Classifying and Summarizing Information from Microblogs During Epidemics

When Infodemic Meets Epidemic: Systematic Literature Review

BACKGROUND

Epidemics and outbreaks present arduous challenges, requiring both individual and communal efforts. The significant medical, emotional, and financial burden associated with epidemics creates feelings of distrust, fear, and loss of control, making vulnerable populations prone to exploitation and manipulation through misinformation, rumors, and conspiracies. The use of social media sites has increased in the last decade. As a result, significant amounts of public data can be leveraged for biosurveillance. Social media sites can also provide a platform to quickly and efficiently reach a sizable percentage of the population; therefore, they have a potential role in various aspects of epidemic mitigation.

OBJECTIVE

This systematic literature review aimed to provide a methodical overview of the integration of social media in 3 epidemic-related contexts: epidemic monitoring, misinformation detection, and the relationship with mental health. The aim is to understand how social media has been used efficiently in these contexts, and which gaps need further research efforts.

METHODS

Three research questions, related to epidemic monitoring, misinformation, and mental health, were conceptualized for this review. In the first PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) stage, 13,522 publications were collected from several digital libraries (PubMed, IEEE Xplore, ScienceDirect, SpringerLink, MDPI, ACM, and ACL) and gray literature sources (arXiv and ProQuest), spanning from 2010 to 2022. A total of 242 (1.79%) papers were selected for inclusion and were synthesized to identify themes, methods, epidemics studied, and social media sites used.

RESULTS

Five main themes were identified in the literature, as follows: epidemic forecasting and surveillance, public opinion understanding, fake news identification and characterization, mental health assessment, and association of social media use with psychological outcomes. Social media data were found to be an efficient tool to gauge public response, monitor discourse, identify misleading and fake news, and estimate the mental health toll of epidemics. Findings uncovered a need for more robust applications of lessons learned from epidemic "postmortem documentation." A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies.

CONCLUSIONS

Harnessing the full potential of social media in epidemic-related tasks requires streamlining the results of epidemic forecasting, public opinion understanding, and misinformation detection, all while keeping abreast of potential mental health implications. Proactive prevention has thus become vital for epidemic curtailment and containment.

The Saudi Ministries Twitter communication strategies during the COVID-19 pandemic: A qualitative content analysis study

Objectives

To understand government communication strategies during the COVID-19 pandemic by examining topics related to COVID-19 posted by Saudi governmental ministries on Twitter and situating our findings within existing health behavior theoretical frameworks.

Study design

Retrospective content analysis of COVID-19 related tweets.

Methods

On November 7th, 2020, we extracted relevant tweets posted by five Saudi governmental ministries. After we extracted the data, we developed and applied a coding schema.

Results

A total of 3,950 tweets were included in our dataset. Topics fell into two groups: disease-related (49.2%) and non-disease related (50.8%). The disease-related group included seven categories: awareness (18.5%), symptom (0.6%), prevention (7.7%), disease transmission (1.9%), treatment (0.3%), testing (3.4%), and reports (16.7%). The non-disease related group included eight categories: lockdown (5.9%), online learning (12.8%), digital platforms (4.3%), empowerment (12.0%), accountability (1.1%), non-disease reports (2.1%), local and international news (10.8%), and general statements (1.9%). Based on the correlation analysis, we found that the top positively correlated categories were: "testing" and "digital platforms" (r = 0.4157), "awareness" and "prevention" (r = 0.3088), "prevention" and "disease transmission" (r = 0.3025), "awareness" and "disease transmission" (r = 0.1685), "symptom" and "testing" (r = 0.1081), "awareness" and "symptom" (r = 0.0812), "symptom" and "digital platforms" (r = 0.0645), and "disease transmission" and "digital platforms" (r = 0.0450), p-values < 0.01. Several health behavior theoretical constructs were linked to our findings.

Conclusions

Integrating behavioral theories in the development of health risk communication should be taken seriously by government communication specialists who manage social media accounts, as these theories help underlining determinants of people's behaviors.

