208 research outputs found

    Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling

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    Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue and Zika in Brasil and other tropical regions has long been a priority for governments in affected areas. Streaming social media content, such as Twitter, is increasingly being used for health vigilance applications such as flu detection. However, previous work has not addressed the complexity of drastic seasonal changes on Twitter content across multiple epidemic outbreaks. In order to address this gap, this paper contrasts two complementary approaches to detecting Twitter content that is relevant for Dengue outbreak detection, namely supervised classification and unsupervised clustering using topic modelling. Each approach has benefits and shortcomings. Our classifier achieves a prediction accuracy of about 80\% based on a small training set of about 1,000 instances, but the need for manual annotation makes it hard to track seasonal changes in the nature of the epidemics, such as the emergence of new types of virus in certain geographical locations. In contrast, LDA-based topic modelling scales well, generating cohesive and well-separated clusters from larger samples. While clusters can be easily re-generated following changes in epidemics, however, this approach makes it hard to clearly segregate relevant tweets into well-defined clusters.Comment: Procs. SoWeMine - co-located with ICWE 2016. 2016, Lugano, Switzerlan

    Recruiting from the network: discovering Twitter users who can help combat Zika epidemics

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    Tropical diseases like \textit{Chikungunya} and \textit{Zika} have come to prominence in recent years as the cause of serious, long-lasting, population-wide health problems. In large countries like Brasil, traditional disease prevention programs led by health authorities have not been particularly effective. We explore the hypothesis that monitoring and analysis of social media content streams may effectively complement such efforts. Specifically, we aim to identify selected members of the public who are likely to be sensitive to virus combat initiatives that are organised in local communities. Focusing on Twitter and on the topic of Zika, our approach involves (i) training a classifier to select topic-relevant tweets from the Twitter feed, and (ii) discovering the top users who are actively posting relevant content about the topic. We may then recommend these users as the prime candidates for direct engagement within their community. In this short paper we describe our analytical approach and prototype architecture, discuss the challenges of dealing with noisy and sparse signal, and present encouraging preliminary results

    When Infodemic Meets Epidemic: a Systematic Literature Review

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    Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment

    A scoping review of the use of Twitter for public health research

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    Public health practitioners and researchers have used traditional medical databases to study and understand public health for a long time. Recently, social media data, particularly Twitter, has seen some use for public health purposes. Every large technological development in history has had an impact on the behaviour of society. The advent of the internet and social media is no different. Social media creates public streams of communication, and scientists are starting to understand that such data can provide some level of access into the people’s opinions and situations. As such, this paper aims to review and synthesize the literature on Twitter applications for public health, highlighting current re- search and products in practice. A scoping review methodology was employed and four leading health, computer science and cross-disciplinary databases were searched. A total of 755 articles were retreived, 92 of which met the criteria for review. From the reviewed literature, six domains for the application of Twit- ter to public health were identified: (i) Surveillance; (ii) Event Detection; (iii) Pharmacovigilance; (iv) Forecasting; (v) Disease Tracking; and (vi) Geographic Identification. From our review, we were able to obtain a clear picture of the use of Twitter for public health. We gained insights into interesting observa- tions such as how the popularity of different domains changed with time, the diseases and conditions studied and the different approaches to understanding each disease, which algorithms and techniques were popular with each domain, and more

    Data-Centric Epidemic Forecasting: A Survey

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    The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing previously unobservable facets and also due to initiatives from government public health and funding agencies. This has resulted, in particular, in a spate of work on 'data-centered' solutions which have shown potential in enhancing our forecasting capabilities by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This survey delves into various data-driven methodological and practical advancements and introduces a conceptual framework to navigate through them. First, we enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting, capturing various factors like symptomatic online surveys, retail and commerce, mobility, genomics data and more. Next, we discuss methods and modeling paradigms focusing on the recent data-driven statistical and deep-learning based methods as well as on the novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline.Comment: 67 pages, 12 figure

    Public Sentiments towards the COVID-19 Pandemic: Insights from the Academic Literature Review and Twitter Analytics

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    The recent COVID-19 pandemic has severely impacted nations across the globe. Not only has it created economic shocks, but also long-term impacts on the social and psychological behaviors of the public. This can be attributed to the severity of the pandemic and because of the preventive and control measures such as global lockdowns, social distancing, and selfisolation that the governments imposed. Previous studies have reported significant changes in human emotions and behaviors are used to measure public sentiments about certain phenomena (such as the recent pandemic). The present study aims to study the public's sentiments during the COVID-19 outbreak based on an analytics review of public tweets highlighting changes in emotions. A dataset of 58,320 tweets extracted from Twitter and 61 academic articles was explored to analyze behavioral and emotional changes during previous and current pandemic situations. We chose the RPA – COV (Research Process Approach – COVID-19) approach, which was combined with the LBTA (Literature-Based Thematic Analysis) and the COVTA (COVID-19 Twitter Analytics). The sentiments' analysis results were coupled with word-tree analysis and highlighted that the public showed more highly neutral, positive, and mixed emotions than negative ones. The analysis pointed that people may react differently on Twitter as compared to real-life circumstances. The present study makes a significant contribution towards understanding how the public express their sentiments in pandemic situations

    Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification

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    Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years. The ability to detect disease outbreaks earlier than traditional methods would be highly useful for public health officials. This paper describes a software system which is built upon recent developments in machine learning and data processing to achieve this goal. The system is built from reusable modules integrated into data processing pipelines that are easily deployable and configurable. It applies deep learning to the problem of classifying health-related tweets and is able to do so with high accuracy. It has the capability to detect illness outbreaks from Twitter data and then to build up and display information about these outbreaks, including relevant news articles, to provide situational awareness. It also provides nowcasting functionality of current disease levels from previous clinical data combined with Twitter data. The preliminary results are promising, with the system being able to detect outbreaks of influenza-like illness symptoms which could then be confirmed by existing official sources. The Nowcasting module shows that using social media data can improve prediction for multiple diseases over simply using traditional data sources

    Public Sentiments towards the COVID-19 Pandemic: Insights from the Academic Literature Review and Twitter Analytics

    Get PDF
    The recent COVID-19 pandemic has severely impacted nations across the globe. Not only has it created economic shocks, but also long-term impacts on the social and psychological behaviors of the public. This can be attributed to the severity of the pandemic and because of the preventive and control measures such as global lockdowns, social distancing, and selfisolation that the governments imposed. Previous studies have reported significant changes in human emotions and behaviors are used to measure public sentiments about certain phenomena (such as the recent pandemic). The present study aims to study the public's sentiments during the COVID-19 outbreak based on an analytics review of public tweets highlighting changes in emotions. A dataset of 58,320 tweets extracted from Twitter and 61 academic articles was explored to analyze behavioral and emotional changes during previous and current pandemic situations. We chose the RPA – COV (Research Process Approach – COVID-19) approach, which was combined with the LBTA (Literature-Based Thematic Analysis) and the COVTA (COVID-19 Twitter Analytics). The sentiments' analysis results were coupled with word-tree analysis and highlighted that the public showed more highly neutral, positive, and mixed emotions than negative ones. The analysis pointed that people may react differently on Twitter as compared to real-life circumstances. The present study makes a significant contribution towards understanding how the public express their sentiments in pandemic situations
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