Text Analysis Tools Research Articles

OP74 Assessing Public Confidence Towards COVID-19 Vaccines Through Social Media Insights Leveraged Using Artificial Intelligence Techniques

IntroductionIn areas where public confidence is low and there is a lack of understanding around behaviors, such as COVID-19 vaccine hesitancy, there is a need to explore novel sources of evidence. When leveraged using artificial intelligence (AI) techniques, social media data may offer rich insights into public concerns around vaccination. Currently, sources of ‘soft-intelligence’ are underutilized by policy makers, health technology assessment (HTA) and other public health research agencies. In this work, we used an AI platform to rapidly detect and analyze key barriers to vaccine uptake from a sample of geo-located tweets.MethodsAn AI-based tool was deployed using a robust search strategy to capture tweets associated with COVID-19 vaccination, posted from users in London, United Kingdom. The tool’s algorithm automatically clustered tweets based on key topics of discussion and sentiment. Tweets contained within the 12 most populated topics with negative sentiment were extracted. The extracted tweets were mapped to one of six pre-determined themes (safety, mistrust, under-representation, complacency, ineffectiveness, and access) informed using the World Health Organization’s 3Cs vaccine hesitancy model. All collated tweets were anonymized.ResultsWe identified 91,473 tweets posted between 30 November 2020 and 15 August 2021. A sample of 913 tweets were extracted from the twelve negative topic clusters. Of these, 302 tweets were coded to a vaccine hesitancy theme. ‘Safety’ (29%) and ‘mistrust’ (23%) were the most commonly coded themes; the least commonly coded was ‘under-representation’ (3%). Within the main themes, adverse reactions, inadequate assessment, and rushed development of the vaccines as key findings. Our analysis also revealed widespread sharing of misinformation.ConclusionsUsing an AI-based text analytics tool, we were able to rapidly assess public confidence in COVID-19 vaccination and identify key barriers to uptake from a corpus of geo-located tweets. Our findings support a growing body of evidence and confidence surrounding the use of AI tools to efficiently analyze early sources of soft-intelligence evidence in public health research.

Paralanguage Classifier (PARA): An Algorithm for Automatic Coding of Paralinguistic Nonverbal Parts of Speech in Text

Brands and consumers alike have become creators and distributors of digital words, thus generating increasing interest in insights to be gained from text-based content. This work develops an algorithm to identify textual paralanguage, defined as nonverbal parts of speech expressed in online communication. The authors develop and validate a paralanguage classifier (called PARA) using social media data from Twitter, YouTube, and Instagram (N = 1,241,489 posts). Using auditory, tactile, and visual properties of text, PARA detects nonverbal communication cues, aspects of text often neglected by other word-based sentiment lexica. This work is the first to reveal the importance of textual paralanguage as a critical indicator of sentiment valence and intensity. The authors further demonstrate that automatically detected textual paralanguage can predict consumer engagement above and beyond existing text analytics tools. The algorithm is designed for researchers, scholars, and practitioners seeking to optimize marketing communications and offers a methodological advancement to quantify the importance of not only what is said verbally but how it is said nonverbally.

Water Policy Evaluation Based on the Multi-Source Data-Driven Text Mining: A Case Study of the Strictest Water Resource Management Policy in China

The strictest water resources management (SWRM) policy is a critical policy to address China’s severe water shortage and pollution problems, and aims to promote sustainable water development and water governance. Based on data mining from multiple sources, including policy text from the strictest water resource management policy from 2011 to 2021, the reports of major media websites, and the Baidu Index, this study used the ROST-CM6 text-analysis tool to analyze the policy content, public opinion, and public perception of the strictest water resources management policy quantitatively and visually. The results found that the policy text and public-opinion are given high attention to the water resources assessment, water control management, and water resources protection, but the policy text focuses on the macro level, and pays more attention to national development and long-term planning. The public opinion belongs to the micro level and is more economic, and there is a certain degree of media bias. With notable regional disparities, the general public’s opinion of the harshest water resource management policy has been rising every year. This research adds to the global body of knowledge on water governance, and serves as a guide for Chinese and other governments looking to improve their water resource management strategies.

What does ownership structure tell us about Integrated Reporting of Polish listed companies?

This paper aims to link the quantity and quality of information disclosed in Integrated Reports of the Polish listed companies with their ownership structure, through which we explore the manifestation of governance mechanism. Our study investigates two main problems in the literature concerning factors and logical relations explaining companies' disclosure patterns in the information reported to stakeholders; the first concerns measures of the quantity and quality of disclosure, and the second is focused on the explanation of the relations. Our paper addresses both issues by proposing an application of advanced textual analysis tools to assess the quantity and quality of the information disclosed. Second, contrasting to the great majority of existing research-based almost exclusively on correlation methods with linearity assumptions, this study presents a practical implementation of the QCA tool to analyse how the ownership structure influences the company disclosure patterns. The results obtained in this study may be of great importance to the governance of companies, IR prepares and regulators.

