Explore the vast range of titles available.

Sustainable water governance is crucial for addressing the global water crisis and ensuring access to clean water resources. In the Indonesian context, Riau Province faces significant challenges in providing sufficient clean water to its population. Collaborative approaches involving diverse actors have emerged as a potential solution to complex water governance problems. However, limited empirical evidence exists regarding the engagement and interactions of these actors in decision-making processes. This study focuses on Bengkalis Regency, Dumai City, and Rokan Hilir Regency, in Riau Province, using Textual Network Analysis (TNA) to identify key actors in local water governance. The findings of this study highlight: (1) The influential actors (nodes) identified by TNA consist of drinking water systems, financial arrangements, oversight mechanisms, environmental concerns, water accessibility, and eco-friendly water governance. These actors nuance the formation of local policies related to Durolis water governance. (2) The Riau provincial government is empowered to fund pipanization projects from the river to the cities. Meanwhile, local governments are given financial responsibility for pipanization in their respective regions. (3) Durolis water governance follows a centralized approach, with the provincial government acting as a facilitator when problems arise. Meanwhile, problem-solving is based on consensus between the regions as a decision-making tool.

Most category studies have focused on established categories with discrete boundaries. These studies not only beg the question of how a de novo category arises, but also upon what institutional material actors draw to create a de novo category. We examine the formation and theorization of the de novo category “modern architecture” between 1870 and 1975. Our study shows that the process of new category formation was driven by groups of architects with distinct clientele associated with institutional logics of commerce, state, religion, and family. These architects enacted different artifact codes for a building based on institutional logics associated with their specific mix of clients. “Modern architects” fought over what logics and artifact codes should guide “modern architecture.” Modern functional architects espoused a logic of commerce enacted through a restricted artifact code of new materials in a building, whereas modern organic architects advocated transforming the profession's logic enacted through a flexible artifact code of mixing new and traditional materials in buildings. The conflict became a source of creative tension for modern architects that followed, who integrated aspects of both logics and materials in buildings, expanding the category boundary. Plural logics and category expansion resulted in multiple conflicting exemplars within “modern architecture” and enabled its adaptation to changing social forces and architectural interpretations for over 70 years.

This study discusses the design of collaborative rural governance strategies to enhance the rural economy through Village-owned Enterprises (VOE) in Riau Province, Indonesia. Using Soft Systems Methodology (SSM) combined with Textual Network Analysis (TNA) in the Rich Picture stage of SSM, we investigated the current state of VOE management. Significant obstacles identified include insufficient business feasibility analyses, lack of managerial skills, misalignment between strategy and practice, and inadequate oversight. To address these challenges, we propose a collaborative strategy involving regional governments, academic institutions, NGOs, and the private sector. This strategy emphasizes community needs assessments, efficient resource mobilization, and targeted training programs. A dedicated working group will ensure continuous monitoring and iterative improvements. Our research highlights the novel integration of SSM with TNA, providing a robust framework for improving VOE management and demonstrating the potential of collaborative efforts in driving rural economic development.

Rural governance is crucial for sustainable development but often faces challenges like limited resources, geographic isolation, and diverse stakeholder interests. This research addresses the gap in integrating collaborative governance frameworks within rural settings by applying Soft System Methodology (SSM) and Textual Network Analysis (TNA) to enhance governance strategies. Combining SSM, a methodology designed for complex problems, with TNA, the research identifies key factors such as inclusivity, communication, and capacity building as essential for effective stakeholder engagement in rural governance. This integrated approach ensures that governance strategies remain adaptable to evolving challenges. The findings demonstrate that applying SSM and collaborative governance frameworks improves rural governance practices by creating more participatory and responsive systems that better meet the needs of rural communities. This contribution provides a practical solution to enhancing governance in rural areas, leading to sustainable improvements.

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

Research Summary We demonstrate how a novel synthesis of three methods—(a) unsupervised topic modeling of text data to generate new measures of textual variance, (b) sentiment analysis of text data, and (c) supervised ML coding of facial images with a cutting‐edge convolutional neural network algorithm—can shed light on questions related to CEO oral communication. With videos and corresponding transcripts of interviews with emerging market CEOs, we use this synthesis of methods to discover five distinct communication styles that incorporate both verbal and nonverbal aspects of communication. Our data comprises interviews that represent unedited expressions and content, making them especially suitable as data sources for the measurement of an individual's communication style. We then perform a proof‐of‐concept analysis, correlating CEO communication styles to M&A outcomes, highlighting the value of combining text and videographic data to define styles. We also discuss the benefits of using our methods versus current research methods. Managerial Summary CEOs spend most of their time communicating to investors, customers, and partners with the aim of influencing these various stakeholders. To what extent though does their effectiveness as leaders depend on a mixture of what they say and how they say it? We use cutting‐edge machine learning approaches to measure a CEO's communication style, which can give clues about the major strategic decisions a CEO's firm must make. With a collection of video interviews with 61 organizational leaders from emerging markets, we use textual analysis and facial image expression recognition to code whether CEOs are “excitable,” “stern,” “dramatic,” “rambling,” and “melancholy” in their communication styles. As a proof‐of‐concept, we also show that CEOs who were more dramatic in expressing themselves were also less likely to oversee major acquisitions. Therefore, not only can CEO communication styles help predict a firm's ability to grow, adapt to change, and reallocate existing assets, styles can also be coded more intuitively by using our new method, representing a vast improvement over previous methods in both accessibility and interpretability.

