Automated Fact Checking in the News Room

Fact checking is an essential task in journalism; its importance has been highlighted due to recently increased concerns and efforts in combating misinformation. In this paper, we present an automated fact-checking platform which given a claim, it retrieves relevant textual evidence from a document collection, predicts whether each piece of evidence supports or refutes the claim, and returns a final verdict. We describe the architecture of the system and the user interface, focusing on the choices made to improve its user-friendliness and transparency. We conduct a user study of the fact-checking platform in a journalistic setting: we integrated it with a collection of news articles and provide an evaluation of the platform using feedback from journalists in their workflow. We found that the predictions of our platform were correct 58\% of the time, and 59\% of the returned evidence was relevant

GPCRTree: online hierarchical classification of GPCR function

Background: G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence. Findings: Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level. Conclusion: A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree

Architecture of a Scalable, Secure and Resilient Translation Platform for Multilingual News Media

This paper presents an example architecture for a scalable, secure and resilient Machine Translation (MT) platform, using components available via Amazon Web Services (AWS). It is increasingly common for a single news organisation to publish and monitor news sources in multiple languages. A growth in news sources makes this increasingly challenging and time-consuming but MT can help automate some aspects of this process. Building a translation service provides a single integration point for news room tools that use translation technology allowing MT models to be integrated into a system once, rather than each time the translation technology is needed. By using a range of services provided by AWS, it is possible to architect a platform where multiple pre-existing technologies are combined to build a solution, as opposed to developing software from scratch for deployment on a single virtual machine. This increases the speed at which a platform can be developed and allows the use of well-maintained services. However, a single service also provides challenges. It is key to consider how the platform will scale when handling many users and how to ensure the platform is resilient

Methods of a national colorectal cancer cohort study: the PIPER Project

A national study looking at bowel cancer in New Zealand has previously been completed (the PIPER Project). The study included 5,610 patients and collected medical information about how each person was found to have bowel cancer and the treatment they received. This paper reports how the study was carried out. The information collected in the study will be used to look at the quality of care being provided to New Zealand patients with bowel cancer, and to find out if differences in care occur based on where people live, their ethnicity and their socioeconomic status

Perseverative Cognition and Health Behaviors: A Systematic Review and Meta-Analysis

Recent developments in stress theory have emphasized the significance of perseverative cognition (worry and rumination) in furthering our understanding of stress-disease relationships. Substantial evidence has shown that perseverative cognition (PC) is associated with somatic outcomes and numerous physiological concomitants have been identified (i.e., cardiovascular, autonomic, and endocrine nervous system activity parameters). However, there has been no synthesis of the evidence regarding the association between PC and health behaviors. This is important given such behaviors may also directly and/or indirectly influence health and disease outcomes (triggered by PC). Therefore, the aim of the current review was to synthesize available studies that have explored the relationship between worry and rumination and health behaviors (health risk: behaviors which, if performed, would be detrimental to health; health promoting: behaviors which, if performed, would be beneficial for health). A systematic review and meta-analyses of the literature were conducted. Studies were included in the review if they reported the association between PC and health behavior. Studies identified in MEDLINE or PsycINFO (k = 7504) were screened, of which 19 studies met the eligibility criteria. Random-effects meta-analyses suggested increased PC was generally associated with increased health risk behaviors but not health promoting behaviors. Further analyses indicated that increases in rumination (r = 0.122), but not reflection (r = -0.080), or worry (r = 0.048) were associated with health risk behaviors. In conclusion, these results showed that increases in PC are associated with increases in health risk behaviors (substance use, alcohol consumption, unhealthy eating, and smoking) that are driven primarily through rumination. These findings provide partial support for our hypothesis that in Brosschot et al.'s (2006) original perseverative cognition hypothesis, there may be scope for additional routes to pathogenic disease via poorer health behaviors

WAIRS: Improving Classification Accuracy by Weighting Attributes in the AIRS Classifier

AIRS (Artificial Immune Recognition System) has shown itself to be a competitive classifier. It has also proved to be the most popular immune inspired classifier. However, rather than AIRS being a classifier in its own right as previously described, we see AIRS more as a pre-processor to a KNN classifier. It is our view that by not explicitly classing it as such development of this algorithm has been rather held back. Seeing it as a pre-processor allows inspiration to be taken from the machine learning literature where such pre-processors are not uncommon. With this in mind, this paper takes a core feature of many such pre-processors, that of attribute weighting, and applies it to AIRS. The resultant algorithm called WAIRS (Weighted AIRS) uses a weighted distance function during all affinity evaluations. WAIRS is tested on 9 benchmark datasets and is found to outperform AIRS in the majority of cases

An experimental comparison of classification algorithms for hierarchical prediction of protein function

Some data can be naturally organised as a hierarchy of classes. The classification of data in such a hierarchy poses some unique challenges to data miners such the need for classification at different levels, which may require the use of different characteristics of the data. One particular case of hierarchical classification is the classification of GPCR proteins by their function. G-Protein Coupled Receptor