Participatory action research on climate risk management, Bangladesh

The rural populations of southern Bangladesh are some of the most vulnerable communities in the world to the future impacts of climate change. They are particularly at risk from floods, waterlogged soils, and increasing salinity of both land and water. The objective of this project was to analyze the vulnerability of people in four villages that are experiencing different levels of soil salinity. The study evaluated the strengths and weaknesses of current coping strategies and assessed the potential of an index-based insurance scheme, designed diversification and better information products to improve adaptive capacity

Warning systems as social processes for Bangladesh cyclones

Purpose: The purpose of this paper is to connect the theoretical idea of warning systems as social processes with empirical data of people’s perceptions of and actions for warning for cyclones in Bangladesh. / Design/methodology/approach: A case study approach is used in two villages of Khulna district in southwest Bangladesh: Kalabogi and Kamarkhola. In total, 60 households in each village were surveyed with structured questionnaires regarding how they receive their cyclone warning information as well as their experiences of warnings for Cyclone Sidr in 2007 and Cyclone Aila in 2009. / Findings: People in the two villages had a high rate of receiving cyclone warnings and accepted them as being credible. They also experienced high impacts from the cyclones. Yet evacuation rates to cyclone shelters were low. They did not believe that significant cyclone damage would affect them and they also highlighted the difficulty of getting to cyclone shelters due to poor roads, leading them to prefer other evacuation options which were implemented if needed. / Originality/value: Theoretical constructs of warning systems, such as the First Mile and late warning, are rarely examined empirically according to people’s perceptions of warnings. The case study villages have not before been researched with respect to warning systems. The findings provide empirical evidence for long-established principles of warning systems as social processes, usually involving but not relying on technical components

Sustainability practices at higher education institutions in Asia

Purpose: It is still unclear how Asian universities incorporate the theory or practice of sustainable development (SD) in their research and education programmes. To address this gap, the purpose of this paper is to report on a study that has examined how universities in Asian countries handle and address matters related to SD. Design/methodology/approach: The study used a bibliometric analysis and an online survey-method. The online survey data were analysed through descriptive analysis and one-sample student’s t-test. Findings: The study indicates that there is considerable variation among the Asian countries regarding sustainability practices in higher education institutions (HEIs). The HEIs in far eastern countries, such as Indonesia, Malaysia and Thailand are perceived to demonstrate more sustainability practices. Research limitations/implications: Even though a substantial number of participants participated in the survey, it did not cover all Asian countries. The online survey was carried out over a limited period of time, and not all HEIs in the field may have received information about the study. Practical implications: Asia is the largest continent facing a number of sustainability challenges. In this context, the contribution of HEIs is very important. The findings of the current study may serve as a baseline for Asian HEIs to take more initiatives towards SD goals, as HEIs are responsible for the education and training of hundreds of thousands of students who will be occupying key positions in industry, government or education in the coming years. Originality/value: The study contributes to the existing literature in two distinct ways. First, it was possible to develop a comprehensive instrument to measure sustainability practices in HEIs. Second, this study has filled the gap of the scarcity of studies regarding sustainability practices in HEIs in Asia

Assessing the impacts of climate change in cities and their adaptive capacity: Towards transformative approaches to climate change adaptation and poverty reduction in urban areas in a set of developing countries

Many cities across the world are facing many problems climate change poses to their populations, communities and infrastructure. These vary from increased exposures to floods, to discomfort due to urban heat, depending on their geographical locations and settings. However, even though some cities have a greater ability to cope with climate change challenges, many struggle to do so, particularly in cities in developing countries. In addition, there is a shortage of international studies which examine the links between climate change adaptation and cities, and which at the same time draw some successful examples of good practice, which may assist future efforts. This paper is an attempt to address this information need. The aim of this paper is to analyse the extent to which cities in a sample of developing countries are attempting to pursue climate change adaptation and the problems which hinder this process. Its goal is to showcase examples of initiatives and good practice in transformative adaptation, which may be replicable elsewhere. To this purpose, the paper describes some trends related to climate change in a set of cities in developing countries across different continents, including one of the smallest capital cities (Georgetown, Guyana) and Shanghai, one the world's most populous cities. In particular, it analyses their degree of vulnerability, how they manage to cope with climate change impacts, and the policies being implemented to aid adaptation. It also suggests the use of transformative approaches which may be adopted, in order to assist them in their efforts towards investments in low-carbon and climate-resilient infrastructure, thereby maximizing investments in urban areas and trying to address their related poverty issues. This paper addresses a gap in the international literature on the problems many cities in developing countries face, in trying to adapt to a changing climate

Fostering coastal resilience to climate change vulnerability in Bangladesh, Brazil, Cameroon and Uruguay: a cross-country comparison

