Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses

Systems that exploit publicly available user generated content such as Twitter messages have been successful in tracking seasonal influenza. We developed a novel filtering method for Influenza-Like-Illnesses (ILI)-related messages using 587 million messages from Twitter micro-blogs. We first filtered messages based on syndrome keywords from the BioCaster Ontology, an extant knowledge model of laymen's terms. We then filtered the messages according to semantic features such as negation, hashtags, emoticons, humor and geography. The data covered 36 weeks for the US 2009 influenza season from 30th August 2009 to 8th May 2010. Results showed that our system achieved the highest Pearson correlation coefficient of 98.46% (p-value<2.2e-16), an improvement of 3.98% over the previous state-of-the-art method. The results indicate that simple NLP-based enhancements to existing approaches to mine Twitter data can increase the value of this inexpensive resource.Comment: 10 pages, 5 figures, IEEE HISB 2012 conference, Sept 27-28, 2012, La Jolla, California, U

Advances in nowcasting influenza-like illness rates using search query logs

User-generated content can assist epidemiological surveillance in the early detection and prevalence estimation of infectious diseases, such as influenza. Google Flu Trends embodies the first public platform for transforming search queries to indications about the current state of flu in various places all over the world. However, the original model significantly mispredicted influenza-like illness rates in the US during the 2012–13 flu season. In this work, we build on the previous modeling attempt, proposing substantial improvements. Firstly, we investigate the performance of a widely used linear regularized regression solver, known as the Elastic Net. Then, we expand on this model by incorporating the queries selected by the Elastic Net into a nonlinear regression framework, based on a composite Gaussian Process. Finally, we augment the query-only predictions with an autoregressive model, injecting prior knowledge about the disease. We assess predictive performance using five consecutive flu seasons spanning from 2008 to 2013 and qualitatively explain certain shortcomings of the previous approach. Our results indicate that a nonlinear query modeling approach delivers the lowest cumulative nowcasting error, and also suggest that query information significantly improves autoregressive inferences, obtaining state-of-the-art performance

Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data

The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication and encryption, that are made necessary by this development. Thus, industrial IT-security is needed. In this work, emulated industrial network data is transformed into a time series and analysed with three different algorithms. The data contains labeled attacks, so the performance can be evaluated. Matrix Profiles perform well with almost no parameterisation needed. Seasonal Autoregressive Integrated Moving Average performs well in the presence of noise, requiring parameterisation effort. Long Short Term Memory-based neural networks perform mediocre while requiring a high training- and parameterisation effort.Comment: Extended version of a publication in the 2018 IEEE International Conference on Data Mining Workshops (ICDMW

Enhancing Feature Selection Using Word Embeddings: The Case of Flu Surveillance

Health surveillance systems based on online user-generated content often rely on the identification of textual markers that are related to a target disease. Given the high volume of available data, these systems benefit from an automatic feature selection process. This is accomplished either by applying statistical learning techniques, which do not consider the semantic relationship between the selected features and the inference task, or by developing labour-intensive text classifiers. In this paper, we use neural word embeddings, trained on social media content from Twitter, to determine, in an unsupervised manner, how strongly textual features are semantically linked to an underlying health concept. We then refine conventional feature selection methods by a priori operating on textual variables that are sufficiently close to a target concept. Our experiments focus on the supervised learning problem of estimating influenza-like illness rates from Google search queries. A "flu infection" concept is formulated and used to reduce spurious and potentially confounding features that were selected by previously applied approaches. In this way, we also address forms of scepticism regarding the appropriateness of the feature space, alleviating potential cases of overfitting. Ultimately, the proposed hybrid feature selection method creates a more reliable model that, according to our empirical analysis, improves the inference performance (Mean Absolute Error) of linear and nonlinear regressors by 12% and 28.7%, respectively