Structural topic model

A key tenet from research on geographical concepts is that these are never neutral but filled with different ideas and agendas. The 'Carpathian Basin' is one of the most significant concepts in Hungarian geographical thought, but its recently reemerging use in political discourse has not yet been studied through quantitative text analysis. In this paper, we describe how a structural topic model was used to analyze the 1,525 speeches containing the term delivered in the Hungarian Parliament between 1998 and 2020. Our results indicate a renaissance in the use of the term, both in terms of its more frequent use and its discursive meaning as a sign of a turn in national policy. At the same time, 'Carpathian Basin' discourse serves as a symbolic battleground for different political ideologies to indicate both neutral geographical references and nationalist sentiments. Left-liberals tend to use it politically neutrally, referring to an ethno-culturally heterogeneous area, and using a less personal voice, referring to institutions and interests. In contrast, right-wing narratives often demarcate the Carpathian Basin as a single geographical entity. Some of these speeches exhibit virtual nationalism, while others subtly question territorial legitimacy. The latter MPs speak in terms of representing their own community, referring to values, emotions, and culture, offering a collective identity to which people attach values and emotions.

Patient experience surveys have become a key source of evidence for supporting decision-making and continuous
quality improvement within healthcare services. To harness free-text feedback collected as part of these surveys
for additional insights, text analytics methods are increasingly employed when the data collected is not amenable
to traditional qualitative analysis due to volume. However, while text analytics techniques offer good predictive
capabilities, they have limited explanatory features often required in formal decision-making contexts, such as
programme monitoring or evaluation. To overcome these limitations, this study integrates computational text
and predictive modelling as part of a Computational Grounded Theory method to determine the effect of quality
gaps in care dimensions and their prioritisation from free-text feedback. The feedback was collected as part of a
national survey to support decisions on continuous improvement in Maternity Services in Ireland. Our approach
enables (1) operationalising the service quality lexicon in the context of maternity care to explain the effect of
quality gaps in care dimensions on overall satisfaction from free-text comments; and (2) extending the service
quality lexicon with two organisational and political decision-making concepts: “Salience” and “Valence”, for
prioritising perceived quality gaps. These methodological affordances enable the extension of service quality
theory to explicitly support the prioritisation of improvement decisions which before now required additional
decision frameworks. Results show that tangibles-, process-, and reliability-related care issues have the highest
importance in our study context. We also find that hospital contexts partly determine the relative importance of
gaps in care dimensions.

Social media are fundamental in creating new opportunities for firms and they represent a relevant tool for the communication and the engagement with customers. The purpose of this paper is to analyse the communication of Corporate Social Responsibility (CSR) activities on Twitter. We consider the listed companies included in the Dow Jones Industrial Average Index and we implement a topic model analysis on their timelines. In order to identify the topic discussed, their correlation, and their evolution over time and sectors, we apply the Structural Topic Model algorithm, which allows estimating the model including document-level metadata. This model proves to be a powerful tool for topic detection and for estimating the effects of document-level metadata. Indeed, we find that the topics are overall well identified, and the model allows catching signals from the data. Finally, we discuss issues related to the validity of the analysis, including data quality problems.

While depression is globally on the rise, the mental health sector struggles with handling the increased number of cases, especially since the pandemic. These circumstances have resulted in an increased interest in the e-mental health sector. The dataset is constituted of 67 857 posts from the most popular English-language online health forums between 15 February 2016 and 15 February 2019. The posts were first automatically labelled (biomedical vs. psy framing) via deep learning; second, the time series of framing types of recurring forum users were analysed; third, the clusters of biomedical and psy patterns were analysed; fourth, the discursive characteristics of each cluster were analysed with the help of topic modelling. Five ideal-typical patterns of forum socialization are described: the first and the second clusters express the developing of a ‘recovery helper’ role, either by opposing expert discourses or by identifying with the psy discourses; the third cluster expresses th...