Social media has become a popular venue for individuals to share the results of their own analysis on financial securities. This paper investigates the extent to which investor opinions transmitted through social media predict future stock returns and earnings surprises. We conduct textual analysis of articles published on one of the most popular social media platforms for investors in the United States. We also consider the readers' perspective as inferred via commentaries written in response to these articles. We find that the views expressed in both articles and commentaries predict future stock returns and earnings surprises.

Over the last decade, the amount of Arabic content created on websites and social media has grown significantly. Opinions are shared openly and freely on social media and thus provide a rich source for trend analyses, which are accomplished by conventional methods of language interpretation, such as sentiment analysis. Due to its accuracy in studying unstructured data, deep learning has been increasingly used to test opinions. Recurrent neural networks (RNNs) are a promising approach in textual analysis and exhibit large morphological variations. In total, 193 studies used RNNs in English-language sentiment analysis, and 24 studies used RNNs in Arabic-language sentiment analysis. Those studies varied in the areas they address, the functionality and weaknesses of the models, and the number and scale of the available datasets for different dialects. Such variations are worthy of attention and monitoring; thus, this paper presents a systematic examination of the literature to label, evaluate, and identify state-of-the-art studies using RNNs for Arabic sentiment analysis.

What rhetorics run throughout radical discourse, and why do some gain prominence over others? The scholarship on radicalism largely portrays radical discourse as opposition to powerful ideas and enemies, but radicals often evince great interest in personal and local concerns. To shed light on how radicals use and adopt rhetoric, we analyze an original corpus of more than 23,000 pages produced by Afghan radical groups between 1979 and 2001 using a novel computational abductive approach. We first identify how radicalism not only attacks dominant ideas, actors, and institutions using a rhetoric of subversion, but also how it can use a rhetoric of reversion to urge intimate transformations in morals and behavior. Next, we find evidence that radicals’ networks of support affect the rhetorical mixture they espouse, due to social ties drawing radicals into encounters with backers’ social domains. Our study advances a relational understanding of radical discourse, while also showing how a combination of computational and abductive methods can help theorize and analyze discourses of contention.

First detected in Wuhan, China in December 2019, the COVID-19 pandemic stretched the medical system in Wuhan and posed a challenge to the state's risk communication efforts. Timely access to quality health care information during outbreaks of infectious diseases can be effective to curtail the spread of disease and feelings of anxiety. Although existing studies have extended our knowledge about online health information-seeking behavior, processes, and motivations, rarely have the findings been applied to an outbreak. Moreover, there is relatively little recent research on how people in China are using the internet for seeking health information during a pandemic. The aim of this study is to explore how people in China are using the internet for seeking health information during a pandemic. Drawing on previous research of online health information seeking, this study asks the following research questions: how was the \"#COVID-19 Patient Seeking Help\" hashtag being used by patients in Wuhan seeking health information on Weibo at the peak of the outbreak? and what kinds of health information were patients in Wuhan seeking on Weibo at the peak of the outbreak? Using entity identification and textual analysis on 10,908 posts on Weibo, we identified 1496 patients with COVID-19 using \"#COVID-19 Patient Seeking Help\" and explored their online health information-seeking behavior. The curve of the hashtag posting provided a dynamic picture of public attention to the COVID-19 pandemic. Many patients faced difficulties accessing offline health care services. In general, our findings confirmed that the internet is used by the Chinese public as an important source of health information. The lockdown policy was found to cut off the patients' social support network, preventing them from seeking help from family members. The ability to seek information and help online, especially for those with young children or older adult members during the pandemic. A high proportion of female users were seeking health information and help for their parents or for older adults at home. The most searched information included accessing medical treatment, managing self-quarantine, and offline to online support. Overall, the findings contribute to our understanding of health information-seeking behaviors during an outbreak and highlight the importance of paying attention to the information needs of vulnerable groups and the role social media may play.