Domestic Violence (DV) is a pervasive public health problem characterized by patterns of coercive and abusive behavior within intimate relationships. With the rise of social media as a key outlet for DV victims to disclose their experiences, online self - di sclosure has emerged as a critical yet underexplored avenue for support - seeking. In addition, existing research lacks a comprehensive and nuanced understanding of DV self - disclosure, support provisions, and their connections. To address these gaps, this study proposes a novel c omputational framework for modeling DV support - seeking behavior alongside community support mechanisms. The framework consists of four key components: self - disclosure detection, post clustering, topic summarization, and support extraction and mapping . We implement and evaluate the framework with data collected from relevant social media communities. Our findings not only advance existing knowledge on DV self - disclosure and online support provisions but also enable victim - centered digital interventions.

Domestic violence, which is often perceived as a gendered issue among female victims, has gained increasing attention in recent years. Despite this focus, male victims of domestic abuse remain primarily overlooked, particularly in Bangladesh. Our study represents a pioneering exploration of the underexplored realm of male domestic violence (MDV) within the Bangladeshi context, shedding light on its prevalence, patterns, and underlying factors. Existing literature predominantly emphasizes female victimization in domestic violence scenarios, leading to an absence of research on male victims. We collected data from the major cities of Bangladesh and conducted exploratory data analysis to understand the underlying dynamics. We implemented 11 traditional machine learning models with default and optimized hyperparameters, 2 deep learning, and 4 ensemble models. Despite various approaches, CatBoost has emerged as the top performer due to its native support for categorical features, efficient handling of missing values, and robust regularization techniques, achieving 76% accuracy. In contrast, other models achieved accuracy rates in the range of 58-75%. The eXplainable AI techniques, SHAP and LIME, were employed to gain insights into the decision-making of black-box machine learning models. By shedding light on this topic and identifying factors associated with domestic abuse, the study contributes to identifying groups of people vulnerable to MDV, raising awareness, and informing policies and interventions aimed at reducing MDV. Our findings challenge the prevailing notion that domestic abuse primarily affects women, thus emphasizing the need for tailored interventions and support systems for male victims. ML techniques enhance the analysis and understanding of the data, providing valuable insights for developing effective strategies to combat this pressing social issue.

Kismet Jackson used to carry her handgun just about everywhere in San Bernardino County. To get her nails done. To pick up her prescription. To hang out with her grandchildren. For her, it was all about staying safe. "Being out and about, you just want to protect yourself," explained Jackson, an Air Force veteran and member of the National African American Gun Assn.

Femicide refers to the killing of a female victim, often perpetrated by an intimate partner or family member, and is also associated with gender-based violence. Studies have shown that there is a pattern of escalating violence leading up to these killings, highlighting the potential for prevention if the level of danger to the victim can be assessed. Machine learning offers a promising approach to address this challenge by predicting risk levels based on textual descriptions of the violence. In this study, we employed the Long Short Term Memory (LSTM) technique to identify patterns of behavior in Brazilian police reports preceding femicides. Our first objective was to classify the content of these reports as indicating either a lower or higher risk of the victim being murdered, achieving an accuracy of 66%. In the second approach, we developed a model to predict the next action a victim might experience within a sequence of patterned events. Both approaches contribute to the understanding and assessment of the risks associated with domestic violence, providing authorities with valuable insights to protect women and prevent situations from escalating.

Stealing someone else's joke is one of the highest crimes in comedy. With new AI tools like ChatGPT, some comedians are now worried about getting ripped off. Comedian Matt Rife is facing backlash on social media after allegedly telling a 6-year-old boy that his mother buys his presents with profits from OnlyFans. In a Saturday video that has garnered over 13 million views, TikToker Bunny Hedaya claimed Rife had started "beef" with her child online. Hedaya's son drew Rife's attention after criticizing the comedian's recent Netflix standup special, "Natural Selection."

By using low-cost microcontrollers and TinyML, we investigate the feasibility of detecting potential early warning signs of domestic violence and other anti-social behaviors within the home. We created a machine learning model to determine if a door was closed aggressively by analyzing audio data and feeding this into a convolutional neural network to classify the sample. Under test conditions, with no background noise, accuracy of 88.89\% was achieved, declining to 87.50\% when assorted background noises were mixed in at a relative volume of 0.5 times that of the sample. The model is then deployed on an Arduino Nano BLE 33 Sense attached to the door, and only begins sampling once an acceleration greater than a predefined threshold acceleration is detected. The predictions made by the model can then be sent via BLE to another device, such as a smartphone of Raspberry Pi.

A chilling video featuring the faces of five Israeli women who were murdered by their husbands has gone viral in an eerie social media campaign that has brought them back to life after death. With artificial intelligence and animation capabilities from Israeli "creative reality" startup D-ID, the videos use the voice of each victim -- as well as realistic facial features and gestures -- to convey the message that someone living in the reality of domestic abuse can and should get out before its too late. The project, dubbed Listen To Our Voices, was created in response to a global and local surge in domestic violence since the start of the pandemic, and in honor of International Day for the Elimination of Violence Against Women on November 25. With deep learning technology, AI startup, D-ID captured the faces, voices, and gestures of the late Michal Sela, the late Esther Aharonovitch, the late Marin Haj Yechieh, the late Esther Barhani, and the late Sagit Ozeri, as they described their own marital difficulties which led to verbal and physical abuse from their spouses. The five victims also encouraged other women who experience similar relationships to talk to experts who know how to deal with these situations.

Amba Kak was in law school in India when the country rolled out the Aadhaar project in 2009. The national biometric ID system, conceived as a comprehensive identity program, sought to collect the fingerprints, iris scans, and photographs of all residents. It wasn't long, Kak remembers, before stories about its devastating consequences began to spread. "We were suddenly hearing reports of how manual laborers who work with their hands--how their fingerprints were failing the system, and they were then being denied access to basic necessities," she says. "We actually had starvation deaths in India that were being linked to the barriers that these biometric ID systems were creating. So it was a really crucial issue."

Lack of trust: One of the biggest difficulty for AI or ML products is lack of trust. Millions of dollars have been spent on prototyping but with very little success in the real-world launches. Essentially, one of the most fundamental values of doing business and providing value to customers is trust, and Artificial Intelligence is the most-heavily debated technology when it comes to ethical concerns and related trust issues. Trust comes from involving different options and parties in the entire development phase, which is not done in the prototype phase. The complexity of a launch: Building a prototype is easy, but there are tens of other external entities that need to be considered when moving into the real world.

A group of tech experts are developing a new Artificial Intelligence (AI) chatbot to help victims of domestic violence more easily access the justice system and counselling programmes as the problem balloons in Thailand. Studies have shows that people live in fear of judgement and may be more embarrassed to relay the whole truth about the sensitive and often, humiliating, experience. Some fear the pity that falls upon them when they relay their experiences and others do not understand why the person that they are relaying their experience to, does not show any pity or any emotion for that matter. This is where the AI machines are brought into the picture when it comes to domestic violence. People understand that they are speaking to just a machine.