Automatic Extraction of News Values from Headline Text

Headlines play a crucial role in attracting audiences’ attention to online artefacts (e.g. news articles, videos, blogs). The ability to carry out an automatic, largescale analysis of headlines is critical to facilitate the selection and prioritisation of a large volume of digital content. In journalism studies news content has been extensively studied using manually annotated news values – factors used implicitly and explicitly when making decisions on the selection and prioritisation of news items. This paper presents the first attempt at a fully automatic extraction of news values from headline text. The news values extraction methods are applied on a large headlines corpus collected from The Guardian, and evaluated by comparing it with a manually annotated gold standard. A crowdsourcing survey indicates that news values affect people’s decisions to click on a headline, supporting the need for an automatic news values detection

Finding relevant free-text radiology reports at scale with IBM Watson Content Analytics: a feasibility study in the UK NHS

Background. Significant amounts of health data are stored as free-text within clinical reports, letters, discharge summaries and notes. Busy clinicians have limited time to read such large amounts of free-text and are at risk of information overload and consequently missing information vital to patient care. Automatically identifying relevant information at the point of care has the potential to reduce these risks but represents a considerable research challenge. One software solution that has been proposed in industry is the IBM Watson analytics suite which includes rule-based analytics capable of processing large document collections at scale. Results. In this paper we present an overview of IBM Watson Content Analytics and a feasibility study using Content Analytics with a large-scale corpus of clinical free-text reports within a UK National Health Service (NHS) context. We created dictionaries and rules for identifying positive incidence of hydronephrosis and brain metastasis from 5.6m radiology reports and were able to achieve 94% precision, 95% recall and 89% precision, 94% recall respectively on a sample of manually annotated reports. With minor changes for US English we applied the same rule set to an open access corpus of 0.5m radiology reports from a US hospital and achieved 93% precision, 94% recall and 84% precision, 88% recall respectively. Conclusions. We were able to implement IBM Watson within a UK NHS context and demonstrate effective results that could provide clinicians with an automatic safety net which highlights clinically important information within free-text documents. Our results suggest that currently available technologies such as IBM Watson Content Analytics already have the potential to address information overload and improve clinical safety and that solutions developed in one hospital and country may be transportable to different hospitals and countries. Our study was limited to exploring technical aspects of the feasibility of one industry solution and we recognise that healthcare text analytics research is a fast moving field. That said, we believe our study suggests that text analytics is sufficiently advanced to be implemented within industry solutions that can improve clinical safety

Headlines data for social media popularity prediction

This dataset is part of a larger project on using headlines to predict the social media popularity of news articles. The dataset consists of two headlines corpora -- The Guardian and New York Times -- collected in 2014 using news outlet APIs. Each corpus includes a unique headline identifier (to enable recreating the corpus by querying the relevant API), the extracted features (news values, style, metadata), and the corresponding popularity on Twitter and Facebook

Cross-sectional analysis to explore the awareness, attitudes and actions of UK adults at high risk of severe illness from COVID-19

OBJECTIVES: This study explored the impact of COVID-19 on people identified as at high risk of severe illness by UK government, and in particular, the impact of lockdown on access to healthcare, medications and use of technological platforms. DESIGN: Online survey methodology. SETTING: UK. PARTICIPANTS: 1038 UK adults were recruited who were either identified by UK government as at high risk of severe illness from COVID-19 or self-identified as at high risk with acute or other chronic health conditions not included in the UK government list. Participants were recruited through social media advertisements, health charities and patient organisations. MAIN OUTCOME MEASURES: The awareness, attitudes and actions survey which explores the impact of COVID-19, on including access to healthcare, use of technology for health condition management, mental health, depression, well-being and lifestyle behaviours. RESULTS: Nearly half of the sample (44.5%) reported that their mental health had worsened during the COVID-19 lockdown. Management of health conditions changed including access to medications (28.5%) and delayed surgery (11.9%), with nearly half of the sample using telephone care (45.5%). Artificial Intelligence identified that participants in the negative cluster had higher neuroticism, insecurity and negative sentiment. Participants in this cluster reported more negative impacts on lifestyle behaviours, higher depression and lower well-being, alongside lower satisfaction with platforms to deliver healthcare. CONCLUSIONS: This study provides novel evidence of the impact of COVID-19 on people identified as at high risk of severe illness. These findings should be considered by policy-makers and healthcare professionals to avoid unintended consequences of continued restrictions and future pandemic responses

Use of Artificial Intelligence to understand adults’ thoughts and behaviours relating to COVID-19

