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  1. Article ; Online: Fault Lines of Refugee Exclusion: Statelessness, Gender, and COVID-19 in South Asia.

    Chakraborty, Roshni / Bhabha, Jacqueline

    Health and human rights

    2021  Volume 23, Issue 1, Page(s) 237–250

    Abstract: Despite widespread recognition of the right to a nationality, statelessness and its attendant vulnerabilities continue to characterize the lives of millions in South Asia. During the onset of the COVID-19 pandemic, when states turned inward to protect ... ...

    Abstract Despite widespread recognition of the right to a nationality, statelessness and its attendant vulnerabilities continue to characterize the lives of millions in South Asia. During the onset of the COVID-19 pandemic, when states turned inward to protect their own citizens, refugees and de facto stateless persons found themselves excluded from humanitarian services and health care and were denied the ability to claim rights. Stateless women faced the additional burden of gender-based violence, a hostile labor market, and the threat of trafficking. This paper analyzes gender and statelessness as vectors of exclusion in South Asia, where asylum seekers are neither recognized by law nor protected by social institutions. We argue that citizenship constitutes an unearned form of social capital that is claimed and experienced in distinctively gendered ways. The pandemic has shone a bright light on the perils of statelessness, particularly for women, who face exacerbated economic inequities, the forced commodification of their sexuality, and exclusion from mechanisms of justice.
    MeSH term(s) Asia ; COVID-19 ; Female ; Gender Equity ; Gender-Based Violence ; Human Rights ; Humans ; Male ; Pandemics ; Refugees ; Women
    Language English
    Publishing date 2021-06-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1223919-7
    ISSN 2150-4113 ; 1079-0969
    ISSN (online) 2150-4113
    ISSN 1079-0969
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: PORTRAIT

    Garg, Piyush Kumar / Chakraborty, Roshni / Dandapat, Sourav Kumar

    a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT

    2023  

    Abstract: Disaster summarization approaches provide an overview of the important information posted during disaster events on social media platforms, such as, Twitter. However, the type of information posted significantly varies across disasters depending on ... ...

    Abstract Disaster summarization approaches provide an overview of the important information posted during disaster events on social media platforms, such as, Twitter. However, the type of information posted significantly varies across disasters depending on several factors like the location, type, severity, etc. Verification of the effectiveness of disaster summarization approaches still suffer due to the lack of availability of good spectrum of datasets along with the ground-truth summary. Existing approaches for ground-truth summary generation (ground-truth for extractive summarization) relies on the wisdom and intuition of the annotators. Annotators are provided with a complete set of input tweets from which a subset of tweets is selected by the annotators for the summary. This process requires immense human effort and significant time. Additionally, this intuition-based selection of the tweets might lead to a high variance in summaries generated across annotators. Therefore, to handle these challenges, we propose a hybrid (semi-automated) approach (PORTRAIT) where we partly automate the ground-truth summary generation procedure. This approach reduces the effort and time of the annotators while ensuring the quality of the created ground-truth summary. We validate the effectiveness of PORTRAIT on 5 disaster events through quantitative and qualitative comparisons of ground-truth summaries generated by existing intuitive approaches, a semi-automated approach, and PORTRAIT. We prepare and release the ground-truth summaries for 5 disaster events which consist of both natural and man-made disaster events belonging to 4 different countries. Finally, we provide a study about the performance of various state-of-the-art summarization approaches on the ground-truth summaries generated by PORTRAIT using ROUGE-N F1-scores.
    Keywords Computer Science - Computation and Language ; Computer Science - Social and Information Networks
    Subject code 410 ; 710
    Publishing date 2023-05-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: IKDSumm

    Garg, Piyush Kumar / Chakraborty, Roshni / Gupta, Srishti / Dandapat, Sourav Kumar

    Incorporating Key-phrases into BERT for extractive Disaster Tweet Summarization

    2023  

    Abstract: Online social media platforms, such as Twitter, are one of the most valuable sources of information during disaster events. Therefore, humanitarian organizations, government agencies, and volunteers rely on a summary of this information, i.e., tweets, ... ...

