This study examines the job postings for open innovation (OI) specialists to determine a universal archetype of what competencies and tasks are requested from said professionals and their implications for corporate sustainability and responsibility. This research uses Bayesian statistics and latent Dirichlet allocation (LDA) topic modeling to measure multiple dimensions of the 341 sampled job postings. Our empirical findings unveil the pivotal role that OI specialists have in engaging with stakeholders and monitoring OI dynamics. Multiple dimensions are (...) expected from OI specialists, addressing a multitude of concerns, such as the environment and technologies. Moreover, this study underlines the need for OI specialists to comprehend both internal and external stakeholders' needs. This research contributes to the literature as follows. First, it underlines the value of topic modeling analysis in job profiling research. Second, it bridges existing knowledge gaps on OI specialists' competencies and roles with empirical evidence obtained from a global dataset. Third, it outlines the current market expectations and requirements for OI specialists, which is useful to both candidates and companies. (shrink)

The wanton dissemination of network pseudohealth information has brought great harm to people’s health, life, and property. It is important to detect and identify network pseudohealth information. Based on this, this paper defines the concepts of pseudohealth information, data block, and data block integration, designs an architecture that combines the latent Dirichlet allocation algorithm and data block update integration, and proposes the combination algorithm model. In addition, crawler technology is used to crawl the pseudohealth information transmitted on (...) the Sina Weibo platform during the “epidemic situation” from February to March 2020 for the simulation test on the experimental case dataset. The research results show that the LDA model can deeply mine the semantic information of network pseudohealth information, obtain the features of document-topic distribution, and classify and train topic features as input variables; the dataset partitioning method can effectively block data according to the text attributes and class labels of network pseudohealth information and can accurately classify and integrate the block data through the data block reintegration method; and considering that the combination model has certain limitations on the detection of network pseudohealth information, the support vector machine model can extract the granularity content of data blocks in pseudohealth information in real time, thus greatly improving the recognition performance of the combination model. (shrink)

Automatic text summarization extracts information from a source text and presents it to the user in a condensed form while preserving its primary content. Many text summarization approaches have been investigated in the literature for highly resourced languages. At the same time, ATS is a complicated and challenging task for under-resourced languages like Malayalam. The lack of a standard corpus and enough processing tools are challenges when it comes to language processing. In the absence of a standard corpus, we have (...) developed a dataset consisting of Malayalam news articles. This article proposes an extractive topic modeling-based multi-document text summarization approach for Malayalam news documents. We first cluster the contents based on latent topics identified using the latent Dirichlet allocation topic modeling technique. Then by adopting vector space model, the topic vector and sentence vector of the given document are generated. According to the relevant status value, sentences are ranked between the document’s topic and sentence vectors. The summary obtained is optimized for non-redundancy. Evaluation results on Malayalam news articles show that the summary generated by the proposed method is closer to the human-generated summaries than the existing text summarization methods. (shrink)

Expectation Maximisation (EM) and Latent Dirichlet Allocation (LDA) are two frequently used inference algorithms, for finding an appropriate mixture of latent variables, and for finding an allocation of topics for a collection of documents. A recent insight in probabilistic learning is that Jeffrey’s update rule gives a decrease of Kullback-Leibler divergence. Its logic is error correction. It is shown that this same rule and divergence decrease logic is at the heart of EM and LDA, ensuring (...) that successive iterations are decreasingly wrong. (shrink)

This study analyzes the questions on aspects of child custody, visitation rights, or child endangerment that judges pose to forensic psychologists in family law proceedings. Before conducting a psychological evaluation, the legal question in the referral has to be translated into case-specific, forensically relevant issues. The only overarching principle guiding this process is the “best interests of the child” criterion. Literature indicates that judges often struggle to define what variables should be specified for a psychological evaluation in their referral questions. (...) This study aims to contribute to a better understanding of the information judges would like to ascertain from psychological evaluators in child custody and child protection proceedings—an understanding allowing a clearer determination of whether forensic psychologists as experts can deliver this information. Latent Dirichlet allocation (LDA) is used to analyze the referral questions that these judges pose to forensic evaluators in terms of (a) underlying topics (latent dimensions) that can be identified within the referral questions and (b) the probability distributions of legal terms and forensic issues contained in the referral questions. This analysis is based on unclassified text data extracted from German court files. Five topics (latent dimensions) were identified within referral questions resembling cases when the issue was as follows: (a) potential child endangerment in the context of visitation contacts, (b) a possibly limited parenting capacity and its potential effects on child well-being, (c) an impairment of the child has already occurred or could occur, (d) a better option concerning custody and residence, and (e) an unclear topic addressing questions on custody, residence, and visitation in which no specific psychological constructs are involved. In four of the five topics, judges utilize their referral questions to ask for case-group-specific psychological information. In one topic addressing questions on custody, residence, and visitation, judges seem to struggle to define criteria that forensic evaluators should assess. Overall, results help to identify and define more clearly the relevant constructs that forensic experts should examine from the perspective of the courts with the goal of making clearer and more accurate recommendations. (shrink)

