Pub Date : 2020-09-03DOI: 10.18653/V1/2020.NLPCOVID19-2.17
Shihan Wang, M. Schraagen, E. T. K. Sang, M. Dastani
Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to July 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public.
{"title":"Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media","authors":"Shihan Wang, M. Schraagen, E. T. K. Sang, M. Dastani","doi":"10.18653/V1/2020.NLPCOVID19-2.17","DOIUrl":"https://doi.org/10.18653/V1/2020.NLPCOVID19-2.17","url":null,"abstract":"Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to July 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public.","PeriodicalId":131251,"journal":{"name":"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129133371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-03DOI: 10.18653/v1/2020.nlpcovid19-2.20
Márcia Barros, Andre Lamurias, Diana Sousa, Pedro Ruas, Francisco M. Couto
With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each researcher. This work aims to build a new semantic-based pipeline for recommending biomedical entities to scientific researchers. To this end, we developed a pipeline that creates an implicit feedback matrix based on Named Entity Recognition (NER) on a corpus of documents, using multidisciplinary ontologies for recognizing and linking the entities. Our hypothesis is that by using ontologies from different fields in the NER phase, we can improve the results for state-of-the-art collaborative-filtering recommender systems applied to the dataset created. The tests performed using the COVID-19 Open Research Dataset (CORD-19) dataset show that when using four ontologies, the results for precision@k, for example, reach the 80%, whereas when using only one ontology, the results for precision@k drops to 20%, for the same users. Furthermore, the use of multi-fields entities may help in the discovery of new items, even if the researchers do not have items from that field in their set of preferences.
{"title":"COVID-19: A Semantic-Based Pipeline for Recommending Biomedical Entities","authors":"Márcia Barros, Andre Lamurias, Diana Sousa, Pedro Ruas, Francisco M. Couto","doi":"10.18653/v1/2020.nlpcovid19-2.20","DOIUrl":"https://doi.org/10.18653/v1/2020.nlpcovid19-2.20","url":null,"abstract":"With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each researcher. This work aims to build a new semantic-based pipeline for recommending biomedical entities to scientific researchers. To this end, we developed a pipeline that creates an implicit feedback matrix based on Named Entity Recognition (NER) on a corpus of documents, using multidisciplinary ontologies for recognizing and linking the entities. Our hypothesis is that by using ontologies from different fields in the NER phase, we can improve the results for state-of-the-art collaborative-filtering recommender systems applied to the dataset created. The tests performed using the COVID-19 Open Research Dataset (CORD-19) dataset show that when using four ontologies, the results for precision@k, for example, reach the 80%, whereas when using only one ontology, the results for precision@k drops to 20%, for the same users. Furthermore, the use of multi-fields entities may help in the discovery of new items, even if the researchers do not have items from that field in their set of preferences.","PeriodicalId":131251,"journal":{"name":"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132098672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-12DOI: 10.18653/v1/2020.nlpcovid19-2.30
P. Resnik, K. Goodman, Mike Moran
Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.
{"title":"Developing a Curated Topic Model for COVID-19 Medical Research Literature","authors":"P. Resnik, K. Goodman, Mike Moran","doi":"10.18653/v1/2020.nlpcovid19-2.30","DOIUrl":"https://doi.org/10.18653/v1/2020.nlpcovid19-2.30","url":null,"abstract":"Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.","PeriodicalId":131251,"journal":{"name":"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133264011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.18653/v1/2020.nlpcovid19-2.25
Ramya Tekumalla, J. Banda
Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommended for COVID-19, there are several drugs that can potentially help with symptoms. In this work, we mined a large twitter dataset of 424 million tweets of COVID-19 chatter to identify discourse around drug mentions. While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning alongside traditional automated methods to aid in this task. By applying these complementary methods, we are able to recover almost 15% additional data, making misspelling handling a needed task as a pre-processing step when dealing with social media data.
{"title":"Characterizing drug mentions in COVID-19 Twitter Chatter","authors":"Ramya Tekumalla, J. Banda","doi":"10.18653/v1/2020.nlpcovid19-2.25","DOIUrl":"https://doi.org/10.18653/v1/2020.nlpcovid19-2.25","url":null,"abstract":"Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommended for COVID-19, there are several drugs that can potentially help with symptoms. In this work, we mined a large twitter dataset of 424 million tweets of COVID-19 chatter to identify discourse around drug mentions. While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning alongside traditional automated methods to aid in this task. By applying these complementary methods, we are able to recover almost 15% additional data, making misspelling handling a needed task as a pre-processing step when dealing with social media data.","PeriodicalId":131251,"journal":{"name":"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128495940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-04DOI: 10.18653/v1/2020.nlpcovid19-2.14
Dan Su, Yan Xu, Tiezheng Yu, Farhad Bin Siddique, Elham J. Barezi, Pascale Fung
The outbreak of COVID-19 raises attention from the researchers from various communities. While many scientific articles have been published, a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. To address the requests, we propose our CAiRE-COVID, a neural-based system that uses open-domain question answering (QA) techniques combined with summarization techniques for mining the available scientific literature. It leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from existing literature given a query. Fluent summaries are also provided to help understand the content in a more efficient way. Our system has been awarded as winner for one of the tasks in CORD-19 Kaggle Challenge. We also launched our CAiRE-COVID website for broader use. The code for our system is also open-sourced to bootstrap further study.