A Transformer-Based Model for Evaluation of Information Relevance in Online Social-Media: A Case Study of Covid-19 Media Posts

Online social media has become a major source of information gathering for a huge section of society. As the amount of information flows in online social media is enormous but on the other hand, the fact-checking sources are limited. This shortfall of fact-checking gives birth to the problem of misinformation and disinformation in the case of the truthfulness of facts on online social media which can have serious effects on the wellbeing of society. This problem of misconception becomes more rapid and critical when some events like the recent outbreak of Covid-19 happen when there is no or very little information is available anywhere. In this scenario, the identification of the content available online which is mostly propagated from person to person and not by any governing authority is very needed at the hour. To solve this problem, the information available online should be verified properly before being conceived by any individual. We propose a scheme to classify the online social media posts (Tweets) with the help of the BERT (Bidirectional Encoder Representations from Transformers)-based model. Also, we compared the performance of the proposed approach with the other machine learning techniques and other State of the art techniques available. The proposed model not only classifies the tweets as relevant or irrelevant, but also creates a set of topics by which one can identify a text as relevant or irrelevant to his/her need just by just matching the keywords of the topic. To accomplish this task, after the classification of the tweets, we apply a possible topic modelling approach based on latent semantic analysis and latent Dirichlet allocation methods to identify which of the topics are mostly propagated as false information.

A methodology for managing public spaces to increase access to essential goods and services by vulnerable populations during the COVID-19 pandemic

Purpose

The purpose of this paper is to present a spatial decision support system (SDSS) to be used by the local authorities of a city in the planning and response phase of a disaster. The SDSS focuses on the management of public spaces as a resource to increase a vulnerable population’s accessibility to essential goods and services. Using a web-based platform, the SDSS would support data-driven decisions, especially for cases such as the COVID-19 pandemic which requires special care in quarantine situations (which imply walking access instead of by other means of transport).

Design/methodology/approach

This paper proposes a methodology to create a web-SDSS to manage public spaces in the planning and response phase of a disaster to increase the access to essential goods and services. Using a regular polygon grid, a city is partitioned into spatial units that aggregate spatial data from open and proprietary sources. The polygon grid is then used to compute accessibility, vulnerability and population density indicators using spatial analysis. Finally, a facility location problem is formulated and solved to provide decision-makers with an adaptive selection of public spaces given their indicators of choice.

Findings

The design and implementation of the methodology resulted in a granular representation of the city of Lima, Peru, in terms of population density, accessibility and vulnerability. Using these indicators, the SDSS was deployed as a web application that allowed decision-makers to explore different solutions to a facility location model within their districts, as well as visualizing the indicators computed for the hexagons that covered the district’s area. By performing tests with different local authorities, improvements were suggested to support a more general set of decisions and the key indicators to use in the SDSS were determined.

Originality/value

This paper, following the literature gap, is the first of its kind that presents an SDSS focused on increasing access to essential goods and services using public spaces and has had a successful response from local authorities with different backgrounds regarding the integration into their decision-making process.

Accuracy of health-related information regarding COVID-19 on Twitter during a global pandemic

This study was performed to analyze the accuracy of health-related information on Twitter during the coronavirus disease 2019 (COVID-19) pandemic. Authors queried Twitter on three dates for information regarding COVID-19 and five terms (cure, emergency or emergency room, prevent or prevention, treat or treatments, vitamins or supplements) assessing the first 25 results with health-related information. Tweets were authoritative if written by governments, hospitals, or physicians. Two physicians assessed each tweet for accuracy. Metrics were compared between accurate and inaccurate tweets using χ ² analysis and Mann-Whitney U. A total of 25.4% of tweets were inaccurate. Accurate tweets were more likely written by Twitter authenticated authors (49.8% vs. 20.9%, 28.9% difference, 95% confidence interval [CI]: 17.7-38.2) with accurate tweet authors having more followers (19,491 vs. 7346; 3446 difference, 95% CI: 234-14,054) versus inaccurate tweet authors. Likes, retweets, tweet length, botometer scores, writing grade level, and rank order did not differ between accurate and inaccurate tweets. We found 1/4 of health-related COVID-19 tweets inaccurate indicating that the public should not rely on COVID-19 health information written on Twitter. Ideally, improved government regulatory authority, public/private industry oversight, independent fact-checking, and artificial intelligence algorithms are needed to ensure inaccurate information on Twitter is removed.