Based on Network Agenda Setting Model, this study collected 42,516 media reports from Party Media, commercial media, and We Media of China during the COVID-19 pandemic. We trained LDA models for topic clustering through unsupervised machine learning. Questionnaires and social network analysis methods were then applied to examine the correlation between media network agendas and public network agendas in terms of explicit and implicit topics. The study found that the media reports could be classified into 14 topics by the LDA (...) topic modeling, and the three types of media presented homogeneity in the topics of their reports, yet had their own characteristics; there was a significant correlation between the media network agenda and the public network agenda, and the We Media reports had the most prominent effect on the public network agenda; the correlation between the media agenda and the implicit public agenda was higher than that of the explicit public agenda. Overall, findings showed a significant correlation between network agendas among different media. (shrink)

Due to existence of a huge amount of textual data either on the World Wide Web or in textual databases like PubMed, the development of novel automatic keyphrase extraction methods has emerged as one of the key research problems in recent past. Consequently, a number of machine learning techniques, mostly supervised, have been proposed to extract keyphrases from text documents. But, one of the main bottlenecks that hinders the success of such systems is the requirement of annotated corpora for training (...) purpose. In this paper, we propose the design of a deep text mining system to identify keyphrases in text documents that are either unstructured or semi-structured in nature. The novelty of our system lies in its applicability on a single document, instead of demanding a collection of annotated texts for training, to identify keyphrases embedded within it. The proposed system applies parsing techniques to identify candidate phrases. After mapping the original set of candidate phrases into a low-dimensional space using Singular Value Decomposition, the Markov Clustering technique is applied to cluster related sentences together. Finally, considering each cluster as a document, Latent Dirichlet Allocation is applied to identify feasible keyphrases that are presented to users in non-increasing order of their relevance score values. The efficacy of the proposed system is established through experimentation on datasets from two different domains. On comparative evaluation, we found that the proposed system outperforms KEA and KEA that apply the supervised machine learning approach for automatic keyphrase extraction from text documents. (shrink)

Despite the growing significance of corporate social responsibility (CSR) in corporate public relations (PR) strategies, there has been a paucity of comprehensive reviews of the extant CSR literature within the PR discipline. The aim of this study is to investigate the relationship between the concept of CSR and the discipline of PR through a hybrid concept-discipline-focused review. We employed latent dirichlet allocation (LDA) via topic modeling as a novel approach for intellectual structure analysis. The dataset for this (...) review comprised 246 peer-reviewed articles on CSR published between 1975 and 2022. Based on the LDA results, five topics were identified and discussed: the political CSR stream, social media-oriented CSR, consumer-oriented CSR, CSR reporting, and business-oriented CSR. Additionally, implications for both academic and practical applications were drawn, along with recommendations for future research. This study is pioneering in its contribution to the literature and practice of CSR-related PR research, offering insights into the intellectual structures and trends within this field. (shrink)

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose “The lockdown: One month in Wuhan” YouTube documentary, and applied emotion analysis as well as a (...) machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust, joy, and anticipation are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing. (shrink)

The Securities and Exchange Commission requires the disclosure of changes to or waivers of corporate codes of ethics. Because the nature of amendments or waivers can vary, we expect the text of Item 5.05 to include different topics within different filings. We examine the population of these disclosures in Item 5.05 8-K filings from 2004 to 2020. While previous studies utilized small samples (fewer than 50 observations) to examine limited aspects of these filings, we use the population of these filings (...) from 2004 to 2020 (2121 8-K filings) to elucidate the nature and details of the disclosures. We assess whether Latent Dirichlet Allocation—a computational linguistics technique—can help discern the underlying topics represented in filings. While the Securities and Exchange Commission identifies two topics—amendments and waivers—the Latent Dirichlet Allocation analysis reveals four topics (three related to amendments): (1) code updates and clarifications; (2) combining codes of conduct for all officers and employees, or splitting codes to include a code directly applicable to senior financial officers; (3) codes of ethics waivers; and (4) substantive code changes. The overall trend is for fewer 8-Ks to be filed in recent years, with updates and clarifications becoming the predominant filing type. Our results further indicate that Item 5.05s related to updates and clarifications, and those related to combined or split codes, use fewer words and are more easily readable than those disclosing waivers or material code changes. Although we find no significant price reaction to Item 5.05 8-K filings, we find significant trading volume and volatility reactions, suggesting that these disclosures could give rise to differences in opinion among investors, which is consistent with the US Senate's assertion that these disclosures are of interest to investors. (shrink)

