Sifting F rench Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety (2021)

Abstract: Sifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety. Social media can be leveraged to understand public sentiment and feelings in real-time, and target public health messages based on user interests and emotions. In this paper, we investigate the impact of the COVID-19 pandemic in triggering intense anxiety, relying on messages exchanged on Twitter. More specifically, we provide : i) a quantitative and qualitative analysis of a corpus of tweets in French related to coronavirus, and ii) a pipeline approach (a filtering mechanism followed by Neural Network methods) to satisfactory classify messages expressing intense anxiety on social media, considering the role played by emotions.

## introduction the covid-19 pandemic -that overtook most of the world's countries -is forcing governmentissued lockdowns, strict hygiene regulations and is ultimately causing global panic, uncertainty and fear. during this crisis, social media are representing a valuable source of news and a medium for expressing people's opinions and sentiment about the emergency 1 and the restrictive measures deployed by the different countries to fight covid-19 spread. given that social media are proving instrumental in keeping people connected during the crisis, they turned out to be beneficial for mental health, in helping to combat widespread feelings of loneliness stemming from extended periods of isolation and social distancing. however, panic attacks are reported to be increasing during covid-19, as people are increasingly worried about their health 2 . panic attacks are characterized by an intense fear and sense of feeling overwhelmed, according to the national institutes of mental health. fear of contamination, sleep disturbance and probability of an economic slowdown with potential job losses are among the major factors leading to depression and anxiety among people (as emerges from the following tweets : "coucou...insomnie depuis 15 jours....merci corona."(en : "hi... insomnia since 15 days... thanks corona" ; "coronavirus au début jmen foutai de oufff mais mntn vazy y commence a être chaud" (en : coronavirus at the beginning i was not caring but now it's heated)). despite how scary they can feel, anxiety attacks are relatively common, and in most of the cases feelings are manageable. however, if multiple anxiety attacks happen, or fear over having a panic attack gets in the way, this may be a sign of anxiety disorder and a person should seek help from a mental health professional. starting from these considerations and from the observation that people are heavily using social media to express -among others -their feelings about the current sanitary situation, the goal of the current work is to investigate the impact of the covid-19 pandemic in triggering intense anxiety, relying on messages exchanged on twitter. such research issue breaks down into the following research questions : can we automatically distinguish tweets expressing a person's intense anxiety status from tweets that are more factual or expressing general feelings on covid-19 ? and does emotion detection in tweets help improving such task ? to address such research questions, we analyse a corpus of tweets in french, with the goal of delivering automated methods to detect severe anxiety in social media messages. as a first step, we propose a qualitative and quantitative study on a subset of french tweets of the multilingual twitter covid-19 dataset (chen et al., 2020). we carried out an annotation process to classify such tweets as expressing severe anxiety, or not. moreover, we also annotated each tweets with one (or more) emotion(s) @xcite and their intensity, to investigate the correlations between emotions and anxiety. we then propose and experiment with a pipeline approach (keyword-based filtering + neural network models) to classify such tweets as containing or not severe anxiety, relying on a set of features including emotions, obtaining satisfactory results. our findings, together with other analysis as the monitoring of google trends can provide continued surveillance and guide public mental health initiatives to mitigate the psychological toll of covid-19. ## anxiety-covid19 dataset for our study we rely on a subset of the french tweets of the multilingual twitter covid-19 dataset (chen et al., 2020), a large-scale public twitter dataset. such ongoing data collection started in january 2020 by tracking covid-19-related keywords and accounts 3 in multiple languages. ## classification of intense anxiety messages as our goal is to automatically detect messages expressing severe anxiety, we propose a pipeline approach that after employing the filtering mechanisms we described to twitter messages, applies supervised methods to classify tweets as expressing or not anxiety. we cast this second step as a binary classification task (anxiety tweet / irrelevant), and we experiment over our dataset of 1000 keyword-filtered tweets. (a) "panic" class (b) "non panic" class figure 3: emotion-to-features correlation classification methods. we test the following supervised methods and features : results and error analysis. ## conclusions and future work the main contributions of the paper are i) a dataset of 1032 tweets in french annotated with the "anxiety" label and six emotions (despite the small size of the minority class, the collected data are representative, as most of the tweets mention or are related to the major factors mentioned by the medical literature to lead to depression and anxiety among people) ; ii) a corpus-based analysis of the correlations between panic messages and emotions, and other linguistic features ; iii) a pipeline approach (keyword-based filtering + supervised model) to classify tweets containing panic. as future work, we plan to extend the anxiety-covid19 dataset, and to test alternative methods to deal with class imbalance. moreover, we plan to carry out a study on the impact of the pandemic evolution in time with respect to messages expressing intense anxiety.