Add to ORKGIn the context of an epidemiological study involving multilingual social media, this paper reports on the ability of machine translation systems to preserve content relevant for a document classification task designed to determine whether the social media text is related to covid-19. The results indicate that machine translation does provide a feasible basis for scaling epidemiological social media surveillance to multiple languages. Moreover, a qualitative error analysis revealed that the majority of classification errors are not caused by MT errors
In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a la...
Social media is a great source of data for analyses, since they provide ways for people to share em...
Statements on social media can be analysed to identify individuals who are experiencing red flag med...
In the context of an epidemiological study involving multilingual social media, this paper reports o...
International audienceThis paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detec...
Abstract Twitter and social media as a whole have great potential as a source of disease surveillanc...
Background: The COVID-19 pandemic has created a pressing need for integrating information from dispa...
In this paper, we approach the multilingual text classification task in the context of the epidemiol...
Utilisation of multilingual language models such as mBERT and XLM-RoBERTa has increasingly gained at...
Background: Different linguo-cultural communities might react to an outbreak differently. The 2015 S...
Background: Different linguo-cultural communities might react to an outbreak differently. The 2015 S...
Every day, more people are becoming infected and dying from exposure to COVID-19. Some countries in ...
The massive spread of false information on social media has become a global risk especially in a glo...
Part 6: 10th Mining Humanistic Data Workshop (MHDW 2021)International audienceSince December 2019, C...
In this paper we aim to analyze the Italian social media communication about COVID-19 through a Twit...
In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a la...
Social media is a great source of data for analyses, since they provide ways for people to share em...
Statements on social media can be analysed to identify individuals who are experiencing red flag med...
In the context of an epidemiological study involving multilingual social media, this paper reports o...
International audienceThis paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detec...
Abstract Twitter and social media as a whole have great potential as a source of disease surveillanc...
Background: The COVID-19 pandemic has created a pressing need for integrating information from dispa...
In this paper, we approach the multilingual text classification task in the context of the epidemiol...
Utilisation of multilingual language models such as mBERT and XLM-RoBERTa has increasingly gained at...
Background: Different linguo-cultural communities might react to an outbreak differently. The 2015 S...
Background: Different linguo-cultural communities might react to an outbreak differently. The 2015 S...
Every day, more people are becoming infected and dying from exposure to COVID-19. Some countries in ...
The massive spread of false information on social media has become a global risk especially in a glo...
Part 6: 10th Mining Humanistic Data Workshop (MHDW 2021)International audienceSince December 2019, C...
In this paper we aim to analyze the Italian social media communication about COVID-19 through a Twit...
In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a la...
Social media is a great source of data for analyses, since they provide ways for people to share em...
Statements on social media can be analysed to identify individuals who are experiencing red flag med...