Natural language processing is a critical part of the digital transformation. NLP enables user-friendly interactions between machine and human by making computers understand human languages. Intelligent chatbot is an essential application of NLP to allow understanding of users’ utterance and responding in understandable sentences for specific applications simulating human-to-human conversations and interactions for problem solving or Q&As. This research studies emerging technologies for NLP-enabled intelligent chatbot development using a systematic patent analytic approach. Some intelligent text-mining techniques are applied, including document (...) term frequency analysis for key terminology extractions, clustering method for identifying the subdomains, and Latent Dirichlet Allocation for finding the key topics of patent set. This research utilizes the Derwent Innovation database as the main source for global intelligent chatbot patent retrievals. (shrink)

Information Retrieval systems benefit from the use of long queries containing a large volume of search-relevant information. This situation is not common, as users of such systems tend to use very short and precise queries with few keywords. In this work we propose a modification of the Latent Dirichlet Allocation (LDA) technique using data from the document collection and its vocabulary for a better representation of short queries. Additionally, a study is carried out on how the modification (...) of the proposed LDA weighted vectors increase the performance using relevant documents as feedback. The work shown in this paper is tested using three biomedical corpora (TREC Genomics 2004, TREC Genomics 2005 and OHSUMED) and one legal corpus (FIRE 2017). Results prove that the application of the proposed representation technique, as well as the feedback adjustment, clearly outperforms the baseline methods (BM25 and non-modified LDA). (shrink)

How can the development of ideas in a scientific field be studied over time? We apply unsupervised topic modeling to the ACL Anthology to analyze historical trends in the field of Computational Linguistics from 1978 to 2006. We induce topic clusters using Latent Dirichlet Allocation, and examine the strength of each topic over time. Our methods find trends in the field including the rise of probabilistic methods starting in 1988, a steady increase in applications, and a sharp (...) decline of research in semantics and understanding between 1978 and 2001, possibly rising again after 2001. We also introduce a model of the diversity of ideas, topic entropy, using it to show that COLING is a more diverse conference than ACL, but that both conferences as well as EMNLP are becoming broader over time. Finally, we apply Jensen-Shannon divergence of topic distributions to show that all three conferences are converging in the topics they cover. (shrink)

From the end of 2018 in China, the Big-data Driven Price Discrimination (BDPD) of online consumption raised public debate on social media. To study the consumers’ attitude about the BDPD, this study constructed a semantic recognition frame to deconstruct the Affection-Behavior-Cognition (ABC) consumer attitude theory using machine learning models inclusive of the Labeled Latent Dirichlet Allocation (LDA), Long Short-Term Memory (LSTM), and Snow Natural Language Processing (NLP), based on social media comments text dataset. Similar to the questionnaires (...) published results, this article verified that 61% of consumers expressed negative sentiment toward BDPD in general. Differently, on a finer scale, this study further measured the negative sentiments that differ significantly among different topics. The measurement results show that the topics “Regular Customers Priced High” (69%) and “Usage Intention” (67%) occupy the top two places of negative sentiment among consumers, and the topic “Precision Marketing” (42%) is at the bottom. Moreover, semantic recognition results that 49% of consumers’ comments involve multiple topics, indicating that consumers have a pretty clear cognition of the complex status of the BDPD. Importantly, this study found some topics that had not been focused on in previous studies, such as more than 8% of consumers calling for government and legal departments to regulate BDPD behavior, which indicates that quite enough consumers are losing confidence in the self-discipline of the platform enterprises. Another interesting result is that consumers who pursue solutions to the BDPD belong to two mutually exclusive groups: government protection and self-protection. The significance of this study is that it reminds the e-commerce platforms to pay attention to the potential harm for consumers’ psychology while bringing additional profits through the BDPD. Otherwise, the negative consumer attitudes may cause damage to brand image, business reputation, and the sustainable development of the platforms themselves. It also provides the government supervision departments an advanced analysis method reference for more effective administration to protect social fairness. (shrink)