© 2017, Springer Science+Business Media B.V. This paper describes a comparative study of four different cases on vulnerability, hazards and adaptive capacity to climate threats in coastal areas and communities in four developing countries: Bangladesh, Brazil, Cameroon and Uruguay. Coastal areas are vulnerable to sea-level rise (SLR), storm surges and flooding due to their (i) exposure, (ii) concentration of settlements, many of which occupied by less advantaged groups and (iii) the concentration of assets and services seen in these areas. The objective of the paper is twofold: (i) to evaluate current evidence of coastal vulnerability and adaptive capacity and (ii) to compare adaptation strategies being implemented in a sample of developing countries, focusing on successful ones. The followed approach for the case evaluation is based on (i) documenting observed threats and damages, (ii) using indicators of physical and socioeconomic vulnerability and adaptive capacity status and (iii) selecting examples of successful responses. Major conclusions based on cross-case comparison are (a) the studied countries show different vulnerability, adaptive capacity and implementation of responses, (b) innovative community-based (CBA) and ecosystem-based adaptation (EbA) and (c) early warning systems are key approaches and tools to foster climate resilience. A recommendation to foster the resilience of coastal communities and services is that efforts in innovative adaptation strategies to sea-level rise should be intensified and integrated with climate risk management within the national adaption plans (NAPAs) in order to reduce the impacts of hazards

A systematic review of hate speech automatic detection using natural language processing

Abstract With the multiplication of social media platforms, which offer anonymity, easy access and online community formation and online debate, the issue of hate speech detection and tracking becomes a growing challenge to society, individual, policy-makers and researchers. Despite efforts for leveraging automatic techniques for automatic detection and monitoring, their performances are still far from satisfactory, which constantly calls for future research on the issue. This paper provides a systematic review of literature in this field, with a focus on natural language processing and deep learning technologies, highlighting the terminology, processing pipeline, core methods employed, with a focal point on deep learning architecture. From a methodological perspective, we adopt PRISMA guideline of systematic review of the last 10 years literature from ACM Digital Library and Google Scholar. In the sequel, existing surveys, limitations, and future research directions are extensively discussed

Team Oulu at SemEval-2020 task 12:multilingual identification of offensive language, type and target of Twitter post using translated datasets

Abstract With the proliferation of social media platforms, anonymous discussions together with easy online access, reports on offensive content have caused serious concern to both authorities and research communities. Although there is extensive research in identifying textual offensive language from online content, the dynamic discourse of social media content, as well as the emergence of new forms of offensive language, especially in a multilingual setting, calls for future research in the issue. In this work, we tackled Task A, B, and C of Offensive Language Challenge at SemEval2020. We handled offensive language in five languages: English, Greek, Danish, Arabic, and Turkish. Specifically, we pre-processed all provided datasets and developed an appropriate strategy to handle Tasks (A, B, & C) for identifying the presence/absence, type and the target of offensive language in social media. For this purpose, we used OLID2019, OLID2020 datasets, and generated new datasets, which we made publicly available. We used the provided unsupervised machine learning implementation for automated annotated datasets and the online Google translation tools to create new datasets as well. We discussed the limitations and the success of our machine learning-based approach for all the five different languages. Our results for identifying offensive posts (Task A) yielded satisfactory accuracy of 0.92 for English, 0.81 for Danish, 0.84 for Turkish, 0.85 for Greek, and 0.89 for Arabic. For the type detection (Task B), the results are significantly higher (.87 accuracy) compared to target detection (Task C), which yields .81 accuracy. Moreover, after using automated Google translation, the overall efficiency improved by 2% for Greek, Turkish, and Danish

Finnish hate-speech detection on social media using CNN and FinBERT

Abstract There has been a lot of research in identifying hate posts from social media because of their detrimental effects on both individuals and society. The majority of this research has concentrated on English, although one notices the emergence of multilingual detection tools such as multilingual-BERT (mBERT). However, there is a lack of hate speech datasets compared to English, and a multilingual pre-trained model often contains fewer tokens for other languages. This paper attempts to contribute to hate speech identification in Finnish by constructing a new hate speech dataset that is collected from a popular forum (Suomi24). Furthermore, we have experimented with FinBERT pre-trained model performance for Finnish hate speech detection compared to state-of-the-art mBERT and other practices. In addition, we tested the performance of FinBERT compared to fastText as embedding, which employed with Convolution Neural Network (CNN). Our results showed that FinBERT yields a 91.7% accuracy and 90.8% F1 score value, which outperforms all state-of-art models, including multilingual-BERT and CNN

Car parking user’s behavior using news articles mining based approach

Abstract Studying individual’s parking choice behavior can considerably contribute towards evidence-based policing in urban area. This study investigates evidence gathered by mining Finland news article API concerning car parking associated topics in order to comprehend user’s behavior and identify potential unforeseen circumstances that may impact users’ decisions and preferences. The study follows a natural language processing research pipeline, emphasizing word co-occurrence analysis, sentiment score and named-entity monitoring. The results can be exploited by local authorities to develop further evidence based policing in city urban planning