Exploring narrativity in data visualization in journalism

Many news stories are based on data visualization, and storytelling with data has become a buzzword in journalism. But what exactly does storytelling with data mean? When does a data visualization tell a story? And what are narrative constituents in data visualization? This chapter first defines the key terms in this context: story, narrative, narrativity, showing and telling. Then, it sheds light on the various forms of narrativity in data visualization and, based on a corpus analysis of 73 data visualizations, describes the basic visual elements that constitute narrativity: the instance of a narrator, sequentiality, temporal dimension, and tellability. The paper concludes that understanding how data are transformed into visual stories is key to understanding how facts are shaped and communicated in society

Design and journalism : challenges and opportunities. A dialogue between two cultures

Communication has become increasingly multi-semiotic and particularly more visual. The shift from a culture dominated by texts to a visual culture is particularly observable in journalism. Besides images, design is coming into play as a crucial semiotic mode for making meaning. In news features, special reports, or data visualizations, we can find a rich and complex interplay of different semiotic modes, e.g., text, image, and layout, which constitute the meaning-making process. However, the multimodal interplay also entails tensions since design and journalism are different disciplines with different semiotic resources, practices, and ways of thinking. In our paper, we focus on the relationship between these two disciplines by using a multimodal approach for our empirical analysis based on social semiotics. In a case study, we will examine the role of design as a semiotic mode in journalistic artifacts and discuss the relationship between design and journalism as well as the challenges and opportunities

Data stories : rethinking journalistic storytelling in the context of data journalism

This paper addresses the increased use of data and data visualization in newsrooms, which has yielded a new form of storytelling: data stories. In journalism, data stories or storytelling with data are the new buzzwords. What journalists mean by data stories, however, remains blurred. We use the emergence of data stories as an opportunity to describe the changing understanding of journalistic storytelling. Based on interviews with editorial leaders, data journalists, developers, and designers in 26 major news organizations in Europe, we focus on practitioners’ perspective on data stories. In our empirical study, we identified seven key features of journalistic data stories: data, communicative function, the textual-visual relationship, structure and design of a story, interactivity, and the meta-story. These findings contribute to rethinking the narrative approach to journalism

Virtual reality as a tool for political decision-making? An empirical study on the power of immersive images on voting behavior

One of the strengths of virtual reality (VR) is to provide a highly realistic user experience. How would VR's power of realism affect political decision-making, for example, when experienced by citizens before they cast their vote on an issue? We set out to empirically assess if and how voting information presented in VR would influence people's voting behavior, compared to the traditional text presentation format. In a 2 (format: text vs. VR) × 2 (argumentation: pro vs. con) between-subject factorial experiment, we assessed participants' voting behavior on a fictitious popular initiative. We first asked all participants (N = 179) to cast their vote based on a brief text, inspired by the traditional Swiss voting booklet (baseline). We then randomly assigned participants to one of four experimental conditions containing the same pro or con arguments concerning the voting issue. Participants could then adjust their previously-cast vote. This was followed by retrospective interviews (N = 32) to gain deeper insights into the decision-making process of the participants. Our study shows that the presentation format has a reinforcing effect, that is, leading to more YES votes for the VR group, and fewer YES votes for the text group. Irrespective of the pro or con arguments, participants show an overall increase in YES votes in VR, which is not the case for the text group. We identified six factors that may have led to this positive change with VR: (1) the affirmative power of images, (2) the vividness of immersive images, (3) first-person storytelling and storyliving, (4) the greater affordances of VR for engagement through interaction, (5) the design of the VR environment, and (6) the novelty of the VR technology

