COVID-19 and Mental Health/Substance Use Disorders on Reddit: A Longitudinal Study

COVID-19 pandemic has adversely and disproportionately impacted people suffering from mental health issues and substance use problems. This has been exacerbated by social isolation during the pandemic and the social stigma associated with mental health and substance use disorders, making people reluctant to share their struggles and seek help. Due to the anonymity and privacy they provide, social media emerged as a convenient medium for people to share their experiences about their day to day struggles. Reddit is a well-recognized social media platform that provides focused and structured forums called subreddits, that users subscribe to and discuss their experiences with others. Temporal assessment of the topical correlation between social media postings about mental health/substance use and postings about Coronavirus is crucial to better understand public sentiment on the pandemic and its evolving impact, especially related to vulnerable populations. In this study, we conduct a longitudinal topical analysis of postings between subreddits r/depression, r/Anxiety, r/SuicideWatch, and r/Coronavirus, and postings between subreddits r/opiates, r/OpiatesRecovery, r/addiction, and r/Coronavirus from January 2020 - October 2020. Our results show a high topical correlation between postings in r/depression and r/Coronavirus in September 2020. Further, the topical correlation between postings on substance use disorders and Coronavirus fluctuates, showing the highest correlation in August 2020. By monitoring these trends from platforms such as Reddit, epidemiologists, and mental health professionals can gain insights into the challenges faced by communities for targeted interventions.Comment: First workshop on computational & affective intelligence in healthcare applications in conjunction with ICPR 202

토픽 모델링, 동시 출현 단어, 감성 분석 방법을 중심으로

학위논문(석사) -- 서울대학교대학원 : 의과대학 의학과, 2021.8. 민경복.코로나바이러스감염증-19 (코로나19)는 2019년 12월 중국 우한에서 처음 확인된 호흡기 감염질환이다. 코로나 바이러스와 관련하여 보건 의료 및 우리 사회 전반의 이슈를 파악하고자 본 연구를 수행하였다. 2020년 1월부터 2021년 3월까지 1년 3개월 동안 지식 정보 공유 포털 사이트에서 검색어 ‘코로나’를 키워드로 하는 질문 23,463건을 수집하였다. 수집한 문서에서 최소 의미 단위인 형태소로 분해하는 전처리 과정을 거친 후, 텍스트마이닝 방법을 통해 분석을 수행하였다. 형태소 단위로 추출한 단어의 빈도를 측정하여 ‘코로나’와 관련된 키워드의 중요도를 살펴보고 Latent Dirichlet Allocation (LDA) 방법을 통해 문서 내 키워드의 집합을 분석하고 8개의 주제를 추출하였다. 동시 출현 단어 분석을 통해 한 문서에 동시에 등장한 단어의 빈도를 측정하여 단어와 단어 사이의 연관성을 살펴보았으며 감성 분석을 통해 감염병에 대한 대중의 의견 또는 감정을 시간의 흐름에 따라 분석하였다. ‘코로나’와 관련된 주요 이슈로는 사회, 보건 의료, 법률 등으로 분류되었고 사회적 측면에서는 고용과 급여를 둘러싼 회사와 관련된 주제가 문서에서 가장 많은 비중을 차지하여 중요한 문제가 될 수 있음을 암시하였다. 보건 의료 측면에서는 코로나19의 의심 증상과 통증을 통해 감염 여부를 파악하는 것이 대중의 걱정이자 주요 관심사라고 볼 수 있다. 코로나19이 장기화되고 ‘감염법예방법’이 개정됨에 따라 관련 법률에 대해 대중의 관심이 있다는 것을 확인할 수 있다. 감성 분석을 통해 코로나19에 대한 대중의 전반적인 태도는 감염병 유행 초기일수록 부정적일 수 있음을 시사한다. 본 연구는 텍스트 마이닝 방법을 통한 탐색적 연구로 코로나19 데이터에 대한 신뢰성을 높이고 감염병 대응을 위한 활용 방안을 마련하기 위해서 뉴스 보도 및 논문 초록 등을 포함하여 다양한 자료원을 추가한 연구가 필요하다.Coronavirus disease (COVID-19) is a pandemic. This study was conducted to identify issues related to coronavirus from public health perspective and social perspective. From January 2020 to March 2021, We have collected 23463 postings with the keyword "corona" in Knowledge Sharing Platform. In this study, the collected postings were broken down into morphemes. After the preprocessing process, it was analyzed through a text mining method. Frequency was measured through word frequency analysis and topics were classified via Topic Modeling (LDA) method. We explore the interword associations through word network analysis. And the public's opinions or emotions were analyzed through sentiment analysis. Major issues related to Corona are largely classified as social, public health, and other administrative issues. From a social perspective, it was confirmed that topics related to companies focused on employment and wages accounted for the largest portion of the documents and could be important issues. Identifying the infection through symptoms and pain is a major concern for people from a public health perspective. In-depth research is needed on the interests of people related to COVID-19.제 1 장 서 론 1 제 1 절 연구의 배경 1 제 2 절 연구의 목적 8 제 2 장 방 법 9 제 1 절 데이터 수집 9 제 2 절 데이터 전처리 10 제 3 절 토픽 모델링 분석 11 제 4 절 동시 출현 단어 분석 15 제 5 절 감성 분석 17 제 3 장 결 과 20 제 1 절 주요 키워드의 빈도 20 제 2 절 토픽 모델링 결과 24 제 3 절 동시 출현 단어 29 제 4 절 감성 분석 결과 35 제 4 장 고 찰 44 제 5 장 결 론 54 참고문헌 56 Abstract 63석

Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19

Quantifying the characteristics of public attention is an essential prerequisite for appropriate crisis management during severe events such as pandemics. For this purpose, we propose language-agnostic tweet representations to perform large-scale Twitter discourse classification with machine learning. Our analysis on more than 26 million COVID-19 tweets shows that large-scale surveillance of public discourse is feasible with computationally lightweight classifiers by out-of-the-box utilization of these representations.Comment: 14 pages, 4 figure

The Response of Governments and Public Health Agencies to COVID-19 Pandemics on Social Media: A Multi-Country Analysis of Twitter Discourse

During the COVID-19 pandemic, information is being rapidly shared by public health experts and researchers through social media platforms. Whilst government policies were disseminated and discussed, fake news and misinformation simultaneously created a corresponding wave of "infodemics." This study analyzed the discourse on Twitter in several languages, investigating the reactions to government and public health agency social media accounts that share policy decisions and official messages. The study collected messages from 21 official Twitter accounts of governments and public health authorities in the UK, US, Mexico, Canada, Brazil, Spain, and Nigeria, from 15 March to 29 May 2020. Over 2 million tweets in various languages were analyzed using a mixed-methods approach to understand the messages both quantitatively and qualitatively. Using automatic, text-based clustering, five topics were identified for each account and then categorized into 10 emerging themes. Identified themes include political, socio-economic, and population-protection issues, encompassing global, national, and individual levels. A comparison was performed amongst the seven countries analyzed and the United Kingdom (Scotland, Northern Ireland, and England) to find similarities and differences between countries and government agencies. Despite the difference in language, country of origin, epidemiological contexts within the countries, significant similarities emerged. Our results suggest that other than general announcement and reportage messages, the most-discussed topic is evidence-based leadership and policymaking, followed by how to manage socio-economic consequences

Assessing causality among topics and sentiments: The case of the G20 discussion on Twitter

Although the identification of topics and sentiments from social media content has attracted substantial research, little work has been carried out on the extraction of causal relationships among those topics and sentiments. This article proposes a methodology aimed at building a causal graph where nodes represent topics and emotions extracted from social media users? posts. To illustrate the proposed methodology, we collected a large multi-year dataset of tweets related to different editions of the G20 summit, which was locally indexed for further analysis. Topic-relevant queries are crafted from phrases extracted by experts from G20 output documents on four main recurring topics, namely government, society, environment and health and economics. Subsequently, sentiments are identified on the retrieved tweets using a lexicon based on Plutchik?s wheel of emotions. Finally, a causality test that uses stochastic dominance is applied to build a causal graph among topics and emotions by exploiting the asymmetries of explaining a variable from other variables. The applied causality discovery process relies on observational data only and does not require any assumptions of linearity, parametric definitions or temporal precedence. In our analysis, we observe that although the time series of topics and emotions always show high correlation coefficients, stochastic causality provides a means to tell apart causal relationships from other forms of associations. The proposed methodology can be applied to better understand social behaviour on social media, offering support to decision and policy making and their communication by government leaders.Fil: Fonseca, Mauro. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Soto, Axel Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

