A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Introduction <s> In a rapidly changing environment, national institutions in charge of health security can no longer rely only on traditional disease reporting mechanisms that are not designed to recognise emergence of new hazards. Epidemic intelligence provides a conceptual framework within which countries may adapt their public health surveillance system to meet new challenges. ::: Epidemic intelligence (EI) encompasses all activities related to early identification of potential health hazards, their verification, assessment and investigation in order to recommend public health control measures. EI integrates both an indicator-based and an event-based component. ‘Indicator-based component’ refers to structured data collected through routine surveillance systems. ‘Event-based component’ refers to unstructured data gathered from sources of intelligence of any nature. ::: All EU member states have long-established disease surveillance systems that provide proper indicator-based surveillance. For most countries, the challenge lies now in developing and structuring the event-based component of EI within national institution in charge of public health surveillance. ::: In May 2006, the European Union member states committed to comply with provisions of the revised International Health Regulations (IHR(2005)) considered relevant to the risk posed by avian and potential human pandemic influenza. This provides for the European Centre for Disease Prevention and Control (ECDC) with an opportunity to guide member states in developing and/or strengthening their national EI , in addition to the ECDC’s task of developing an EI system for the EU. <s> BIB001 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Introduction <s> Summary: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles. ::: ::: Availability: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org. ::: ::: Contact: pj.ca.iin@reilloc <s> BIB002 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Introduction <s> Disease surveillance has been practiced for decades and continues to be an indispensable approach for detecting emerging disease outbreaks and epidemics. Early knowledge of a disease outbreak plays an important role in improving response effectiveness. In this article we presents the application of AI for the global disease surveillance systems. This also provides in depth survey that analyzes and evaluates existing syndromic surveillance system and related outbreak modelling and detection work under a unified framework. Many intelligent global disease surveillance systems have begun to emerge. To illustrate, we present the following study. <s> BIB003 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Introduction <s> Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this paper, we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm. Our results from extensive simulations on several real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond million-sized graphs where the greedy algorithm becomes infeasible, and (b) in all size ranges, our algorithm performs consistently well in influence spread --- it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100%--260% increase in influence spread. <s> BIB004 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Introduction <s> In recent years, social media has become ubiquitous and important for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped. In this paper, we demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors. We further demonstrate how sentiments extracted from Twitter can be utilized to improve the forecasting power of social media. <s> BIB005 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Introduction <s> This paper explores Technosocial Predictive Analytics (TPA) and related methods for Web ''data mining'' where users' posts and queries are garnered from Social Web (''Web 2.0'') tools such as blogs, micro-blogging and social networking sites to form coherent representations of real-time health events. The paper includes a brief introduction to commonly used Social Web tools such as mashups and aggregators, and maps their exponential growth as an open architecture of participation for the masses and an emerging way to gain insight about people's collective health status of whole populations. Several health related tool examples are described and demonstrated as practical means through which health professionals might create clear location specific pictures of epidemiological data such as flu outbreaks. <s> BIB006 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Introduction <s> Real world pandemics such as SARS 2002 as well as popular fiction like the movie Contagion graphically depict the health threat of a global pandemic and the key role of epidemic intelligence (EI). While EI relies heavily on established indicator sources a new class of methods based on event alerting from unstructured digital Internet media is rapidly becoming acknowledged within the public health community. At the heart of automated information gathering systems is a technology called text mining. My contribution here is to provide an overview of the role that text mining technology plays in detecting epidemics and to synthesise my existing research on the BioCaster project. <s> BIB007 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Introduction <s> Social Networks have become an important environment for Collective Trends extraction. The interactions amongst users provide information of their preferences and relationships. This information can be used to measure the influence of ideas, or opinions, and how they are spread within the Network. Currently, one of the most relevant and popular Social Network is Twitter. This Social Network was created to share comments and opinions. The information provided by users is specially useful in different fields and research areas such as marketing. This data is presented as short text strings containing different ideas expressed by real people. With this representation, different Data Mining and Text Mining techniques (such as classification and clustering) might be used for knowledge extraction trying to distinguish the meaning of the opinions. This work is focused on the analysis about how these techniques can interpret these opinions within the Social Network using information related to IKEA® company. <s> BIB008
Web is one of the most important sources of data in the world producing amounts of public information. The exponentially increasing of websites and online web services in the last years has allowed new interdisciplinary challenges for several fields and computer science, such as marketing campaigns BIB004 BIB008 , financial prediction BIB005 or public healthcare BIB007 [17] BIB003 , among others. Recently, the research on artificial intelligence techniques applied to develop technologies allowing monitoring web data sources for detecting public health events has been emerged as a new relevant discipline called Epidemic Intelligence (EI). EI can be defined as the early identification, assessment and verification of potential public health risks BIB001 , and the timely dissemination of the appropriate alerts. This discipline includes surveillance techniques such as automated and continuous analysis of unstructured free text information available on Web from social networks, blogs, digital news media or official sources. Surveillance systems are nowadays widely used by public health organizations such as World Health Organization (WHO) or the European Centre for Disease Prevention and Control (ECDC)  . Tracking and monitoring mechanisms for early detection are critical in reducing the impact of epidemics giving a rapid response. For instance, several of these systems can be able to discover early events of the disease breakout during the A(H1N1) influenza pandemic in 2009 BIB002 . Traditional epidemic surveillance systems are implemented from virology and clinical data, which is manually collected, and often these traditional systems have a delay reporting the emerging diseases. But in situations like epidemic outbreaks, real-time feedback and a rapid response is critical. Social Web media is a profitable medium to extract the society opinion in real time. Blogs, micro-blogs (Twitter), and social networks (Facebook) enable people to publish their personal opinions in real time, including geo-information about their current locations. These big data with situation and context aware information about the users provide a useful source for public healthcare. However, the extraction of information from web is a difficult task due to its unstructured definition, high heterogeneity, and dynamically changing nature. Because of this diversity in the data format, several computational methods are required for its processing and analysing BIB006 (data mining, natural language processes (NLP), knowledge extraction, context awareness, etc...). This paper presents a survey on current data mining applications and web systems based on web data for public healthcare over the last years. It tries to take special attention to machine learning and data mining techniques, and how they have been applied to these web data to extract collective knowledge from social networks like Twitter. The rest of the paper has been structured as follows: Section 2 shows the state of the art of the existing Epidemic Intelligence Systems. Section 3 describes the different web mining techniques used to detect disease outbreaks. Section 4 provides an overview of Twitter applications for monitoring and predicting epidemic and their experimental results. Finally, the last section presents a discussion of the main features extracted from this survey.