    Abstract Online social media platforms, such as Twitter, are one of the most valuable sources of information during disaster events. Therefore, humanitarian organizations, government agencies, and volunteers rely on a summary of this information, i.e., tweets, for effective disaster management. Although there are several existing supervised and unsupervised approaches for automated tweet summary approaches, these approaches either require extensive labeled information or do not incorporate specific domain knowledge of disasters. Additionally, the most recent approaches to disaster summarization have proposed BERT-based models to enhance the summary quality. However, for further improved performance, we introduce the utilization of domain-specific knowledge without any human efforts to understand the importance (salience) of a tweet which further aids in summary creation and improves summary quality. In this paper, we propose a disaster-specific tweet summarization framework, IKDSumm, which initially identifies the crucial and important information from each tweet related to a disaster through key-phrases of that tweet. We identify these key-phrases by utilizing the domain knowledge (using existing ontology) of disasters without any human intervention. Further, we utilize these key-phrases to automatically generate a summary of the tweets. Therefore, given tweets related to a disaster, IKDSumm ensures fulfillment of the summarization key objectives, such as information coverage, relevance, and diversity in summary without any human intervention. We evaluate the performance of IKDSumm with 8 state-of-the-art techniques on 12 disaster datasets. The evaluation results show that IKDSumm outperforms existing techniques by approximately 2-79% in terms of ROUGE-N F1-score.
    Keywords Computer Science - Computation and Language ; Computer Science - Social and Information Networks
    Subject code 006
    Publishing date 2023-05-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: EnDSUM

    Garg, Piyush Kumar / Chakraborty, Roshni / Dandapat, Sourav Kumar

    Entropy and Diversity based Disaster Tweet Summarization

    2022  

    Abstract: The huge amount of information shared in Twitter during disaster events are utilized by government agencies and humanitarian organizations to ensure quick crisis response and provide situational updates. However, the huge number of tweets posted makes ... ...

    Abstract The huge amount of information shared in Twitter during disaster events are utilized by government agencies and humanitarian organizations to ensure quick crisis response and provide situational updates. However, the huge number of tweets posted makes manual identification of the relevant tweets impossible. To address the information overload, there is a need to automatically generate summary of all the tweets which can highlight the important aspects of the disaster. In this paper, we propose an entropy and diversity based summarizer, termed as EnDSUM, specifically for disaster tweet summarization. Our comprehensive analysis on 6 datasets indicates the effectiveness of EnDSUM and additionally, highlights the scope of improvement of EnDSUM.
    Keywords Computer Science - Social and Information Networks
    Publishing date 2022-03-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Predicting Tomorrow's Headline using Today's Twitter Deliberations

    Chakraborty, Roshni / Kharat, Abhijeet / Khatua, Apalak / Dandapat, Sourav Kumar / Chandra, Joydeep

    2019  

    Abstract: Predicting the popularity of news article is a challenging task. Existing literature mostly focused on article contents and polarity to predict popularity. However, existing research has not considered the users' preference towards a particular article. ... ...

    Abstract Predicting the popularity of news article is a challenging task. Existing literature mostly focused on article contents and polarity to predict popularity. However, existing research has not considered the users' preference towards a particular article. Understanding users' preference is an important aspect for predicting the popularity of news articles. Hence, we consider the social media data, from the Twitter platform, to address this research gap. In our proposed model, we have considered the users' involvement as well as the users' reaction towards an article to predict the popularity of the article. In short, we are predicting tomorrow's headline by probing today's Twitter discussion. We have considered 300 political news article from the New York Post, and our proposed approach has outperformed other baseline models.

    Comment: This paper was accepted in CIKM Workshop on News Recommendation and Analytics (INRA), 2018, Turin, Italy
    Keywords Computer Science - Social and Information Networks ; Computer Science - Information Retrieval ; Computer Science - Machine Learning
    Subject code 070
    Publishing date 2019-01-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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