scholarly journals Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242903
Author(s):  
Natália Bezerra Mota ◽  
Janaina Weissheimer ◽  
Marina Ribeiro ◽  
Mizziara de Paiva ◽  
Juliana Avilla-Souza ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Social Behavior ◽  
Natural Language ◽  
Social Isolation ◽  
Language Processing ◽  
Negative Emotions ◽  
Social Distancing ◽  
New Challenges ◽  
Social Habits ◽  
Global Threat

The current global threat brought on by the Covid-19 pandemic has led to widespread social isolation, posing new challenges in dealing with metal suffering related to social distancing, and in quickly learning new social habits intended to prevent contagion. Neuroscience and psychology agree that dreaming helps people to cope with negative emotions and to learn from experience, but can dreaming effectively reveal mental suffering and changes in social behavior? To address this question, we applied natural language processing tools to study 239 dream reports by 67 individuals, made either before the Covid-19 outbreak or during the months of March and April, 2020, when lockdown was imposed in Brazil following the WHO’s declaration of the pandemic. Pandemic dreams showed a higher proportion of anger and sadness words, and higher average semantic similarities to the terms “contamination” and “cleanness”. These features seem to be associated with mental suffering linked to social isolation, as they explained 40% of the variance in the PANSS negative subscale related to socialization (p = 0.0088). These results corroborate the hypothesis that pandemic dreams reflect mental suffering, fear of contagion, and important changes in daily habits that directly impact socialization.

scholarly journals Dreaming during Covid-19 pandemic: Computational assessment of dreams reveals mental suffering and fear of contagion

2020 ◽  
Author(s):  
Natalia B Mota ◽  
Janaina Weissheimer ◽  
Marina Ribeiro ◽  
Mizziara De Paiva ◽  
Juliana D'Avila ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Social Isolation ◽  
Language Processing ◽  
Negative Emotions ◽  
Global Threat

Neuroscience and psychology agree that dreaming helps to cope with negative emotions and learn from experience. The current global threat related to the COVID-19 pandemic led to widespread social isolation. Does dreaming change and/or reflect mental suffering? To address these questions, we applied natural language processing tools to study 239 dream reports from 67 individuals either before the Covid-19 outbreak or during March-April, 2020, when quarantine was imposed in Brazil following the pandemic announcement by the WHO. Pandemic dreams showed a higher proportion of anger and sadness words and higher average semantic similarities to the terms contamination and cleanness. These features were associated with mental suffering related to social isolation, as they explained 39% of the variance in PANSS negative subscale (p=0.0092). These results corroborate the hypothesis that pandemic dreams reflect mental suffering, fear of contagion, and important changes in daily habits.

scholarly journals Natural Language Processing Insights into LGBTQ+ Youth Mental Health during the COVID-19 Pandemic: Longitudinal Analysis of r/LGBTeens Micro-Community Reveals Increased Anxiety in Topics and Trends (Preprint)

2021 ◽  
Author(s):  
Hannah Stevens ◽  
Irena Acic ◽  
Sofia Rhea
Keyword(s):  
Mental Health ◽  
Natural Language Processing ◽  
Natural Language ◽  
Health Outcomes ◽  
Language Processing ◽  
Coming Out ◽  
Latent Dirichlet Allocation ◽  
Mental Health Outcomes ◽  
Lgbtq Youth ◽  
Social Distancing

BACKGROUND Widespread fear surrounding COVID-19, coupled with the extreme physical and social distancing orders, has caused severe negative mental health outcomes. Yet little is known about how the COVID-19 pandemic is impacting LGBTQ+ youth, who experienced disproportionately high adverse mental health outcomes prior to the COVID-19 pandemic. This study aims to address this knowledge gap. OBJECTIVE This work aims to harness natural language processing (NLP) methodologies to investigate the evolution of conversation topics in the most popular subreddit for LGBTQ+ youth. METHODS We generated a dataset of all r/LGBTeens subreddit posts made between Jan 1, 2020 to Feb 1, 2021. We analyzed meaningful trends in anxiety, anger, and sadness in posts. Since the distribution of anxiety before widespread social distancing orders was meaningfully different from the distribution after (P < .001), we employed Latent Dirichlet Allocation (LDA) to examine topics provoking this shift in anxiety. RESULTS While the present study did not find differences in LGBTQ+ youth anger and sadness, results revealed that anxiety increased significantly during social distancing measures compared to before lockdown (P < .001). Further analysis revealed a list of 10 anxiety-provoking topics discussed during the pandemic: attraction to a friend, coming out, coming out to family, discrimination, education, exploring sexuality, gender pronouns, love/relationship advice, starting a new relationship, and struggling with mental health. CONCLUSIONS Conversation topics related to coming-out, gender and sexual identities, discrimination, and relationships were anxiety provoking for LGBTQ+ youth, both before and after the pandemic. The frequency of these conversations increased with lifestyle disruptors related to the pandemic, reflecting LGBTQ+ teens' increased reliance on anonymous discussion forums as safe spaces for discussing lifestyle stressors during COVID-19 lifestyle disruptions (e.g., school closures).

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

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