accuracy.

Citat

"Our findings challenge the prevailing notion that domestic abuse primarily affects women."
"ML techniques enhance the analysis and understanding of the data."

Viktiga insikter från

Analyzing Male Domestic Violence through Exploratory Data Analysis and Explainable Machine Learning Insights

by Md Abrar Jah... på arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15594.pdf

Djupare frågor

How can societal norms be changed to better support male victims of domestic violence?

To address the issue of supporting male victims of domestic violence, it is crucial to initiate a shift in societal norms and perceptions. Here are some strategies that can help bring about this change:

Education and Awareness: Implementing educational programs and awareness campaigns that highlight the prevalence of male domestic violence can help challenge existing stereotypes and misconceptions. By educating the public about the reality of male victimization, we can break down barriers to seeking help.

Promoting Gender Equality: Emphasizing gender equality in all aspects of society can create an environment where both men and women feel empowered to speak out against abuse. This includes promoting equal rights, opportunities, and responsibilities for all individuals regardless of gender.

Support Services: Establishing dedicated support services specifically tailored for male victims of domestic violence is essential. These services should provide a safe space for men to seek assistance, counseling, legal advice, and other forms of support without fear or stigma.

Legal Reforms: Advocating for changes in laws and policies to ensure that they are inclusive of all genders when addressing domestic violence cases is critical. Legal frameworks should offer equal protection and resources for both male and female victims.

Challenging Stereotypes: Encouraging open discussions about masculinity, vulnerability, and emotional expression can help break down harmful stereotypes that prevent men from seeking help when experiencing abuse.

Media Representation: Promoting accurate portrayals of male victims in media representations can play a significant role in changing societal attitudes towards male domestic violence.

By implementing these strategies collectively at various levels - individual, community, institutional - we can work towards creating a more supportive environment for male victims of domestic violence.

What are potential limitations or biases in using machine learning to analyze sensitive topics like domestic violence?

When utilizing machine learning algorithms to analyze sensitive topics such as domestic violence involving males as victims, several limitations and biases may arise:

Data Bias: The quality and representativeness of the dataset used for training ML models could introduce bias if it does not accurately reflect the diversity within the population affected by MDV.

Algorithmic Bias: Machine learning algorithms may inadvertently perpetuate existing biases present in the data used for training them (e.g., underreporting or misclassification).

Interpretability Issues: Black-box nature inherent in some complex ML models might make it challenging to interpret how decisions are made regarding identifying patterns related to MDV among males.

4..Privacy Concerns: Handling sensitive data related to instances of abuse requires strict privacy measures; however,
ML models might unintentionally reveal personal information if not appropriately secured.
5..Ethical Considerations: Ensuring ethical use practices throughout model development stages is crucial since biased
outcomes could have detrimental effects on vulnerable populations like abused males
6..Lack Of Contextual Understanding: Machine learning models may lack contextual understanding necessary when dealing with nuanced issues like MDV among males
Addressing these limitations involves thorough data preprocessing techniques ensuring fairness during model development,
regular audits on algorithm performance post-deployment monitoring any unintended consequences arising from biased predictions

How can technology be leveraged provide more accessible support systems Male Victims Domestic Abuse?

Technology offers numerous avenues through which more accessible support systems tailored specifically toward Male Victims Domestic Abuse (MDA)can be established:
1- Online Support Platforms: Developing online platforms offering confidential chat services,counseling sessions,and informational resources catered exclusively towards MDA allows individuals access immediate assistance remotely.
2- Mobile Applications: Creating mobile apps providing emergency helplines,safety planning tools,and real-time notifications alerting users about available shelters or legal aid ensures quick access vital resources during crisis situations.
3- Virtual Support Groups: Facilitating virtual peer-support groups via video conferencing platforms enables MDA survivors connect with others share experiences receive encouragement fostering sense community belongingness.
4- AI-Powered Chatbots: Integrating AI-powered chatbots capable engaging conversations providing emotional support guidance navigating challenges associated with MDA offers round-the-clock assistance personalized responses based user's needs
5- Data Analytics Monitoring Systems: Employing data analytics monitoring systems track trends patterns related MDA incidents identify high-risk scenarios early intervention preventive measures enhancing overall safety security MDA survivors.
These technological solutions complement traditional forms support empower Male Victims Domestic Abuse navigate their experiences seek necessary assistance overcoming barriers accessing care resources