An Evaluation of Semantically Grouped Word Cloud Designs

A bibliometric review of waste management and innovation: Unveiling trends, knowledge structure and emerging research fronts

The pressing challenges in waste management have motivated this comprehensive study examining prior research and contemporary trends concerning innovation and waste management. A meticulous investigation of 2264 documents (1968-2024) was conducted using bibliometrix R-tool to analyse Scopus and Web of Science databases, offering a holistic global perspective. Heightened societal concern about waste management, driven by soaring waste production from consumption patterns, requires urgent exploration of effective waste elimination and transformation systems. This study provides a comprehensive summary of the topic, delving deeply into its complexities. Through thorough analysis of global trends, it constitutes a significant stride towards identifying effective solutions, offering valuable contributions to both scientific understanding and practical applications. This research pioneers a comprehensive synthesis of innovation and waste management issues, showcasing originality and substantial contributions. The identified collaborative networks expose a lack of transnational cooperation, potentially hindering waste management innovation. Future research around waste management innovation should focus on synergies among competitors within the same industry and across industries to minimize waste and maximize resource utilization, 4.0 technologies, global waste chain impacts and challenges along with solutions for developing countries.

Creating Emordle: Animating Word Cloud for Emotion Expression

We propose emordle, a conceptual design that animates wordles (compact word clouds) to deliver their emotional context to audiences. To inform the design, we first reviewed online examples of animated texts and animated wordles, and summarized strategies for injecting emotion into the animations. We introduced a composite approach that extends an existing animation scheme for one word to multiple words in a wordle with two global factors: the randomness of text animation (entropy) and the animation speed (speed). To create an emordle, general users can choose one predefined animated scheme that matches the intended emotion class and fine-tune the emotion intensity with the two parameters. We designed proof-of-concept emordle examples for four basic emotion classes, namely happiness, sadness, anger, and fear. We conducted two controlled crowdsourcing studies to evaluate our approach. The first study confirmed that people generally agreed on the conveyed emotions from well-crafted animations, and the second one demonstrated that our identified factors helped fine-tune the extent of the emotion delivered. We also invited general users to create their own emordles based on our proposed framework. Through this user study, we confirmed the effectiveness of the approach. We concluded with implications for future research opportunities of supporting emotion expression in visualizations.

Employee perception of precision technology use at the dairy farm

The adoption of precision technologies on dairy farms has increased significantly in recent decades, leading to the challenge of providing employees with resources to maximize the efficient use of these tools. The objective of this study was to explore how dairy farm employees perceive the available precision technologies and to identify possible challenges they face when adapting to their use at the farm. An online survey consisting of four sections (employee demographics, precision technologies in use, perception of these technologies, and opportunities for adapting to technology use) was completed from September to December 2022 by 266 farm employees from three dairies operated under similar management. Most of the respondents were identified as male (72.2%), Hispanic or Latino (92.5%), aged between 21 and 30 (39.1%) or 31 and 40 yr (36.8%), with a bachelor's degree (34.6%) or completion of middle school (29.3%) and having basic or no English proficiency (74%). Overall, the respondents indicated being comfortable (95.6%) with and understanding (91.8%) the technology they use. Employees recognized precision technology as a tool that helps them to be more efficient (93.7%), identifying the technologies' benefits (92.1%). However, challenges for adapting to these technologies included personal limitations, such as not knowing the language of the technology (31%), visual impairments (24%), light sensitivity (14%), and not being able to read (7%). Environmental limitations were also recognized and included cold weather (64.3%), wind (46%), and surroundings that were too dark (31%) or too bright (21%). Significant associations between perception of the technology and age, level of education, and English proficiency were identified. Respondents indicated their desire to learn more about precision technologies implemented at work, which could eventually lead to improved efficiency at the dairy operation through innovations in the way users interact with these technologies, increasing employees' motivation. This study provides insights that could assist the dairy industry in addressing challenges and enhancing opportunities for a more efficient use of precision technologies at dairy farms.

The paradox of subjective age: age(ing) in the self-presentation of older adults

OBJECTIVES

Studies have shown that when people grow older, the negative perceptions about age(ing) become self-directed. In this study, we examined if and how this assertion is expressed in the self-presentation of older adults.

DESIGN

To explore this issue, we undertook an online survey with 818 Israeli older adults (aged 65-90) who were asked to present themselves in writing, using an open question and to choose the age terms that they preferred, relying on a multiple-choice question. Responses were analyzed qualitatively and quantitatively using "word cloud" and linguistic inquiry.

RESULTS

The analysis indicated three ways of coping with the issue of age in one's self-presentation: Absence of old age - older people who blur their processes of aging; Camouflaged aging - older people who emphasize their age by using a line of self-ageism; Multiplicity of old age terms - the existing gap between what is being used by people and the ideal related to the use of existing age terms. The findings highlight the role of subjective age in one's self-presentations.