Background: One of the most comprehensive approaches to depression is the biopsychosocial model. From this wider perspective, social sciences have criticized the reductionist biomedical discourse, which has been dominating expert discourses for a long time. As these discourses determine the horizon of attributions and interventions, their lay interpretation plays a central role in the coping with depression. Methods: In order to map these patterns, online depression forums are analyzed with natural language processing methods, where computational tools are complemented with a qualitative approach. Latent Dirichlet Allocation topic model of depression-related posts from the most popular English-speaking online health discussion forums (N =~70 000) reveals the monolog (attributions and self-disclosures) and interactive (consultations and quasitherapeutic interactions) patterns. Results: Following the evaluation of various models 18 topics were differentiated: attributions referring to health, family, partnership and work issues; self-disclosures referring to contemplations, introducing the experience of suffering and well-being, along with diaries of everyday activities and hardships; consultations about psychotherapies, classifications, drugs and the experience; and quasi-therapeutic interactions relying on unconditional positive regards, recovery helpers experience or spirituality. These topics were evaluated from the perspective of the biopsychosocial model: the weight of each dimension was measured along with the discursive function. Conclusions: Biomedical discourse is underrepresented in lay discussions, while psychological discourse plays an overall dominant role. Even if actors are initially aware of the social mechanisms contributing to depression, they neglect these factors when it comes to considering the countermeasures.

The article aims at exploring the impact of the Covid pandemic on the lay discourses of depression emerging in online mental health forums. The narrative framing of depression plays a central role not only because it affects the instrumental strategies of the depressed subjects (e.g. preferred therapy), but also because it is a constitutive element of the identity of depressed subjects, thus affects the process of recovery itself. Covid had a serious impact on people living with mental disorders (especially depression and anxiety), thus our research aimed at mapping the consequences of these transformations on discursive level. A textual dataset of the most popular, English language online health forums was collected (n=339,550 publicly available entries posted between 15 February 2016 and 31 December 2020). Structural topic modelling was used to explore the various discursive patterns characterising the pre-and post-covid era. Our results show that the pandemic did not take over the discursive space of depression forums, yet it transformed many aspects of it: a new horizon of critique opened up; the biomedical authority was reinforced; the ego-centric perspectives were refined; the previously unquestionable discursive frames become fragmented; and the horizon of emergency overshadowed the previous risk perspective.

We develop the Structural Topic Model which provides a general way to incorporate corpus structure or document metadata into the standard topic model. Document-level covariates enter the model through a simple generalized linear model framework in the prior distributions controlling either topical prevalence or topical content. We demonstrate the model's use in two applied problems: the analysis of open-ended responses in a survey experiment about immigration policy, and understanding differing media coverage of China's rise. * Prepared for the NIPS 2013 Workshop on Topic Models: Computation, Application, and Evaluation. A forthcoming R package implements the methods described here.

There is growing interest in applying computational methods in analysing large amount of data without sacrificing rigour in Information Systems research. In this paper, we demonstrate how the use of structural and temporal topic modelling can be employed to produce insights of both theoretical and practical importance from the analysis of textual comments on the quality of services in hospitals. As a first step, we revealed the thematic structures in the comments as topics which were aligned with the SERVQUAL dimensions. Following this, we established the temporal precedence among SERVQUAL factors based on the evolution of the topics over time. Theoretically, our findings are consistent with the emerging consensus on the nature of SERVQUAL dimensions from extant quantitative research and offer new propositions on the relationships among these dimensions. From the practice perspective, we produced quantified measures of factors associated with healthcare service experience.

The main purpose of this research is to examine the impacts of service quality dimensions on patient satisfaction as a study of private hospitals in Nepal. This research has also evaluated the perceptions of the patients about service quality offered by private hospitals and level of their satisfaction. The research was conducted through influence of positivism philosophy and deductive approach. The two hypotheses were generated with the help of existing knowledge and literature related to service quality and customer/patient satisfaction. The formulated hypotheses were tested through primary data collection from the customers/patients of private hospitals in Nepal. The questionnaires survey was used to collect data in this study. Different five factors of SERVQUAL dimension and four factors of patient satisfaction: price, image, perceived quality and convenience were used to analyse service quality and patient satisfaction respectively. Samples of 490 patients from different 10 private hospitals which all are based on Kathmandu were considered in the study. The results indicated that each dimension of service quality is positively correlated with patient satisfaction, and overall service quality and patient satisfaction has correlation coefficient of 0.719 which is significant at the 0.01 level. This implies that service quality and patient satisfaction are positively correlated with each other. The results demonstrated that service quality has strong positive impacts on patient satisfaction as regression analysis demonstrates that service quality has significant impacts on patient satisfaction as β = 0.719 and P = 0.000 which is less than 0.05. Similarly, the results also indicated that private hospitals in Nepal are providing high level of service quality in the view of patients/customers; and there is high level of patient satisfaction at the private hospitals in Nepal.