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

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

Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling

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

Fatores determinantes para a demanda de calçados na cidade do Recife: um estudo da percepção de clientes e gerentes

O ambiente competitivo dos anos 90 marcado pelo processo de abertura comercial, pela sobrevalorização cambial e pelas mudanças nos padrões de concorrência internacional afetou de maneira diferenciada as empresas brasileiras que comercializavam seus produtos. As lojas que fazem parte do setor calçadista passaram a adotar importantes estratégias como meio de manutenção à competitividade no mercado nacional e, também, a fim de atrair cada vez mais consumidores. Sendo assim, Recife considerada uma metrópole regional, não está à margem destas mudanças no setor de calçados. Diante disto, este estudo busca identificar quais são os fatores que influenciam a decisão da escolha da loja pelos clientes. Este trabalho analisa a percepção de 144 clientes em relação às várias lojas de calçados localizadas na cidade do Recife e compara a percepção de nove gerentes em relação aos mesmos fatores. Inicialmente os dados foram obtidos a partir do levantamento bibliográfico, e posteriormente em pesquisa de campo, incluindo as observações e levantamentos das principais variáveis que influenciam o consumidor no momento da compra. Os questionários elaborados e aplicados aos clientes e gerentes foram analisados com o suporte de métodos quantitativos e qualitativos. Os resultados desta investigação mostram que os comerciantes desse setor passaram a se preocupam cada vez mais com os vários aspectos relacionados à forma de organização da loja, ao tipo de mercadoria e de serviços oferecidos e à localização da sua loja, ou seja, fatores que levam os consumidores a escolherem por sua loja ou pela do concorrente. Os resultados também evidenciam que existe uma relação entre o bem estar do consumidor e sua preferência pela loja, pois os clientes buscam conforto e uma vitrine atraente com produtos da moda a um preço que considerem justo. Verificou-se que muitos dos gerentes se posicionaram acima daquilo que os clientes perceberam em relação a aquela loja, ou seja, houve variação na importância atribuída às variáveis entre as distintas amostras. Espera-se que as recomendações desta pesquisa auxiliem as lojas de varejo de calçados quanto ao seu planejamento estratégic

Recruiting from the network:Discovering twitter users who can help combat zika epidemics

Tropical diseases like Chikungunya and Zika have come to prominence in recent years as the cause of serious health problems. We explore the hypothesis that monitoring and analysis of social media content streams may effectively complement institutional disease prevention efforts. Specifically, we aim to identify selected members of the public who are likely to be sensitive to virus combat initiatives. 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. 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.</p

Recruiting from the network:Discovering twitter users who can help combat zika epidemics

Tropical diseases like Chikungunya and Zika have come to prominence in recent years as the cause of serious health problems. We explore the hypothesis that monitoring and analysis of social media content streams may effectively complement institutional disease prevention efforts. Specifically, we aim to identify selected members of the public who are likely to be sensitive to virus combat initiatives. 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. 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.</p

Tracking dengue epidemics using twitter content classification and topic modelling

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.</p

VazaDengue:An information system for preventing and combating mosquito-borne diseases with social networks

Dengue is a disease transmitted by the Aedes Aegypti mosquito, which also transmits the Zika virus and Chikungunya. Unfortunately, the population of different countries has been suffering from the diseases transmitted by this mosquito. The communities should play an important role in combating and preventing the mosquito-borne diseases. However, due to the limited engagement of the population, new solutions need to be used to strengthen the mosquito surveillance. VazaDengue is one of these solutions, which offers the users a web and mobile platform for preventing and combating mosquito-borne diseases. The system relies on social actions of citizens reporting mosquito breeding sites and dengue cases, in which the reports are made available to the community and health agencies. In order to address the limited population engagement, the system proactively monitors social media network as Twitter to enrich the information provided by the system. It processes the natural language text from the network to classify the tweets according to a set of predefined categories. After the classification, the relevant tweets are provided to the users as reports. In this paper, we describe the VazaDengue features including its ability to harvest and classify tweets. Since the VazaDengue system aims to strengthen the entomological surveillance of the mosquito that transmits Dengue, Zika, and Chikungunya by providing geolocated reports, we present here two studies to evaluate its potential contributions. The first evaluation uses a survey conducted in the Brazilian community of health agents. The goal is to evaluate the relevance of the classified tweets according to the health agents’ perspective. The second study compares the official reports of the 2015–2016 epidemic waves in Brazil with the concentration of mosquito-related tweets found by VazaDengue. The goal is to verify if the concentration of tweets can be used for monitoring the mosquito manifestation in big cities. The results of these two evaluations are encouraging. For instance, we have found that the health agents tend to agree with the relevance of the classified tweets. Moreover, the concentration of tweets is likely to be effective for monitoring big cities. The results of these evaluations are helping us to improve the VazaDengue system further. These improvements will make the VazaDengue system even more useful for combating and preventing the mosquito-borne diseases.</p

VazaDengue:An information system for preventing and combating mosquito-borne diseases with social networks

Dengue is a disease transmitted by the Aedes Aegypti mosquito, which also transmits the Zika virus and Chikungunya. Unfortunately, the population of different countries has been suffering from the diseases transmitted by this mosquito. The communities should play an important role in combating and preventing the mosquito-borne diseases. However, due to the limited engagement of the population, new solutions need to be used to strengthen the mosquito surveillance. VazaDengue is one of these solutions, which offers the users a web and mobile platform for preventing and combating mosquito-borne diseases. The system relies on social actions of citizens reporting mosquito breeding sites and dengue cases, in which the reports are made available to the community and health agencies. In order to address the limited population engagement, the system proactively monitors social media network as Twitter to enrich the information provided by the system. It processes the natural language text from the network to classify the tweets according to a set of predefined categories. After the classification, the relevant tweets are provided to the users as reports. In this paper, we describe the VazaDengue features including its ability to harvest and classify tweets. Since the VazaDengue system aims to strengthen the entomological surveillance of the mosquito that transmits Dengue, Zika, and Chikungunya by providing geolocated reports, we present here two studies to evaluate its potential contributions. The first evaluation uses a survey conducted in the Brazilian community of health agents. The goal is to evaluate the relevance of the classified tweets according to the health agents’ perspective. The second study compares the official reports of the 2015–2016 epidemic waves in Brazil with the concentration of mosquito-related tweets found by VazaDengue. The goal is to verify if the concentration of tweets can be used for monitoring the mosquito manifestation in big cities. The results of these two evaluations are encouraging. For instance, we have found that the health agents tend to agree with the relevance of the classified tweets. Moreover, the concentration of tweets is likely to be effective for monitoring big cities. The results of these evaluations are helping us to improve the VazaDengue system further. These improvements will make the VazaDengue system even more useful for combating and preventing the mosquito-borne diseases.</p