Using Textual Analysis Tools to Identify Trends in Privacy Concern Research

ABSTRACTText mining is a research method that uses interdisciplinary techniques to identify patterns and trends in unstructured data and can be used to detect research trends and developments in various fields. Thus, this study attempts to identify research trends in the issue of privacy concern (PC), focusing on the differences between the first two decades of the 21st century. This is done by applying an automated textual analysis tool (Voyant Tools) on a vast corpora of academic papers conducted on this field. We found that while the first examined decade of PC research focused on commerce and marketing issues, there was much more research emphasis on the social dimension of privacy in the second decade. This difference was explained by the technological developments that were prominent in the respective decades. The research provided support for the use of textual analysis of academic literature as a tool to identify research trends in various fields, including technologically‐oriented fields.

Empathy and EGO-drive in the B2B salesforce: Impacts on job satisfaction

It has long been assumed that empathy and ego-drive are two fundamental characteristics of effective salespeople. Taking a wholistic view of sales effectiveness to include job satisfaction in sales positions, this research assesses the relationship between salesperson empathy and ego-drive and sales job satisfaction. While evidence suggests that high empathy and high ego-drive are predictors of sales performance, this does not always translate to job satisfaction based on our findings. Using the automated text analysis tool, Linguistic Inquiry and Word Count (LIWC) to analyze company reviews written by B2B salespeople, our results show that B2B salespeople who are most satisfied with their jobs possess high levels of empathy but low levels of ego-drive.

Machine Learning Tool to Predict Student Categories After Outlier Removal

Statistical outlier detection techniques uses academic performance oriented results to find the truly brilliant as well as the weakest amongst a colony of students. Machine Learning allows further partitions within the remaining student community, based on both merit and personality. Present work proposes a decision tree model for predicting three more appropriate categories. It utilizes Text Analytic tools to assess student characteristic traits from their textual responses and feedbacks. The cream of the general pool is chosen to belong to a top class comprising the mentor group, provided they can academically assist the weaker of the lot. But all on the top may not be suited for mentor-ship role - textual assessment data delves to reveal character orientations favouring such decisions. The bulk who can manage their own forms the second class. The bottom of the pool benefits with assistance from the mentor group and comprise the third class.

WTO Rulings and the Veil of Anonymity

Abstract Despite a general push for greater transparency, opacity continues to play an important function in international tribunals. The World Trade Organization (WTO) is a case in point. While it has done much to increase its openness, the very design of its dispute settlement body is premised on anonymity in some essential respects. We examine two such instances, each dealing with the authorship of dispute rulings. First, we use text analysis tools to demonstrate that the WTO’s panel reports appear to be largely drafted by WTO Secretariat staff rather than the panellists themselves. This appears especially true for the WTO’s most systemically important disputes. Second, we show that the formal anonymity of dissenting opinions, which is required by the WTO’s rules, is a thin veil. Using the most recent Appellate Body’s dissent for demonstration, we use text analysis to pinpoint its likely author. In both these instances, we argue that anonymity exists by design: it serves to strike a balance between judicial autonomy and political control. Yet, in both settings, due to increased scrutiny and widespread access to text analysis tools, the equilibrium relying on anonymity is likely to be upset, with implications for the institution’s future design. We argue that the ultimate result may be a beneficial one and offer a menu of reform options.

Getting Mixed Signals? How Top Executive Communications about Firm Strategy Shape Market Evaluations

Markets have a fundamental information asymmetry problem in evaluating public organizations, as market actors lack detailed firsthand knowledge about organizational capabilities and strategies. Top executive communication helps to address this asymmetry by providing information about firm strategies that signal firm quality and intentions. In this study we use signaling theory to examine how top executive communications about different parts of their firm’s strategy provide signals to securities analysts that shape their evaluations of the company. We hypothesize that analysts, working on behalf of shareholders who are interested in stock growth, positively evaluate communication related to externally-oriented strategies that signal alignment with analyst goals for growth, and negatively evaluate communication that focuses on internally-oriented maintenance strategies that signal concerns about firm capabilities and market positioning. We then explore the potential moderating influence of factors that reduce signal efficacy through lower signal intensity or weaker signal consistency. We find support for our arguments using a novel text analysis tool in a random sample of S&P500 companies. In supplemental analysis we find that several contextual factors can also magnify or suppress signal impact, assess which specific strategies have the biggest impact, and identify different patterns for high and low performing firms.