{"title":"CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management","authors":"Dan Su, Yan Xu, Tiezheng Yu, Farhad Bin Siddique, Elham J. Barezi, Pascale Fung","doi":"10.18653/v1/2020.nlpcovid19-2.14","DOIUrl":"https://doi.org/10.18653/v1/2020.nlpcovid19-2.14","url":null,"abstract":"The outbreak of COVID-19 raises attention from the researchers from various communities. While many scientific articles have been published, a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. To address the requests, we propose our CAiRE-COVID, a neural-based system that uses open-domain question answering (QA) techniques combined with summarization techniques for mining the available scientific literature. It leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from existing literature given a query. Fluent summaries are also provided to help understand the content in a more efficient way. Our system has been awarded as winner for one of the tasks in CORD-19 Kaggle Challenge. We also launched our CAiRE-COVID website for broader use. The code for our system is also open-sourced to bootstrap further study.","PeriodicalId":131251,"journal":{"name":"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116835478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18653/v1/2020.nlpcovid19-2.24
Aseel Addawood, Alhanouf Alsuwailem, Ali Alohali, D. Alajaji, Mashail Alturki, Jaida Alsuhaibani, Fawziah Aljabli
The coronavirus disease of 2019 (COVID-19) has had a huge impact on economies and societies around the world. While governments are taking extreme measures to reduce the spread of the virus, people are being affected by these new measures. With restrictions like lockdowns and social distancing, it has become important to understand the emotional response of the public towards the pandemic. In this paper, we study the reaction of Saudi Arabian citizens towards the pandemic. We utilize a collection of Arabic tweets that were sent during 2020, primarily through hashtags that originated in Saudi Arabia. Our results showed that people had maintained a positive reaction towards the pandemic. This positive reaction was at its highest at the beginning of the COVID-19 crisis and started to decline as time passed. Overall, the results showed that people were highly supportive of each other through this pandemic. This research can help researchers and policymakers in understanding the emotional effect of a pandemic on societies.
{"title":"Tracking And Understanding Public Reaction During COVID-19: Saudi Arabia As A Use Case","authors":"Aseel Addawood, Alhanouf Alsuwailem, Ali Alohali, D. Alajaji, Mashail Alturki, Jaida Alsuhaibani, Fawziah Aljabli","doi":"10.18653/v1/2020.nlpcovid19-2.24","DOIUrl":"https://doi.org/10.18653/v1/2020.nlpcovid19-2.24","url":null,"abstract":"The coronavirus disease of 2019 (COVID-19) has had a huge impact on economies and societies around the world. While governments are taking extreme measures to reduce the spread of the virus, people are being affected by these new measures. With restrictions like lockdowns and social distancing, it has become important to understand the emotional response of the public towards the pandemic. In this paper, we study the reaction of Saudi Arabian citizens towards the pandemic. We utilize a collection of Arabic tweets that were sent during 2020, primarily through hashtags that originated in Saudi Arabia. Our results showed that people had maintained a positive reaction towards the pandemic. This positive reaction was at its highest at the beginning of the COVID-19 crisis and started to decline as time passed. Overall, the results showed that people were highly supportive of each other through this pandemic. This research can help researchers and policymakers in understanding the emotional effect of a pandemic on societies.","PeriodicalId":131251,"journal":{"name":"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127449895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18653/v1/2020.nlpcovid19-2.12
Yulia Otmakhova, K. Verspoor, Timothy Baldwin, Simon Suster, Jey Han Lau
Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and pro-pose several ways to improve topic coherence and specificity.
{"title":"Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration","authors":"Yulia Otmakhova, K. Verspoor, Timothy Baldwin, Simon Suster, Jey Han Lau","doi":"10.18653/v1/2020.nlpcovid19-2.12","DOIUrl":"https://doi.org/10.18653/v1/2020.nlpcovid19-2.12","url":null,"abstract":"Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and pro-pose several ways to improve topic coherence and specificity.","PeriodicalId":131251,"journal":{"name":"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126385704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18653/v1/2020.nlpcovid19-2.10
Austin Van Loon, Sheridan A Stewart, Brandon Waldon, S. K. Lakshmikanth, Ishan Shah, Sharath Chandra Guntuku, G. Sherman, J. Zou, J. Eichstaedt
Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities’ response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the “Trump Gap,” or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaningmaking determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.
{"title":"Explaining the Trump Gap in Social Distancing Using COVID Discourse","authors":"Austin Van Loon, Sheridan A Stewart, Brandon Waldon, S. K. Lakshmikanth, Ishan Shah, Sharath Chandra Guntuku, G. Sherman, J. Zou, J. Eichstaedt","doi":"10.18653/v1/2020.nlpcovid19-2.10","DOIUrl":"https://doi.org/10.18653/v1/2020.nlpcovid19-2.10","url":null,"abstract":"Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities’ response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the “Trump Gap,” or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaningmaking determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.","PeriodicalId":131251,"journal":{"name":"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020","volume":"914 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132793212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}