Multi-label Categorization of French Death Certificates using NLP and Machine Learning

Allaouzi, Imane; Mohamed, Ben Ahmed

doi:10.1145/3090354.3090384

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Previous research has studied language use around death using computational methods in a variety of contexts, from classifying reports of death [27] to identifying suicidal signals [16,17,25,36,39], and has achieved promising results. Research of bereaved language has also revealed important patterns of sentiment and linguistic style.…”

Section: The Language Of Death and Bereavementmentioning

confidence: 99%

Tending Unmarked Graves

Jiang¹,

Brubaker²

2018

Proc. ACM Hum.-Comput. Interact.

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User-generated content is central to social computing scholarship. However, researchers and practitioners often presume that these users are alive. Failing to account for mortality is problematic in social media where an increasing number of profiles represent those who have died. Identifying mortality can empower designers to better manage content and support the bereaved, as well as promote high-quality data science. Based on a computational linguistic analysis of post-mortem social media profiles and content, we report on classifiers developed to detect mortality and show that mortality can be determined after the first few occurrences of post-mortem content. Applying our classifiers to content from two other platforms also provided good results. Finally, we discuss trade-offs between models that emphasize pre-vs. post-mortem precision in this sensitive context. These results mark a first step toward identifying mortality at scale, and show how designers and scientists can attend to mortality in their work.

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Section: The Language Of Death and Bereavementmentioning

confidence: 99%

Tending Unmarked Graves

Jiang¹,

Brubaker²

2018

Proc. ACM Hum.-Comput. Interact.

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“…classifying reports of death (Imane and Mohamed 2017) to identifying suicidal signals (Mulholland and Quinn 2013;Hoste 2013, 2014;Pestian et al 2010;Huang et al 2017), and achieved promising results. Research of bereaved language has also revealed important patterns of sentiment and linguistic style.…”

Section: Introductionmentioning

confidence: 99%

Describing and Classifying Post-Mortem Content on Social Media

Jiang

Brubaker

2018

ICWSM

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As the quantity of user profiles on social media grows, so does the number of post-mortem profiles. In this paper, we present a computational linguistic analysis of post-mortem social media profiles. Specifically, we provide an analysis of pre- vs. post-mortem language use, followed by a description of classifiers we developed that can accurately classify the mortality of social media profiles. These results shed initial lights into the ways in which people speak to the dead, and mark a first step toward accurately identifying mortality on a large scale.

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“…The former presupposes that there are more than two classes to choose from, but the text must be allocated to just one (Haralabopoulos, Anagnostopoulos, & McAuley, 2020;Imane & Mohamed, 2017). Despite the relatively small size of the dataset utilised in this study, the researcher also wanted to classify different levels of academic suitability, hence the choice of multiclass classification in this study.…”

Section: Classificationmentioning

confidence: 99%

Developing a predictive model using Twitter dataset for recruiting job-fit candidates in higher education institutions

Vela Vela

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Multi-label Categorization of French Death Certificates using NLP and Machine Learning

Cited by 4 publications

References 9 publications

Tending Unmarked Graves

Tending Unmarked Graves

Describing and Classifying Post-Mortem Content on Social Media

Developing a predictive model using Twitter dataset for recruiting job-fit candidates in higher education institutions

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