Mothers use online resources frequently to obtain information on pregnancy, birth, and parenting. Yet, second-time mothers may have different concerns than first-time mothers given they have a newborn infant and another child at home. The current study conducted an on-line textual analysis of the posts of second-time mothers during pregnancy and the first months postpartum on the BabyCenter LLC website, one of the largest online parenting communities. Latent Dirichlet Allocation analysis on roughly 16,000 posts to BabyCenter birth (...) clubs in 2017 by approximately 4,000 users revealed second-time mothers relied on the online support of the BabyCenter community to share and discuss topics of pregnancy, birth, and child rearing. Second-time mothers also raised questions about preparing their firstborn children for a new baby sibling, how they would care for two children, whether they would love the second one as much as the first, and how the second child would change family dynamics. Future research needs to recognize that second-time mothers may have distinct concerns surrounding the birth of their second baby, and antenatal education and parenting classes may need to be modified to be more inclusive of these women’s needs and perspectives. Online parenting communities offer avenues to support women as they make the transition from one child to two and may provide targeted opportunities to disseminate evidence-based practices that can assist these women and their children. (shrink)

The COVID-19 pandemic has created an urgent need for volunteers to complement overwhelmed public health systems. This study aims to explore Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. To this end, we identify the latent topics in volunteerism-related microblogs on Weibo, the Chinese equivalent of Twitter using the topic modeling analysis via Latent Dirichlet Allocation. To further investigate the public sentiment toward the topics generated by LDA, we also conducted sentiment analysis on the sample (...) posts using the open-source natural language processing technique from Baidu. Through an in-depth analysis of 91,933 Weibo posts, this study captures 10 topics that are, in turn, distributed into five factors associated with volunteerism in China as motive fulfillment, fear of COVID-19, individual characteristic, government support, and community effect. The results show that motive fulfillment, government support, and community effect are the factors that could enhance positive attitudes toward volunteerism since the topics related to these factors report high proportions of positive emotion. Fear of COVID-19 and individual characteristic are the factors inducing negative sentiment toward volunteerism as the topics related to these factors show relatively high proportions of negative emotion. The provision of tailored strategies based on the factors could potentially enhance Chinese people’s willingness to participate in volunteer activities during the COVID-19 pandemic. (shrink)

With the emergence of online communities, more and more people are participating in online technology communities to meet personalized learning needs. This study aims to investigate whether and how male and female users behave differently in online technology communities. Using text data from Python Technology Community, through LDA (Latent Dirichlet Allocation) model, sentiment analysis and regression analysis, this paper reveals the different topics of male and female users in the online technology community, their sentimental tendencies and activity (...) under different topics, and their correlation and their mutual influence. The results show that: (1) Male users tend to provide information help, while female users prefer to participate in the topic of making friends and advertising. (2) When communicating in the technology community, male and female users mostly express positive emotions, but female users express positive emotions more frequently. (3) Different emotional tendencies of male and female users under different topics have different effects on their activity in the community. The activity of female users is more susceptible to emotional orientation. (shrink)

A significant portion of the world’s text is tagged by readers on social bookmarking websites. Credit attribution is an inherent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. Solving the credit attribution problem requires associating each word in a document with the most appropriate tags and vice versa. This paper introduces Labeled LDA, a topic model that constrains Latent Dirichlet Allocation by (...) defining a one-to-one correspondence between LDA’s latent topics and user tags. This allows Labeled LDA to directly learn word-tag correspondences. We demonstrate Labeled LDA’s improved expressiveness over traditional LDA with visualizations of a corpus of tagged web pages from del.icio.us. Labeled LDA outperforms SVMs by more than 3 to 1 when extracting tag-specific document snippets. As a multi-label text classifier, our model is competitive with a discriminative baseline on a variety of datasets. (shrink)

This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation and supervised LDA approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence (...) of the topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners. (shrink)