Negative Offshoring Sentiments – An Exploratory Study Using Twitter Data

Offshoring (i.e. relocation of existing business activities and jobs from one country to another) is a central topic in international business research, but also a polarizing topic in the society. As the public is an important actor when companies make decisions to internationalize, it is indeed critical to understand the public sentiment about offshoring. To address this, I rely on Twitter data and text analytics tools. I find that offshoring sentiments are rather negative, which is associated with higher levels of tweet popularity, but also that the extent to which a tweet is negative is related to other language characteristics. Namely, a higher degree of focus on past and social issues in the used language are associated with increased levels of negative emotions. However, the more cognitive processes are present in the written text, the lower the degree of negative emotions. With populism on the rise, this study addresses the public sentiment implications of company offshoring decisions and contributes to international business research, but has implications also for politicians and managers.

Towards or not towards? Using the ACT Matrix as a psychological tool in the analysis and interpretation of dramatic texts and characters

ABSTRACT This article addresses key elements of “ACT for Acting”, an ongoing project using the psychological framework of Acceptance and Commitment Therapy/Training (ACT) to explore the processes of acting and actor training. The article introduces readers to a tool called the ACT Matrix and demonstrates how it can be used to “sort”, understand, and visualize different aspects of human experience and behaviour. It then illustrates the application of this process to a dramatic text through the discussion of a Shakespearean soliloquy: Hamlet’s “To be or not to be”. It is suggested that the ACT Matrix can provide a useful tool for textual analysis and serve as the foundation for new approaches to dramatic action and character that are grounded in twenty-first century understandings of human behaviour and psychology.

Representation of Libraries in Artificial Intelligence Regulations and Implications for Ethics and Practice

ABSTRACT We are already living in an algorithmic society. AI policies and regulations are now emerging at the same time as more is learned about the implications of bias in machine learning sets, the surveillance risks of smart cities and facial recognition, and automated decision-making by government, among many other applications of AI and machine learning. Each of these issues raises concerns around ethics, privacy, and data protection. This paper introduces some of the key AI regulatory developments to date and engagement by libraries in these processes. While many AI applications are largely emergent and hypothetical in libraries, some mature examples can be identified in research literature searching, language tools for textual analysis, and access to collection data. The paper presents a summary of how library activities such as these are represented in national AI plans and ways that libraries have engaged with other aspects of AI regulation including the development of ethical frameworks. Based on the sector's expertise in related regulatory issues including copyright and data protection, the paper suggests further opportunities to contribute to the future of ethical, trustworthy, and transparent AI.

Contrasting compulsive behaviour: Computerized text analysis of compulsion narratives.

Those who gamble compulsively, and those who shop or buy in a compulsive manner share a number of common characteristics, stemming from similar impulse-control issues. As such, it is predicted that a lexical analysis of personal narratives of compulsion would share similarities. Using secondary data from an online mental health forum, Psychforums, the research analyzed narratives of compulsive gambling (n = 199) and compulsive buying (n = 196) using the automated text analysis tool, LIWC. The results indicated that compulsive buying narratives rated significantly higher in clout and emotional tone and significantly lower in authenticity, with no significant differences noted in analytical thinking between the two compulsion narratives. Recommendations for future research include that demographic variables be incorporated and that narratives sourced from different online platforms should be contrasted.

Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media.

Anti-vaccine content is rapidly propagated via social media, fostering vaccine hesitancy, while pro-vaccine content has not replicated the opponent's successes. Despite this disparity in the dissemination of anti- and pro-vaccine posts, linguistic features that facilitate or inhibit the propagation of vaccine-related content remain less known. Moreover, most prior machine-learning algorithms classified social-media posts into binary categories (e.g., misinformation or not) and have rarely tackled a higher-order classification task based on divergent perspectives about vaccines (e.g., anti-vaccine, pro-vaccine, and neutral). Our objectives are (1) to identify sets of linguistic features that facilitate and inhibit the propagation of vaccine-related content and (2) to compare whether anti-vaccine, provaccine, and neutral tweets contain either set more frequently than the others. To achieve these goals, we collected a large set of social media posts (over 120 million tweets) between Nov. 15 and Dec. 15, 2021, coinciding with the Omicron variant surge. A two-stage framework was developed using a fine-tuned BERT classifier, demonstrating over 99 and 80 percent accuracy for binary and ternary classification. Finally, the Linguistic Inquiry Word Count text analysis tool was used to count linguistic features in each classified tweet. Our regression results show that anti-vaccine tweets are propagated (i.e., retweeted), while pro-vaccine tweets garner passive endorsements (i.e., favorited). Our results also yielded the two sets of linguistic features as facilitators and inhibitors of the propagation of vaccine-related tweets. Finally, our regression results show that anti-vaccine tweets tend to use the facilitators, while pro-vaccine counterparts employ the inhibitors. These findings and algorithms from this study will aid public health officials' efforts to counteract vaccine misinformation, thereby facilitating the delivery of preventive measures during pandemics and epidemics.