CONCLUSIONS

The results point to the ambivalence that older people feel in relation to their age(ing) and especially reflect the paradox of subjective age. That is, on the one hand studies have stressed the positive aspects of this practice, whereas on the other hand, subjective age possibly reflects a response to internalized negative stereotypes and prejudice about old age(ing). In this sense, this study expands the knowledge in the field of self-presentation and (subjective) age in the second half of life.

Lived experience-centred word clouds may improve research uncertainty gathering in priority setting partnerships

INTRODUCTION

AO Spine RECODE-DCM was a multi-stakeholder priority setting partnership (PSP) to define the top ten research priorities for degenerative cervical myelopathy (DCM). Priorities were generated and iteratively refined using a series of surveys administered to surgeons, other healthcare professionals (oHCP) and people with DCM (PwDCM). The aim of this work was to utilise word clouds to enable the perspectives of people with the condition to be heard earlier in the PSP process than is traditionally the case. The objective was to evaluate the added value of word clouds in the process of defining research uncertainties in National Institute for Health Research (NIHR) James Lind Alliance (JLA) Priority Setting Partnerships.

METHODS

Patient-generated word clouds were created for the four survey subsections of the AO Spine RECODE-DCM PSP: diagnosis, treatment, long-term management and other issues. These were then evaluated as a nested methodological study. Word-clouds were created and iteratively refined by an online support group of people with DCM, before being curated by the RECODE-DCM management committee and expert healthcare professional representatives. The final word clouds were embedded within the surveys administered at random to 50% of participants. DCM research uncertainties suggested by participants were compared pre- and post-word cloud presentation.

RESULTS

A total of 215 (50.9%) participants were randomised to the word cloud stream, including 118 (55%) spinal surgeons, 52 (24%) PwDCM and 45 (21%) oHCP. Participants submitted 434 additional uncertainties after word cloud review: word count was lower and more uniform across each survey subsections compared to pre-word cloud uncertainties. Twenty-three (32%) of the final 74 PSP summary questions did not have a post-word cloud contribution and no summary question was formed exclusively on post-word cloud uncertainties. There were differences in mapping of pre- and post-word cloud uncertainties to summary questions, with greater mapping of post-word cloud uncertainties to the number 1 research question priority: raising awareness. Five of the final summary questions were more likely to map to the research uncertainties suggested by participants after having reviewed the word clouds.

CONCLUSIONS

Word clouds may increase the perspective of underrepresented stakeholders in the research question gathering stage of priority setting partnerships. This may help steer the process towards research questions that are of highest priority for people with the condition.

Machine learning based attribution mapping of climate related discussions on social media

A united front from all the stakeholders including public, administration and academia alike is required to counter the growing threat of climate change. The recent rise of social media as the new public address system, makes it an ideal source of information to assess public discussions and responses in real time. We mine c.1.7 m posts from 55 climate related subreddits on social media platform Reddit since its inception. Using USE, a state-of-the-art sentence encoder, and K-means clustering algorithm, we develop a machine learning based approach to identify, store, process and classify the posts automatically, and at a scale. In the broad and multifaceted theme of climate change, our approach narrows down the focus to 10 critical underlying themes comprising the public discussions on social media over time. Furthermore, we employ a full order partial correlation analysis to assess the relationship between the different identified themes. We show that in line with Paris Agreement, while the climate science community has been successful in influencing the discussions on both the causes and effects of climate change, the public administration has failed to appropriately communicate the causes of climate change and has been able to influence only the discussions on the effects of it. Hence, our study shows a clear gap in the public communication by the administration, wherein counter-intuitively less emphasis has been given on the drivers of climate change. This information can be particularly beneficial to policymakers and climate activists in decision making as they try to close the gap between public and academia.

Presenting a comprehensive multi-scale evaluation framework for participatory modelling programs: A scoping review

INTRODUCTION

Systems modelling and simulation can improve understanding of complex systems to support decision making, better managing system challenges. Advances in technology have facilitated accessibility of modelling by diverse stakeholders, allowing them to engage with and contribute to the development of systems models (participatory modelling). However, despite its increasing applications across a range of disciplines, there is a growing need to improve evaluation efforts to effectively report on the quality, importance, and value of participatory modelling. This paper aims to identify and assess evaluation frameworks, criteria, and/or processes, as well as to synthesize the findings into a comprehensive multi-scale framework for participatory modelling programs.

MATERIALS AND METHODS

A scoping review approach was utilized, which involved a systematic literature search via Scopus in consultation with experts to identify and appraise records that described an evaluation framework, criteria, and/or process in the context of participatory modelling. This scoping review is registered with the Open Science Framework.