The Use of Social Media for Health Research Purposes: Scoping Review

BACKGROUND

As social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. However, social media features, methodologies, and ethical issues are unclear so far because, to our knowledge, there has been no overview of this relatively young field of research.

OBJECTIVE

This scoping review aimed to provide an evidence map of the different uses of social media for health research purposes, their fields of application, and their analysis methods.

METHODS

We followed the scoping review methodologies developed by Arksey and O'Malley and the Joanna Briggs Institute. After developing search strategies based on keywords (eg, social media, health research), comprehensive searches were conducted in the PubMed/MEDLINE and Web of Science databases. We limited the search strategies to documents written in English and published between January 1, 2005, and April 9, 2020. After removing duplicates, articles were screened at the title and abstract level and at the full text level by two independent reviewers. One reviewer extracted data, which were descriptively analyzed to map the available evidence.

RESULTS

After screening 1237 titles and abstracts and 407 full texts, 268 unique papers were included, dating from 2009 to 2020 with an average annual growth rate of 32.71% for the 2009-2019 period. Studies mainly came from the Americas (173/268, 64.6%, including 151 from the United States). Articles used machine learning or data mining techniques (60/268) to analyze the data, discussed opportunities and limitations of the use of social media for research (59/268), assessed the feasibility of recruitment strategies (45/268), or discussed ethical issues (16/268). Communicable (eg, influenza, 40/268) and then chronic (eg, cancer, 24/268) diseases were the two main areas of interest.

CONCLUSIONS

Since their early days, social media have been recognized as resources with high potential for health research purposes, yet the field is still suffering from strong heterogeneity in the methodologies used, which prevents the research from being compared and generalized. For the field to be fully recognized as a valid, complementary approach to more traditional health research study designs, there is now a need for more guidance by types of applications of social media for health research, both from a methodological and an ethical perspective.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2020-040671.

Evaluating the motivation of Red Cross Health volunteers in the COVID-19 pandemic: a mixed-methods study protocol

INTRODUCTION

Voluntary organisations provide essential support to vulnerable populations and front-line health responders to the COVID-19 pandemic. The French Red Cross (FRC) is prominent among organisations offering health and support services in the current crisis. Comprised primarily of lay volunteers and some trained health workers, FRC volunteers in the Paris (France) region have faced challenges in adapting to pandemic conditions, working with sick and vulnerable populations, managing limited resources and coping with high demand for their services. Existing studies of volunteers focus on individual, social and organisational determinants of motivation, but attend less to contextual ones. Public health incertitude about the COVID-19 pandemic is an important feature of this pandemic. Whether and how uncertainty interacts with volunteer understandings and experiences of their work and organisational relations to contribute to Red Cross worker motivation is the focus of this investigation.

METHODS AND ANALYSIS

This mixed-methods study will investigate volunteer motivation using ethnographic methods and social network listening. Semi-structured interviews and observations will illuminate FRC volunteer work relations, experiences and concerns during the pandemic. A questionnaire targeting a sample of Paris region volunteers will allow quantification of motivation. These findings will iteratively shape and be influenced by a social media (Twitter) analysis of biomedical and public health uncertainties and debates around COVID-19. These tweets provide insight into a French lay public's interpretations of these debates. We evaluate whether and how socio-political conditions and discourses concerning COVID-19 interact with volunteer experiences, working conditions and organisational relations to influence volunteer motivation. Data collection began on 15 June 2020 and will continue until 15 April 2021.

ETHICS AND DISSEMINATION

The protocol has received ethical approval from the Institut Pasteur Institutional Review Board (no 2020-03). We will disseminate findings through peer-reviewed articles, conference presentations and recommendations to the FRC.