Leveraging social media data using latent dirichlet allocation and naïve bayes for mental health sentiment analytics on Covid-19 pandemic

In Malaysia, during the early stages of the COVID-19 pandemic, the negative impact on mental health became noticeable. The public's psychological and behavioral responses have risen as the COVID-19 outbreak progresses. A high impression of severity, vulnerability, impact, and fear was the element that influenced higher anxiety. Social media data can be used to track Malaysian sentiments in the COVID-19 era. However, it is often found on the internet in text format with no labels, and manually decoding this data is usually complicated. Furthermore, traditional data-gathering approaches, such as filling out a survey form, may not completely capture the sentiments. This study uses a text mining technique called Latent Dirichlet Allocation (LDA) on social media to discover mental health topics during the COVID-19 pandemic. Then, a model is developed using a hybrid approach, combining both lexicon-based and Naïve Bayes classifier. The accuracy, precision, recall, and F-measures are used to evaluate the sentiment classification. The result shows that the best lexicon-based technique is VADER with 72% accuracy compared to TextBlob with 70% accuracy. These sentiments results allow for a better understanding and handling of the pandemic. The top three topics are identified and further classified into positive and negative comments. In conclusion, the developed model can assist healthcare workers and policymakers in making the right decisions in the upcoming pandemic outbreaks

Leveraging Twitter data to analyze the virality of Covid-19 tweets: a text mining approach

As the novel coronavirus spreads across the world, work, pleasure, entertainment, social interactions, and meetings have shifted online. The conversations on social media have spiked, and given the uncertainties and new policies, COVID-19 remains the trending topic on all such platforms, including Twitter. This research explores the factors that affect COVID-19 content-sharing by Twitter users. The analysis was conducted using 57,000 plus tweets that mentioned COVID-19 and related keywords. The tweets were subjected to the Natural Language Processing (NLP) techniques like Topic modelling, Named Entity-Relationship, Emotion & Sentiment analysis, and Linguistic feature extraction. These methods generated features that could help explain the retweet count of the tweets. The results indicate that tweets with named entities (person, organisation, and location), expression of negative emotions (anger, disgust, fear, and sadness), reference to mental health, optimistic content, and greater length have higher chances of being shared (retweeted). On the other hand, tweets with more hashtags and user mentions are less likely to be shared

Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model

IntroductionTo protect citizens during the COVID-19 pandemic unprecedented public health restrictions were imposed on everyday life in the UK and around the world. In emergencies like COVID-19, it is crucial for policymakers to be able to gauge the public response and sentiment to such measures in almost real-time and establish best practices for the use of social media for emergency response.MethodsIn this study, we explored Twitter as a data source for assessing public reaction to the pandemic. We conducted an analysis of sentiment by topic using 25 million UK tweets, collected from 26th May 2020 to 8th March 2021. We combined an innovative combination of sentiment analysis via a recurrent neural network and topic clustering through an embedded topic model.ResultsThe results demonstrated interpretable per-topic sentiment signals across time and geography in the UK that could be tied to specific public health and policy events during the pandemic. Unique to this investigation is the juxtaposition of derived sentiment trends against behavioral surveys conducted by the UK Office for National Statistics, providing a robust gauge of the public mood concurrent with policy announcements.DiscussionWhile much of the existing research focused on specific questions or new techniques, we developed a comprehensive framework for the assessment of public response by policymakers for COVID-19 and generalizable for future emergencies. The emergent methodology not only elucidates the public’s stance on COVID-19 policies but also establishes a generalizable framework for public policymakers to monitor and assess the buy-in and acceptance of their policies almost in real-time. Further, the proposed approach is generalizable as a tool for policymakers and could be applied to further subjects of political and public interest