Discovering Conversation Spaces in the Public Discourse of Gender Violence: a Comparative Between Two Different Contexts

A huge factor in gender-based violence is perception and stigma, revealed by public discourse. Topic modelling is useful for discourse analysis and reveals prevalent topics and actors. This study aims to find and compare examples of collectivist and individualist conversation spaces of gendered violence by applying Principal Component Analysis, NGram analysis and word association in two gender violence cases which occured in the different contexts of the Philippines and the United States. The data from the Philippines consist of 2010-2011 articles on the 1991 Vizconde Massacre and the data from the United States consist of 2016-2017 articles from the 2015 Stanford Rape Case. Results show that in both cases’ conversation space there is a focus on institutions involved in the cases that does not really change over time, and a time-dependent conversation space for victims. Even in two different contexts of gender violence, patterns in conversation space appear simila

Profiling Flood Risk through Crowdsourced Flood Level Reports

Disaster risk reduction and management, which includes flood risk management, is among the top priorities in the Philippines. In the process of contributing to flood monitoring and public awareness, FloodPatrol, an Android mobile phone application, allows the crowd to report flood levels in various locations. However, a crowdsourcing-based approach poses the challenge to the credibility of the crowdsourced data. Towards the goal of having a validation model using crowdsourced flood level reports, this study presents two results. First, that there is a significant difference between No Flood reports and Flood-leveled reports. Second, that there are four possible distinct groups shown in profiling the flood reports by location

Validating the Voice of the Crowd During Disasters

Since the late 1990 s, the intensity of tropical cyclones have increased over time, causing massive flooding and landslides in thePhilippines. Nationwide Operational Assessment of Hazards or Project NOAH was put in place as a responsive program for disaster prevention and mitigation. Part of the solution was to set up nababaha.com(www.nababaha.com) and FloodPatrol which provided the public with a web and mobile phone based application for reporting flood height. This paper addresses the problem of providing an interactive and visual method of validating crowdsourced flood reports for the purpose of helping frontline responders and decision makers in disaster management. The approach involves finding the neighborhood of the crowdsourced flood report and weather station data based on their geospatial proximity and time record. A report is classified as correct if it falls within the obtained confidence interval of the crowdsourced flood report neighborhood. The neighborhood of crowdsourced flood reports are correlated with weather station data, which serves as the ground truth in the validation process. Use cases are presented to provide examples of automatic validation. The results of this study is beneficial to disaster management coordinators, first-line responders, government unit officials and citizens. The system provides an interactive approach in validating reports from the crowd, aside from providing an avenue to report flood events in an area. Overall, this contributes to the study of how crowdsourced reports are verified and validated

Management of Health- and Disaster-Related Data

Prolonged health emergencies and disasters greatly affect health and well-being of individuals and communities. Past experiences on extreme emergencies and disasters have taught communities the value of preparedness. Information is key in responding to health crises especially in areas where health capacity is challenged. This chapter explains the necessity of identifying appropriate health and disaster data and proposes its transformation to information needed for decision-making. It presents different examples of systems and datasets that were used for the management of response during disasters and extreme emergencies. By introducing examples from Japan and Philippines; this chapter also points out that aside from medical data; nonmedical data; such as lifestyle and hygiene information; are necessary to protect the health of disaster victims.The objective of disaster response is to ensure that no one is left behind. It is imperative therefore that disaster response is complemented with targeted information. We recognized difficulties in community monitoring such as lack of geographic-specific information; no standard for minimum health security indicator; limited availability to submit data; and variances in need for meaningful information. There are also challenges in visualizing uncountable data; real-time updating of disaster situations; and accurate statistics disaggregated by characteristics. At the core of decision-making is the appropriate transformation of data to meaningful information. Utilization of data now becomes one of the essential adaptive technologies that needs to be provided at the local level. The challenge lies in preferential options in collecting and storing disaster- and health- and non-health-related data. Although the international initiatives expend significant effort to produce data and maps for the Health EDRM; this review considers the producers and end-users of the data products or how the data was used with the objective of studying mechanisms on how to improve on the product

Agent-Based Modeling Approach in Understanding Behavior During Disasters: Measuring Response and Rescue in eBayanihan Disaster Management Platform Authors Authors and affiliations

Development of a disaster management system is as complex as the environment it mimics. In 2015, the eBayanihan disaster management platform was launched in Metro Manila, Philippines. It is designed to be an integrated multidimensional and multi-platform system that can be used in managing the flow of information during disaster events. Since its development, usage of the system varies depending on the agent who uses the system and which area is affected by what type of disaster. As a complex problem, behavior of disaster agents, such as official responders, volunteers, regular citizens, is best understood if the system can capture, model, and visualize behavior over time. This study presents the development and implementation of an agent-based approach in understanding disaster response and rescue by automatically capturing agent behavior in the eBayanihan Disaster Management Platform. All user activities are logged and converted into behavior matrices that can be saved and imported into the Organizational Risk Analyzer (ORA) tool. ORA is used to generate the agent-based model which can be viewed in the eBayanihan platform. Actual behavior (ABehM) is compared against perceived (PBM) and expected behavior (EBM) during rescue and response. Results show that EBM networks are fully connected while PBM during rescue and response are granular and vast. Both however show centrality at the provincial and municipal level. ABehM on the other hand shows concentration only at the municipal level with more interactions with ordinary volunteers and citizens

wapr.tugon.ph: A Secure Helpline for Detecting Psychosocial Aid from Reports of Unlawful Killings in the Philippines

Availability and affordance of information communications technology has provided additional medium to monitor human rights violation. Reporting, extraction, collection and verification of reports through natural language processing and machine learning techniques can now be integrated into one system. The processed narratives becomes readily available for response, monitoring, interventions, policy-making and even as evidence in court. This paper discusses the design and development of wapr.tugon.ph, a block-chain enabled NLP-based platform that provides a simple yet effective way of reporting, validating and securing human rights violation reports from victims or witnesses. wapr.tugon.ph allows for SMS-based and web-based reporting of human rights violation. Reports are processed for detection of emotions using NRCLex, and behaviors using Stanford Parser and modified Multi-Liason algorithm from narratives which serve as input to assess wellness. A total of 5,418 records were obtained from Reddit’s subreddits and HappyDB corpus to serve as baseline corpora for our model. Our best psychosocial wellness detection model produced an accuracy and F1 score of 84% on validation set (n = 1,426) and 87% on test set (n = 666). An ethereum private blockchain is implemented to record all transactions made in the system for authenticity tracking. Findings underscore the importance of providing a system that assists in determining the appropriate psychosocial intervention to victims, families and witnesses of human rights violation. Specifically, the study contributes a framework in embedding a combined sentiment and behavior model that outputs: sentiments that are used to assess mental wellness, behaviors that are used to assess physical needs, and detection of wellness that serves as input to refer victims, families of victims and witnesses to appropriate agencies