Aims: The outbreak of severe acute respiratory syndrome coronavirus 2 (COVID-19) is a global pandemic that has had substantial impact across societies. An attempt to reduce infection and spread of the disease, for most nations, has led to a lockdown period, where people’s movement has been restricted resulting in a consequential impact on employment, lifestyle behaviours and wellbeing. As such, this study aimed to explore adults’ thoughts and behaviours in response to the outbreak and resulting lockdown measures. Methods: Using an online survey, 1126 adults responded to invitations to participate in the study. Participants, all aged 18 years or older, were recruited using social media, email distribution lists, website advertisement and word of mouth. Sentiment and personality features extracted from free-text responses using Artificial Intelligence methods were used to cluster participants. Results: Findings demonstrated that there was varied knowledge of the symptoms of COVID-19 and high concern about infection, severe illness and death, spread to others, the impact on the health service and on the economy. Higher concerns about infection, illness and death were reported by people identified at high risk of severe illness from COVID-19. Behavioural clusters, identified using Artificial Intelligence methods, differed significantly in sentiment and personality traits, as well as concerns about COVID-19, actions, lifestyle behaviours and wellbeing during the COVID-19 lockdown. Conclusions: This time-sensitive study provides important insights into adults’ perceptions and behaviours in response to the COVID-19 pandemic and associated lockdown. The use of Artificial Intelligence has identified that there are two behavioural clusters that can predict people’s responses during the COVID-19 pandemic, which goes beyond simple demographic groupings. Considering these insights may improve the effectiveness of communication, actions to reduce the direct and indirect impact of the COVID-19 pandemic and to support community recovery

Investigating the Effect of Adding Nudges to Increase Engagement in Active Video Watching

In order for videos to be a powerful medium for learning, it is crucial that learners engage in constructive learning. Historic interactions of previous learners can provide a rich resource to enhance interaction and promote engagement fostering constructive learning. This paper proposes such a novel approach of adding nudges to AVW-Space, a platform for video-based learning. We present the enhancements implemented in AVW-Space in the form of interactive visualizations and personalized prompts. A study focusing on presentation skills was conducted in a large first-year engineering course, in which AVW-Space provided an online resource for the students to use as they wish. The students were randomly divided into the control and experimental groups, which had access to the original and enhanced version of AVW-Space respectively. Our findings show that nudging is effective in fostering constructive learning: there was a significant difference in the percentage of constructive students in the two groups. The experimental group students wrote more comments, found AVW-Space easier to use, reported less frustration when commenting, and had higher confidence in their performance on commenting

Self-Regulation, Knowledge, Experience: Which User Characteristics Are Useful for Predicting Video Engagement?

The use of videos in education has attracted considerable research attention. However, in order to gain the most benefits, learners need to actively engage with videos. It is an important, yet challenging, task to disentangle the relation between engagement with videos and learning, and at the same time to take into account relevant individual differences in order to offer personalised support. In this paper we investigate the question: `Can user characteristics relating to self-regulation, knowledge, and experience be leveraged for predicting user engagement with videos?'. Our results show that users' domain knowledge and self-regulation abilities can inform overall engagement prediction (inactive, passive and constructive learners), which makes them useful for adaptation and personalisation

Temporal Analytics of Workplace-based Assessment Data to Support Self-Regulated Learning

One of the most effective ways to develop self-regulated learning skills in higher education is to include work placements. Workplace-based assessment (WBA) provides opportunities for students to gain feedback on their practical skills, reflect on their performance, and set goals and actions for further development. This requires identifying temporal patterns, as placements usually span extended periods of time. In this paper we explore two intelligent computational methods (burst detection and process mining) to derive temporal patterns. We apply both methods on WBA data from a cohort of first-year medical students. Through this we identify interesting temporal patterns, and gather educators' feedback on their usefulness for self-regulated learning

Quantified Self Analytics Tools for Self-regulated Learning with myPAL

One of the major challenges in higher education is developing self-regulation skills for lifelong learning. We address this challenge within the myPAL project, in medical education context, utilising the vast amount of student assessment and feedback data collected throughout the programme. The underlying principle of myPAL is Quantified Self -- the use of personal data to enable students to become lifelong learners. myPAL is facilitating this with learning analytics combined with interactive nudges. This paper reviews the state of the art in Quantified Self analytics tools to identify what approaches can be adopted in myPAL and what gaps require further research. The paper contributes to awareness and reflection in technology-enhanced learning by: (i) identifying requirements for intelligent personal adaptive learning systems that foster self-regulation (using myPAL as an example); (ii) analysing the state of the art in text analytics and visualisation related to Quantified Self for self-regulated learning; and (iii) identifying open issues and suggesting possible ways to address them

Using the Explicit User Profile to Predict User Engagement in Active Video Watching

In this paper we leverage the explicit user profile (relating to experience, knowledge, and self-regulation) to predict user engagement in active video watching. Data from two user studies for informal learning of presentation skills in a Higher Education context is used to develop and validate the prediction models. Our results show that these user characteristics can reasonably predict the overall engagement (inactive, passive and constructive learners). Our approach can be used to inform adaptive interventions that prevent disengagement and enhance the learning experience