“It’s a matter of age” : four dimensions of youths’ news consumption

News media in Switzerland are confronted with the challenge of not reaching young people, as youth-specific news platforms and formats are not used by the target group. Our study aims to determine how and where young people can be reached with news. It uses a mixed methods approach to analyze the expectations of young people towards news content and formats and to determine their news consumption patterns. The results show that young people’s news consumption is characterized by four dimensions: 1. duration and times of consumption, 2. news consumption habits and behavior, 3. restrictions, 4. media literacy. The significance of these dimensions varies between three age groups determined through the study. The news consumption of the 12-to-14 year-olds is strongly restricted by parents and school. This group consumes news mainly through media available at home. In the group of 15- to-17 year-olds, parental influence and restrictions decrease, while peer influence increases. This age group spends a lot of time on social media platforms where young people stumble upon news rather accidentally. Between 18 and 20 years of age, news consumption stabilizes, and individual patterns emerge. This age group accesses news via selected apps and social media channels. Young people in Switzerland prefer visual formats like pictures, videos, and memes. When scrolling through social media platforms, they come across news content which arouses their interest in certain information and leads them to search for it on media websites. Swiss Youth wants to be informed about relevant news or topics that are “in vogue”

Automated wildlife image classification: An active learning tool for ecological applications

Wildlife camera trap images are being used extensively to investigate animal abundance, habitat associations, and behavior, which is complicated by the fact that experts must first classify the images manually. Artificial intelligence systems can take over this task but usually need a large number of already-labeled training images to achieve sufficient performance. This requirement necessitates human expert labor and poses a particular challenge for projects with few cameras or short durations. We propose a label-efficient learning strategy that enables researchers with small or medium-sized image databases to leverage the potential of modern machine learning, thus freeing crucial resources for subsequent analyses. Our methodological proposal is two-fold: (1) We improve current strategies of combining object detection and image classification by tuning the hyperparameters of both models. (2) We provide an active learning (AL) system that allows training deep learning models very efficiently in terms of required human-labeled training images. We supply a software package that enables researchers to use these methods directly and thereby ensure the broad applicability of the proposed framework in ecological practice. We show that our tuning strategy improves predictive performance. We demonstrate how the AL pipeline reduces the amount of pre-labeled data needed to achieve a specific predictive performance and that it is especially valuable for improving out-of-sample predictive performance. We conclude that the combination of tuning and AL increases predictive performance substantially. Furthermore, we argue that our work can broadly impact the community through the ready-to-use software package provided. Finally, the publication of our models tailored to European wildlife data enriches existing model bases mostly trained on data from Africa and North America

Predictive Value of Fever and Palmar Pallor for P. falciparum Parasitaemia in Children from an Endemic Area

INTRODUCTION: Although the incidence of Plasmodium falciparum malaria in some parts of sub-Saharan Africa is reported to decline and other conditions, causing similar symptoms as clinical malaria are gaining in relevance, presumptive anti-malarial treatment is still common. This study traced for age-dependent signs and symptoms predictive for P. falciparum parasitaemia. METHODS: In total, 5447 visits of 3641 patients between 2-60 months of age who attended an outpatient department (OPD) of a rural hospital in the Ashanti Region, Ghana, were analysed. All Children were examined by a paediatrician and a full blood count and thick smear were done. A Classification and Regression Tree (CART) model was used to generate a clinical decision tree to predict malarial parasitaemia a7nd predictive values of all symptoms were calculated. RESULTS: Malarial parasitaemia was detected in children between 2-12 months and between 12-60 months of age with a prevalence of 13.8% and 30.6%, respectively. The CART-model revealed age-dependent differences in the ability of the variables to predict parasitaemia. While palmar pallor was the most important symptom in children between 2-12 months, a report of fever and an elevated body temperature of ≥37.5°C gained in relevance in children between 12-60 months. The variable palmar pallor was significantly (p<0.001) associated with lower haemoglobin levels in children of all ages. Compared to the Integrated Management of Childhood Illness (IMCI) algorithm the CART-model had much lower sensitivities, but higher specificities and positive predictive values for a malarial parasitaemia. CONCLUSIONS: Use of age-derived algorithms increases the specificity of the prediction for P. falciparum parasitaemia. The predictive value of palmar pallor should be underlined in health worker training. Due to a lack of sensitivity neither the best algorithm nor palmar pallor as a single sign are eligible for decision-making and cannot replace presumptive treatment or laboratory diagnosis