How physicians can get better ratings and more page views in online healthcare communities is an important issue. Based on 38,457 physicians' profiles from a popular online healthcare community in China, we used Latent Dirichlet Allocation model, which is a common topic model, to analyze the non-English text to obtain more doctor's latent characteristics. We found five of the most frequently mentioned topics. In addition to the first topic, “research ability,” “foreign experience,” “committee position,” and “clinical (...) experience” were included as unstructured descriptions in the doctor's profile. Inferences about physician ratings and page views could be improved if these themes were set as characteristics of physicians. Specifically, in our findings, Physicians' mentions of their “research ability” and “foreign experience” had a significant positive impact on physician ratings. Surprisingly, physicians mentioning more “clinical experience” had a significant negative impact on physician ratings. Moreover, while descriptions about “foreign experience” and “committee position” had a significant positive impact on page views, physician mentions of “research ability” had a significant negative impact on page views. These results provide new insights into the ways in which online healthcare community managers or physicians create their personal online profiles. (shrink)

Social network services such as Twitter are important venues that can be used as rich data sources to mine public opinions about various topics. In this study, we used Twitter to collect data on one of the most growing theories in education, namely Self-Regulated Learning and carry out further analysis to investigate What Twitter says about SRL? This work uses three main analysis methods, descriptive, topic modeling, and geocoding analysis. The searched and collected dataset consists of a large volume of (...) relevant SRL tweets equal to 54,070 tweets between 2011 and 2021. The descriptive analysis uncovers a growing discussion on SRL on Twitter from 2011 till 2018 and then markedly decreased till the collection day. For topic modeling, the text mining technique of Latent Dirichlet allocation was applied and revealed insights on computationally processed topics. Finally, the geocoding analysis uncovers a diverse community from all over the world, yet a higher density representation of users from the Global North was identified. Further implications are discussed in the paper. (shrink)

The COVID-19 pandemic was a global health crisis that required US residents to understand the phenomenon, interpret the cues, and make sense within their environment. Therefore, how the communication of COVID-19 was framed to stakeholders during the early stages of the pandemic became important to guide them through specific actions in their state and subsequently with the sensemaking process. The present study examines which frames were emphasized in the states' press releases on policies and other COVID information to influence stakeholders (...) on what to focus on to help with sensemaking during the crisis. We conducted content analysis on 602 press releases from 50 US states using an unsupervised machine learning approach called Latent Dirichlet Allocation (LDA). The results show that health communication using press releases to help the public make sense of the crisis were framed to include health frames as well as economic frames. Health communication messages are typically framed with health and safety measures; however, this study shows that economic frames were emphasized more than public health frames in the government's health communication for COVID-19, which forced both large and small businesses to engage in specific socially responsible activities that were previously voluntary to support public health safety. (shrink)

With the prevalence of the Internet and new media channels, consumer reviews have become one of the main determinants of Consumers’ purchasing decisions. This paper uses the Latent Dirichlet Allocation (LDA) model to identify the key factors that are of major concern to consumers, including design factors, laptop setup factors, logistics factors, after-sales factors, and user experience factors. And, we classify these factors into product quality factors and supporting service factors for new products. We then explore the (...) relationship between online reviews and purchase decisions under these different factors, and also further explore the impact of interactions between online review metrics on purchase decisions. Our findings suggest that the impact of online reviews on consumer purchase decisions also varies considerably across different consumer focus factors. In addition, we find that the impact of the interaction between online review features is complex. In particular, consumers do not follow the positive guidance and make purchase decisions as we would expect when confronted with a large number of positive emotional polarity online reviews. Meanwhile, the interaction between negative emotional polarity and variance of online reviews had no significant effect on consumer purchase decisions. The variance of online reviews has a limited role in reducing consumer risk perceptions triggered by negative emotional polarity. Our study provides new evidence for the study of the impact of online reviews through text mining. (shrink)

Automatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags from largescale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages. This paper explores the use of tags in 1) K-means clustering in an extended vector space model that includes tags as well as page text and 2) a novel (...) generative clustering algorithm based on latent Dirichlet allocation that jointly models text and tags. We evaluate the models by comparing their output to an established web directory. We find that the naive inclusion of tagging data improves cluster quality versus page text alone, but a more principled inclusion can substantially improve the quality of all models with a statistically signifi- cant absolute F-score increase of 4%. The generative model outperforms K-means with another 8% F-score increase. (shrink)

