A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Twitter Applications for Tracking and Monitoring Epidemics <s> We explore the hypothesis that social media such as Twitter encodes the belief of a large number of people about some concrete statement about the world. Here, these beliefs are aggregated using a Prediction Market specifically concerning the possibility of a Swine Flu Pandemic in 2009. Using a regression framework, we are able to show that simple features extracted from Tweets can reduce the error associated with modelling these beliefs. Our approach is also shown to outperform some baseline methods based purely on time-series information from the Market. <s> BIB001 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Twitter Applications for Tracking and Monitoring Epidemics <s> Rapid response to a health epidemic is critical to reduce loss of life. Existing methods mostly rely on expensive surveys of hospitals across the country, typically with lag times of one to two weeks for influenza reporting, and even longer for less common diseases. In response, there have been several recently proposed solutions to estimate a population's health from Internet activity, most notably Google's Flu Trends service, which correlates search term frequency with influenza statistics reported by the Centers for Disease Control and Prevention (CDC). In this paper, we analyze messages posted on the micro-blogging site Twitter.com to determine if a similar correlation can be uncovered. We propose several methods to identify influenza-related messages and compare a number of regression models to correlate these messages with CDC statistics. Using over 500,000 messages spanning 10 weeks, we find that our best model achieves a correlation of .78 with CDC statistics by leveraging a document classifier to identify relevant messages. <s> BIB002 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Twitter Applications for Tracking and Monitoring Epidemics <s> With the recent rise in popularity and scale of social media, a growing need exists for systems that can extract useful information from huge amounts of data. We address the issue of detecting influenza epidemics. First, the proposed system extracts influenza related tweets using Twitter API. Then, only tweets that mention actual influenza patients are extracted by the support vector machine (SVM) based classifier. The experiment results demonstrate the feasibility of the proposed approach (0.89 correlation to the gold standard). Especially at the outbreak and early spread (early epidemic stage), the proposed method shows high correlation (0.97 correlation), which outperforms the state-of-the-art methods. This paper describes that Twitter texts reflect the real world, and that NLP techniques can be applied to extract only tweets that contain useful information. <s> BIB003 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Twitter Applications for Tracking and Monitoring Epidemics <s> Social media is producing massive amounts of data on an unprecedented scale. Here people share their experiences and opinions on various topics, including personal health issues, symptoms, treatments, side-effects, and so on. This makes publicly available social media data an invaluable resource for mining interesting and actionable healthcare insights. In this paper, we describe a novel real-time flu and cancer surveillance system that uses spatial, temporal, and text mining on Twitter data. The real-time analysis results are reported visually in terms of US disease surveillance maps, distribution and timelines of disease types, symptoms, and treatments, in addition to overall disease activity timelines on our project website. Our surveillance system can be very useful not only for early prediction of seasonal disease outbreaks such as flu, but also for monitoring distribution of cancer patients with different cancer types and symptoms in each state and the popularity of treatments used. The resulting insights are expected to help facilitate faster response to and preparation for epidemics and also be very useful for both patients and doctors to make more informed decisions. <s> BIB004 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Twitter Applications for Tracking and Monitoring Epidemics <s> Data mining social media has become a valuable resource for infectious disease surveillance. However, there are considerable risks associated with incorrectly predicting an epidemic. The large amount of social media data combined with the small amount of ground truth data and the general dynamics of infectious diseases present unique challenges when evaluating model performance. In this paper, we look at several methods that have been used to assess influenza prevalence using Twitter. We then validate them with tests that are designed to avoid and illustrate issues with the standard k-fold cross validation method. We also find that small modifications to the way that data are partitioned can have major effects on a model's reported performance. <s> BIB005
The increasing popularity and use of micro-blogging services such as Twitter are recently a new valuable data source for web-based surveillance because of its message volume and frequency. Twitter users may post about an illness, and their relationships in the network can give us information about which people could be in contact with. Furthermore, user posts retrieved from the public Twitter API can come with GPS-based location tags, which can be used to detect potential disease outbreaks for a health surveillance system. Recently, several works have already appeared shown the potential of Twitter messages to track and predict disease outbreaks. Ritterman et al. BIB001 work is focused on using prediction market to model public belief about the possibility that H1N1 virus will become a pandemic. In order to forecast the future prices of the prediction market, they decided to use the Support Vector Machine algorithm to carry out regression. A document classifier to identify relevant messages is presented in Culotta et al. paper BIB002 . In this work, Twitter messages related to flu were recollected during 10 weeks using keywords such as flu, cough, sore throat or headache. Then, several classification systems based on different regression models to correlate these messages with CDC statistics were compared, finding that the best model achieves a correlation of 0.78 (simple model regression). Aramaki et al. BIB003 presents a comparative study of various machine-learning methods to classify tweets related to influenza into two categories: positive or negative. Their experimental results show that SVM model using a polynomial kernel achieves the highest accuracy (FMeasure of 0.756) and the lowest training time. A novel real-time surveillance system to detect cancer and flu is described in paper BIB004 . The proposed system continuously extracts text related the two specific diseases from twitter using Twitter streaming API and applies spatial, temporal, and text mining to discover disease-related activities. The output of the three models is summarized as pie charts, time-series graphs, and US disease activity maps on the project website as can be seen in Figure 5 . This system can be useful not only for early prediction of disease outbreaks, but also for monitoring distribution of different cancer types and the effectiveness of the treatments used. Well known regression models are evaluated on their ability to assess disease outbreaks from tweets in Bodnar et al. BIB005 . Regression methods such as Linear, Multivariable an SVM, are applied to the raw count of tweets that contain at least one of the keywords related to a specific disease, in this case "flu". The results confirmed that even using irrelevant tweets and randomly generated datasets, regression methods were able to assess disease levels comparatively well. Finally a summary of all the systems mentioned and the machine learning techniques used is listed in Table 2 . It can be noticed that most of the works use regression models, and are usually focused on detecting influenza outbreaks.