RESULTS

The review identified 11 studies, which varied in evaluation purposes, terminologies, levels of examination, and time points. The review of studies highlighted areas of overlap and opportunities for further development, which prompted the development of a comprehensive multi-scale evaluation framework to assess participatory modelling programs across disciplines and systems modelling methods. The framework consists of four categories (Feasibility, Value, Change/Action, Sustainability) with 30 evaluation criteria, broken down across project-, individual-, group- and system-level impacts.

DISCUSSION & CONCLUSION

The presented novel framework brings together a significant knowledge base into a flexible, cross-sectoral evaluation effort that considers the whole participatory modelling process. Developed through the rigorous synthesis of multidisciplinary expertise from existing studies, the application of the framework can provide the opportunity to understand practical future implications such as which aspects are particularly important for policy decisions, community learning, and the ongoing improvement of participatory modelling methods.

Sex-specific analysis of traumatic brain injury events: applying computational and data visualization techniques to inform prevention and management

BACKGROUND

The interplay of host, agent, and environment implicated in traumatic brain injury (TBI) events is difficult to account for in hypothesis-driven research. Data-driven analysis of injury data can enable insight into injury events in novel ways. This research dissected complex and multidimensional data at the time of the TBI event by exploiting data mining and information visualization methods.

METHODS

We drew upon population-based decade-long health administrative data collected through the routine operation of the publicly funded health system in Ontario, Canada. We applied a computational approach to categorize health records of 235,003 patients with TBI versus the same number of reference patients without TBI, individually matched based on sex, age, place of residence, and neighbourhood income quantile. We adopted the basic concepts of the Haddon Matrix (host, agent, environment) to organize emerging factors significantly related to TBI versus non-TBI events. To explore sex differences, the data of male and female patients with TBI were plotted on heatmaps and clustered using hierarchical clustering algorithms.

RESULTS

Based on detected similarities, the computational technique yielded 34 factors on which individual TBI-event codes were loaded, allowing observation of a set of definable patterns within the host, the agent, and the environment. Differences in the patterns of host, agent and environment were found between male and female patients with TBI, which are currently not identified based on data from injury surveillance databases. The results were internally validated.

CONCLUSIONS

The study outlines novel areas for research relevant to TBI and offers insight into how computational and visual techniques can be applied to advance the understanding of TBI event. Results highlight unique aspects of sex differences of the host and agent at the injury event, as well as differences in exposure to adverse social and environmental circumstances, which can be a function of gender, aiding in future studies of injury prevention and gender-transformative care.

The Science of Visual Data Communication: What Works

Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust-especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.

PyramidTags: Context-, Time- and Word Order-Aware Tag Maps to Explore Large Document Collections

It is difficult to explore large text collections if no or little information is available on the contained documents. Hence, starting analytic tasks on such corpora is challenging for many stakeholders from various domains. As a remedy, recent visualization research suggests to use visual spatializations of representative text documents or tags to explore text collections. With PyramidTags, we introduce a novel approach for summarizing large text collections visually. In contrast to previous work, PyramidTags in particular aims at creating an improved representation that incorporates both temporal evolution and semantic relationship of visualized tags within the summarized document collection. As a result, it equips analysts with a visual starting point for interactive exploration to not only get an overview of the main terms and phrases of the corpus, but also to grasp important ideas and stories. Analysts can hover and select multiple tags to explore relationships and retrieve the most relevant documents. In this work, we apply PyramidTags to hundreds of thousands of web-crawled news reports. Our benchmarks suggest that PyramidTags creates time- and context-aware layouts, while preserving the inherent word order of important pairs.

The development of lived experience-centered word clouds to support research uncertainty gathering in degenerative cervical myelopathy: results from an engagement process and protocol for their evaluation, via a nested randomized controlled trial

OBJECTIVES

AO Spine REsearch objectives and Common Data Elements for Degenerative Cervical Myelopathy [RECODE-DCM] is a multi-stakeholder consensus process aiming to promote research efficiency in DCM. It aims to establish the top 10 research uncertainties, through a James Lind Alliance Priority Setting Partnership [PSP]. Through a consensus process, research questions are generated and ranked. The inclusion of people with cervical myelopathy [PwCM] is central to the process. We hypothesized that presenting PwCM experience through word cloud generation would stimulate other key stakeholders to generate research questions better aligned with PwCM needs. This protocol outlines our plans to evaluate this as a nested methodological study within our PSP.

METHODS

An online poll asked PwCM to submit and vote on words associated with aspects of DCM. After review, a refined word list was re-polled for voting and word submission. Word clouds were generated and an implementation plan for AO Spine RECODE-DCM PSP surveys was subsequently developed.

