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

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

Supporting Constructive Video-based Learning: Requirements Elicitation from Exploratory Studies

Although videos are a highly popular digital medium for learning, video watching can be a passive activity and results in limited learning. This calls for interactive means to support engagement and active video watching. However, there is limited insight into what engagement challenges have to be overcome and what intelligent features are needed. This paper presents an empirical way to elicit requirements for innovative functionality to support constructive video-based learning. We present two user studies with an active video watching system instantiated for soft skill learning (pitch presenta-tions). Based on the studies, we identify whether learning is happening and what kind of interaction contributes to learning, what difficulties participants face and how these can be overcome with additional intelligent support. Our findings show that participants who engaged in constructive learning have improved their conceptual understanding of presentation skills, while those who exhibited more passive ways of learning have not improved as much as constructive learners. Analysis of participants’ profiles and experiences led to requirements for intelligent support with active video watching. Based on this, we propose intelligent nudging in the form of signposting and prompts to further promote constructive learning

Learning the Repair Urgency for a Decision Support System for Tunnel Maintenance

The transport network in many countries relies on extended portions which run underground in tunnels. As tunnels age, repairs are required to prevent dangerous collapses. However repairs are expensive and will affect the operational efficiency of the tunnel. We present a decision support system (DSS) based on supervised machine learning methods that learns to predict the risk factor and the resulting repair urgency in the tunnel maintenance planning of a European national rail operator. The data on which the prototype has been built consists of 47 tunnels of varying lengths. For each tunnel, periodic survey inspection data is available for multiple years, as well as other data such as the method of construction of the tunnel. Expert annotations are also available for each 10m tunnel segment for each survey as to the degree of repair urgency which are used for both training and model evaluation. We show that good predictive power can be obtained and discuss the relative merits of a number of learning methods

Divergence of Mammalian Higher Order Chromatin Structure Is Associated with Developmental Loci

Several recent studies have examined different aspects of mammalian higher order chromatin structure - replication timing, lamina association and Hi-C inter-locus interactions - and have suggested that most of these features of genome organisation are conserved over evolution. However, the extent of evolutionary divergence in higher order structure has not been rigorously measured across the mammalian genome, and until now little has been known about the characteristics of any divergent loci present. Here, we generate a dataset combining multiple measurements of chromatin structure and organisation over many embryonic cell types for both human and mouse that, for the first time, allows a comprehensive assessment of the extent of structural divergence between mammalian genomes. Comparison of orthologous regions confirms that all measurable facets of higher order structure are conserved between human and mouse, across the vast majority of the detectably orthologous genome. This broad similarity is observed in spite of many loci possessing cell type specific structures. However, we also identify hundreds of regions (from 100 Kb to 2.7 Mb in size) showing consistent evidence of divergence between these species, constituting at least 10% of the orthologous mammalian genome and encompassing many hundreds of human and mouse genes. These regions show unusual shifts in human GC content, are unevenly distributed across both genomes, and are enriched in human subtelomeric regions. Divergent regions are also relatively enriched for genes showing divergent expression patterns between human and mouse ES cells, implying these regions cause divergent regulation. Particular divergent loci are strikingly enriched in genes implicated in vertebrate development, suggesting important roles for structural divergence in the evolution of mammalian developmental programmes. These data suggest that, though relatively rare in the mammalian genome, divergence in higher order chromatin structure has played important roles during evolution

Body appreciation around the world: Measurement invariance of the Body Appreciation Scale-2 (BAS-2) across 65 nations, 40 languages, gender identities, and age

The Body Appreciation Scale-2 (BAS-2) is a widely used measure of a core facet of the positive body image construct. However, extant research concerning measurement invariance of the BAS-2 across a large number of nations remains limited. Here, we utilised the Body Image in Nature (BINS) dataset - with data collected between 2020 and 2022 - to assess measurement invariance of the BAS-2 across 65 nations, 40 languages, gender identities, and age groups. Multi-group confirmatory factor analysis indicated that full scalar invariance was upheld across all nations, languages, gender identities, and age groups, suggesting that the unidimensional BAS-2 model has widespread applicability. There were large differences across nations and languages in latent body appreciation, while differences across gender identities and age groups were negligible-to-small. Additionally, greater body appreciation was significantly associated with higher life satisfaction, being single (versus being married or in a committed relationship), and greater rurality (versus urbanicity). Across a subset of nations where nation-level data were available, greater body appreciation was also significantly associated with greater cultural distance from the United States and greater relative income inequality. These findings suggest that the BAS-2 likely captures a near-universal conceptualisation of the body appreciation construct, which should facilitate further cross-cultural research

Understanding collaborative sensemaking behaviour using semantic types in interaction data

This paper presents a trial study to gain insight into collaborative sensemaking behaviour. We use a generic argumentation tool to capture interaction data, which is then analyzed by exploiting collaboration-specific semantic types. A Community Behaviour Analytics Tool, CommBAT, was developed. Further studies were conducted to visualize the different patterns in the collaborative behaviour. Initial findings show the power of semantics in helping to detect a series of sensemaking behaviour patterns