Mobile app stores provide an extremely rich source of information on app descriptions, characteristics, and usage, and analyzing these data provides insights and a deeper understanding of the nature of apps. However, manual analysis of this vast amount of information on mobile apps is not a simple and straightforward task; it is costly in terms of human effort and time. Computational methods such as topic modeling can provide an efficient and satisfactory approach to mobile app information analysis. Topic modeling is (...) a type of statistical modeling technique for discovering abstract topics that occur in a set of documents. This study explores the relationship between features of Arabic apps and investigates how well the current predefined Google Play app categories represent the type and genre of Arabic mobile apps. Based on the textual app description analysis, we aim to design and develop a sustainable classification system using the Latent Dirichlet Allocation method of topic modeling in order to cover the Arabic apps classification in Google Play app store. Our study supports the hypothesis that the textual app descriptions are effective in suggesting new categories for Arabic mobile apps in Google Play app store. Also, the results indicated that the current classification on Google Play app store is not suitable for our case study “Arabic apps,” as well as it is not sustainable, as it can not cover the new app types including Arabic apps. This study offers an important contribution to Arabic app analysis and design, to improve app search and exploration in several domains such as business, marketing, and technical development. Furthermore, it provides insights for the future of Arabic app research and provides guidance for the development of an Arabic app dashboard that will support users on how to select an app based on their specific needs. (shrink)

Environmental, social, and governance is an indicator that measures a company’s non-financial performance. Many firms have recently emphasized the importance of ESG. Ascertaining what topics are being discussed around ESG and how they change over time will contribute significantly to gaining insight into ESG. Using 73,397,870 text data scraped and refined from publicly available Twitter data, this study applied Latent Dirichlet Allocation and the dynamic topic model to ascertain the hidden structure of the ESG-related document collection and (...) the topics being discussed. The study further conducts a sentiment analysis to examine the sentiment of the general public regarding ESG. Topic modeling shows that various topics regarding ESG are being discussed and evolve over time. Sentiment analysis shows that many people have neutral or positive sentiments toward ESG-related issues. This study contributes to exploring insights into ESG among the public and understanding public reactions toward ESG. We further conclude the study with a discussion of managerial implications and potential future research. (shrink)

This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not eliminate the (...) researchers’ judgments from the process of producing evidence for social scientific theories. The paper shows this by distinguishing between two prevalent attitudes toward topic modeling, i.e., topic realism and topic instrumentalism. Under neither can modeling provide social scientific evidence without the researchers’ interpretive engagement with the original text materials. Thus the unsupervised text analysis cannot improve the objectivity of interpretation by alleviating the problem of underdetermination in interpretive debate. The paper argues that the sense in which unsupervised methods can improve objectivity is by providing researchers with the resources to justify to others that their interpretations are correct. This kind of objectivity seeks to reduce suspicions in collective debate that interpretations are the products of arbitrary processes influenced by the researchers’ idiosyncratic decisions or starting points. The paper discusses this view in relation to alternative approaches to formalizing interpretation and identifies several limitations on what unsupervised learning can be expected to achieve in terms of supporting interpretive work. (shrink)

Sustainable tourism is essential to improving the preservation of cultural property since it encourages ethical travel, helps local communities, and raises money for conservation initiatives. It guarantees the preservation of cultural treasures for future generations while promoting awareness and appreciation of them. In this paper, the relationship between sustainable tourism practice, environmental policies, tourist education, community involvement, tourism revenue allocation, and cultural heritage preservation were evaluated using structural equation modeling (SEM) analysis. Five hypotheses were developed and the dataset includes (...) 232 respondents. A systematic questionnaire with a Likert scale rating was used to evaluate the data. As a result, latent construct validity and reliability were examined, and the structural model evaluated the connections between the constructs in the context of the suggested hypotheses. The structural model revealed that EP, STP, and TE are positively connected to the CHP, providing support for hypothesis 1: Cultural Heritage Preservation and Sustainable Tourism Practice (CHP→STP) and 3:Cultural Heritage Preservation and Environmental Policies (CHP→EP) are well supported and show no significant changes to enhancing heritage preservation. (shrink)