RESULTS

Seventy-nine terms were submitted after the first poll. Eighty-seven refined words were then re-polled (which added a further 39 words). Four word clouds were generated under the categories of diagnosis, management, long-term effects, and other. A 1:1 block randomization protocol to assess word cloud impact on the number and relevance of PSP research questions was generated.

CONCLUSIONS

We have shown it is feasible to work with PwCM to generate a tool for the AO Spine RECODE-DCM nested methodological study. Once the survey stage is completed, we will be able to evaluate the impact of the word clouds. Further research will be needed to assess the value of any impact in terms of stimulating a more creative research agenda.

Dishes Adapted to Dysphagia: Sensory Characteristics and Their Relationship to Hedonic Acceptance

Dishes whose texture has been modified for dysphagia undergo changes in other sensory characteristics as well. Therefore, it is necessary to identify these characteristics in adapted dishes and their relationship to hedonic acceptance. In the present work, the sensory characteristics of five dishes adapted to dysphagia associated with cerebral palsy were investigated using the check-all-that-apply method. A hedonic evaluation with a panel of non-dysphagic judges was performed to relate the degree of acceptance with the sensory characteristics of the adapted dishes. The identification of the original non-adapted dish as well as the relationship between the hedonic evaluation by non-dysphagic judges and dysphagic judges were explored. The main attributes of the dishes adapted to dysphagia were "homogeneity" and "easy-to-swallow". Attributes that increased the hedonic evaluation were "flavorful", "flavor of the original dish", "soft texture", "easy-to-swallow", and "odor of the original dish". The attributes that decreased the hedonic evaluation were "thick mash" and "bland". The fish dish was the only one correctly identified more than 62.5% of the time. The adapted dishes received scores above 4.7 out of 9.0 in the hedonic evaluation. The most accepted dishes were the chicken stew and the chickpea stew. Except for the pasta dish, the test yielded similar results to those obtained with dysphagic judges. The texture-modified dishes were correctly characterized and accepted. This study shows that all the sensory characteristics of the adapted dishes are crucial for acceptance and identification.

A comparative study on the use of microscopy in pharmacology and cell biology research

Microscopy is the main technique to visualize and study the structure and function of cells. The impact of optical and electron microscopy techniques is enormous in all fields of biomedical research. It is possible that different research areas rely on microscopy in diverse ways. Here, we analyzed comparatively the use of microscopy in pharmacology and cell biology, among other biomedical sciences fields. We collected data from articles published in several major journals in these fields. We analyzed the frequency of use of different optical and electron microscopy techniques: bright field, phase contrast, differential interference contrast, polarization, conventional fluorescence, confocal, live cell imaging, super resolution, transmission and scanning electron microscopy, and cryoelectron microscopy. Our analysis showed that the use of microscopy has a distinctive pattern in each research area, and that nearly half of the articles from pharmacology journals did not use any microscopy method, compared to the use of microscopy in almost all the articles from cell biology journals. The most frequent microscopy methods in all the journals in all areas were bright field and fluorescence (conventional and confocal). Again, the pattern of use was different: while the most used microscopy methods in pharmacology were bright field and conventional fluorescence, in cell biology the most used methods were conventional and confocal fluorescence, and live cell imaging. We observed that the combination of different microscopy techniques was more frequent in cell biology, with up to 6 methods in the same article. To correlate the use of microscopy with the research theme of each article, we analyzed the proportion of microscopy figures with the use of cell culture. We analyzed comparatively the vocabulary of each biomedical sciences field, by the identification of the most frequent words in the articles. The collection of data described here shows a vast difference in the use of microscopy among different fields of biomedical sciences. The data presented here could be valuable in other scientific and educational contexts.

Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

Purpose

Graphic-based tag clouds aim to visually represent tag content and tag structure, and then to better represent tagged information for later search. However, few studies have clarified the features among varied visualization approaches involved in graphic-based tag clouds and compared them for the purpose of information search.

Design/methodology/approach

After reviewing four kinds of graphic-based tag clouds, an experimental demonstration was conducted in our study to verify how user performs in information search for a general seeking task by using them. Precision ratio, recall ratio, clicks on search and time for search were four variables tested in the experiment. Also, two supplementary tests were respectively carried out to manifest how graphic-based tag clouds contributed to the identification of target tags and tag clusters.

Findings

The experimental results showed that compared to tag content visual tag structure was more important to find related tags from tag clouds for information search. In addition, tag clouds that visually represented the semantic relationships within tags could make user more confident about their search result and carry out a shorter learning process during searching, which signified a tag-based information search path when visual elements were applied.

Originality/value

This research is one of the first to illustrate systematically the graphic-based tag clouds and their impacts on information search. The research findings could suggest on how to build up more effective and interactive tag clouds and make proposition for the design of search user interface by using graphic-based tag clouds.