The COVID-19 pandemic that struck New York City in the spring of 2020 was a natural experiment for the clinical ethics services of NewYork-Presbyterian (NYP). Two distinct teams at NYP’s flagship academic medical centers—at NYP/ Columbia University Medical Center (Columbia) and NYP/ Weill Cornell Medical Center (Weill Cornell)—were faced with the same pandemic and operated under the same institutional rules. Each campus used time as an heuristic to analyze our collective response. The Columbia team compares consults during the pandemic with (...) the same period during the year prior. The Weill Cornell service describes the phases of the pandemic to depict its temporal evolution and subsequent ethical challenges. Both sites report that the predominant ethical challenges centered around end-of-life decision making, setting goals of care, and medical futility, all complicated by resource allocation questions and the ambiguity of state law under crisis standards of care. The Columbia campus saw a statistically significant increase in ethics consultations provided to Hispanic patients, perhaps reflective of the disproportionate burden of COVID-19 suffered by this demographic. While Weill Cornell and Columbia saw a surge in clinical ethics consultations, the two services assumed a more expansive role than one normally played in institutional life. Serving as intermediaries between frontline clinicians and senior hospital administrators, consultants provided critical intelligence to hospital leadership about the evolution of the pandemic, disseminated information to clinicians, and attended to the moral distress of colleagues who were asked to provide care under truly extraordinary circumstances. The COVID-19 surge in New York City revealed latent capabilities in ethics consultation that may prove useful to the broader clinical ethics community as it responds to the current pandemic and reconceptualizes its potential for future service. (shrink)

The Continuous Parameter Estimation Model is applied to develop individual genomic-level heritabilities for the latent hierarchical structure and developmental dynamics of Life History (LH) strategy LH strategies relate to the allocations of bioenergetic resources into different domains of fitness. LH has moderate to high population-level heritability in humans, both at the level of the high-order Super-K Factor and the lower-order factors, the K-Factor, Covitality Factor, and General Factor of Personality (GFP). Several important questions remain unexplored. We developed measures of (...) genome-level heritabilities employing an American sample of 316 monozygotic (MZ) and 274 dizygotic (DZ) twin dyads and a Swedish sample of 863 MZ and 475 DZ twin dyads. This novel heritability index measures individual genetic transmissibility, therefore opening new avenues for analyzing complex interactions among heritable traits inaccessible to standard structural equations methods. For these samples: (1) moderate to high heritability of factor loadings of Super-K on its lower-order factors is demonstrated, evidencing biological preparedness, genetic accommodation, and the gene-culture coevolution of biased epigenetic rules of development; (2) moderate to high heritability of the magnitudes of the effect of the higher-order factors upon their loadings on their constituent factors, evidencing genetic constraints upon phenotypic plasticity; and (3) that heritability of the LH factors, of factor loadings, and of the magnitudes of the correlations among factors are weaker among those with slower LH speeds, demonstrating that inter-individual variation in transmissibility is a function of individual socioecological selection pressures. (shrink)

The World Health Organization (WHO) is currently in advanced stages of developing a ‘WHO convention, agreement, or other international instrument on pandemic prevention, preparedness and response’ (also known as WHO CA+, referred to below as: Pandemic Agreement).1 Rightly, the instrument places equity at the centre. Yet, it currently also omits reference to an impactful tool to promote equity that has been adopted in an unprecedented manner during COVID-19—a set of measures known as disadvantage indices. Embedding disadvantage indices would provide concrete (...) constructive guidance; align the Pandemic Agreement with the use of indices by other United Nations (UN) agencies; help realise the Agreement’s goal of addressing social and other determinants of health within countries, and furthermore offer feasible ways improving equity in allocation across countries. WHO member states began work on a Pandemic Agreement in December 2021. The current timeline envisages a final version to be considered by the May 2024 World Health Assembly.1 The publicly available version of the Pandemic Agreement at the time of writing comprises 36 articles under three separate chapters (Bureau’s Text, A/INB/7/3, 30 Oct 2023).1 Chapter 1 provides general scene setting describes objectives, and highlights key terms. While the concept of equity is still somewhat implicit and would benefit from further articulation,2 overall, there seems to be a latent understanding that inequity obtains when there are unfair differences in health outcomes and opportunities within and across countries. Correspondingly, ‘[e]quity is at the centre of pandemic prevention, preparedness, response, both at the national level within States and at the international level between States. It requires, inter alia, specific measures to protect persons in vulnerable situations’.1 Chapter 3 addresses procedural issues, such as the establishment and rules of interactions of different types of advisory committees. Chapter 2 is titled ‘The world together equitably: …. (shrink)

This commentary explores the potential consequence of latent racial formation in emergent climate finance data projects and draws from ethnographic research on climate finance governance conducted in Fiji. Climate finance data projects emerging in the Pacific aim to ease the flow of finance from the Global North to the South. These emergent data projects, such as renewable energy resource availability and investment mapping, are imbedded in the climate finance organizations that fund, develop, and use them. Thus, the commentary explores (...) climate finance organizations through the lens of Ray’s theory of racial organizations, highlighting the ways in which important climate-related resources are mediated through racial and colonial schemas. The racial mediation of two key resources are spotlighted in this discussion: the finance itself and knowledge. Given that the Pacific region is at the coalface of climate change’s existential effects, the just allocation of resources is imperative. In interrogating the ways in which emergent data projects may deny these resources based on hidden racial schemas, the paper cautions against new and old forms of colonization that may be mobilized through even well-meaning techno-benevolent fixes. (shrink)

Unlike other states, California retained a large proportion of the child care centers that had been established during World War II. In 1946, the California state government allocated state funds for child care in response to a vigorous child care campaign. The campaign, which was, in large part, a working mothers movement, was a “transformed maternalist” movement. It used maternalist rhetoric to defend state-subsidized child care that was criticized by more traditional maternalists. Using resource mobilization theory, I explain the relatively (...) high degree of political mobilization on the child care issue in California in terms of the greater availability of co-optable social resources, which California's relatively large wartime child care program provided, and the existence of movement entrepreneurs willing to mobilize these latent resources. Child care advocates were successful because they operated within a relatively favorable political opportunity structure since they did not have to contend with a strained state treasury or a mobilized countermovement and were able to garner the support of a wide variety of women's and social organizations and an influential social reformer. (shrink)

Four ethical values — maximizing benefits, treating equally, promoting and rewarding instrumental value, and giving priority to the worst off — yield six specific recommendations for allocating medical resources in the Covid-19 pandemic: maximize benefits; prioritize health workers; do not allocate on a first-come, first-served basis; be responsive to evidence; recognize research participation; and apply the same principles to all Covid-19 and non–Covid-19 patients.

Background Hospital ethical climate has important implications for clinical nurses’ service behavior; however, the relationships are complicated by the fact that five types of ethical climate (caring, law and code, rules, instrumental, and independence) can be combined differently according to their level and shape differences. Recent developments in person-centered methods (e.g., latent profile analysis (LPA)) have helped to address these complexities. Aim From a person-centered perspective, this study explored the distinct profiles of hospital ethical climate and then examined the (...) relationships of the profiles with clinical nurses’ service behavior (both in-role and extra-role service behavior). Research design A quantitative study was conducted using cluster random sampling. Latent profile analysis and binary coded hexadecimal (BCH) analysis were conducted using Mplus 8.2. Participants and research context A total of 871 clinical nurses in China were surveyed using the Ethical Climate Scale and Nurses’ Service Behavior Questionnaire. Ethical considerations Ethical approval was obtained from the IRB of the First Affiliated Hospital of Jinan University (No. KY-2020-090). Results A four-profile hospital ethical climate model provided the best fit for the data. The four different profiles not only varied in level, but also in shape: high normative and low egoism (45.8%), high ethical climate (19.9%), low ethical climate (3.6%), and moderate ethical climate (30.8%). These profiles differentially predicted clinical nurses’ overall, in-role, and extra-role service behaviors. Conclusions The results reveal new insights into the nature of hospital ethical climate and how different clinical nurses in these profiles can be best managed to accomplish different forms of service behavior. (shrink)

Volume 20, Issue 7, July 2020, Page 199-201.

Background: The three-dimensional model of nurses’ moral sensitivity has typically been studied using a variable-centered rather than a person-centered approach, preventing a more complete understanding of how these forms of moral sensitivity are expressed as a whole. Latent profile analysis is a person-centered approach that classifies individuals from a heterogeneous population into homogeneous subgroups, helping identify how different subpopulations of nurses use distinct combinations of different moral sensitivities to affect their service behaviors. Objective: Latent profile analysis was used (...) to identify three distinct profiles of nurses’ moral sensitivity. Associations of the profiles with service behaviors were then examined. Methods: Five hundred twenty-five nurses from three tertiary hospitals in China were investigated with Moral Sensitivity Questionnaire and Nurses’ Service Behavior Scale. Latent profile analysis was used to analyze the data. Ethical considerations: Approval was obtained from the Ethics committee for biomedical research of Medical College, the Hebei University of Engineering. Results: A three-profile moral sensitivity model provided the best fit to the data. The resulting profiles were low moral sensitivity, moderate moral sensitivity, and high moral sensitivity. There were significant differences in service behaviors among different profiles of moral sensitivity. Conclusion: The results provide a new and expanded view of nurses’ moral sensitivity, which may be used to monitor nurses’ service behaviors comprehensively and to evaluate nursing ethics management strategies. (shrink)