‘Against the cult of veganism’: Unpacking the social psychology and ideology of anti-vegans

Despite the established health and ecological benefits of a plant-based diet, the decision to eschew meat and other animal-derived food products remains controversial. So polarising is this topic that anti-vegan communities — groups of individuals who stand vehemently against veganism — have sprung up across the internet. Much scholarship on veganism characterizes anti-vegans in passing, painting them as ill-informed, uneducated, or simply obstinate. However, little empirical work has investigated these communities and the individuals within them. Accordingly, we conducted a study using social media data from the popular platform, Reddit. Specifically, we collected all available submissions (∼3523) and comments (∼45,528) from r/AntiVegan subreddit users (N = 3819) over a five-year period. Using a battery of computerized text analytic tools, we examined the psychosocial characteristics of Reddit users who publicly identify as anti-vegan, how r/AntiVegan users discuss their beliefs, and how the individual user changes as a function of community membership. Results from our analyses suggest several individual differences that align r/AntiVegan users with the community, including dark entertainment, ex-veganism and science denial. Several topics were extensively discussed by r/AntiVegan members, including nuanced discourse on the ethicality and health implications of vegan diets, and the naturalness of animal death, which ran counter to our expectations and lay stereotypes of r/AntiVegan users. Finally, several longitudinal changes in language use were observed within the community, reflecting enhanced group commitment over time, including an increase in group-focused language and a decrease in cognitive processing. Implications for vegan-nonvegan relations are discussed.

Enhancing Legal Argument Mining with Domain Pre-training and Neural Networks

The contextual word embedding model, BERT, has proved its ability on downstream tasks with limited quantities of annotated data. BERT and its variants help to reduce the burden of complex annotation work in many interdisciplinary research areas, for example, legal argument mining in digital humanities. Argument mining aims to develop text analysis tools that can automatically retrieve arguments and identify relationships between argumentation clauses. Since argumentation is one of the key aspects of case law, argument mining tools for legal texts are applicable to both academic and non-academic legal research. Domain-specific BERT variants (pre-trained with corpora from a particular background) have also achieved strong performance in many tasks. To our knowledge, previous machine learning studies of argument mining on judicial case law still heavily rely on statistical models. In this paper, we provide a broad study of both classic and contextual embedding models and their performance on practical case law from the European Court of Human Rights (ECHR). During our study, we also explore a number of neural networks when being combined with different embeddings. Our experiments provide a comprehensive overview of a variety of approaches to the legal argument mining task. We conclude that domain pre-trained transformer models have great potential in this area, although traditional embeddings can also achieve strong performance when combined with additional neural network layers.

Emotional Attitudes towards the Components of the Digital Environment (Based on the Text Analysis of Network Comments)

One of the psychological effects of digitalization is the establishment of specific relationships between a person and the cyber environment and its components. The paper presents the results of a study of the emotional component of attitudes towards the components of the digital environment, carried out using emotive-predicate analysis, a new method of computer text processing implemented in TITANIS, an advanced social media text analysis tool. Using this method, it is possible to automatically extract descriptions of emotional situations from texts in which the components of the digital environment are the cause or subject of 68 emotional states. The texts of 2048 online videos posted in the Russian-language segment of YouTube served as the material for the analysis. According to the frequency of occurrence, emotional situations involving various components of the digital environment are quite typical for even thematically irrelevant online discussions. The components of the digital environment mentioned in non-thematic discussions as participants in emotional situations are classified into the following three groups: (1) general concepts of digital technologies; (2) digital devices; (3) activities mediated by digital technologies. The lexemes of the latter group, denoting various aspects of network communication, are included in the vast majority of descriptions of emotional situations involving the components of the digital environment, and six times more often as causes of emotions than as subjects of emotional states. In general, the emotional attitude towards the components of the cyber environment as a whole is characterized as balanced, without a noticeable predominance of negative or positive emotions. However, negative states are more often attributed to the components of the cyber environment as the subjects than as the causes of emotions. The practical significance of the described method of text analysis as a means of assessing the emotional component of attitudes towards the components of the digital environment is determined by the influence that affective reactions of users have on the demand for technical innovations and the direction of their development.

Maximizing integrative learning in software development teams: A systematic review of key drivers and future research agenda

Software development is a complex phenomenon that requires software teams to integrate diverse knowledge and expertise to provide innovative solutions. A critical success factor for software teams is their integrative learning capability which is driven by multiple factors. We systematically review and synthesize extant literature to identify critical antecedents of integrative learning in software teams and uncover knowledge gaps to inform future research. Searching multiple databases, 447 papers were identified, of which 32 were selected for the final analysis after a rigorous data extraction process. We performed open and axial coding and affinity diagramming to thematically analyze each study for antecedents associated with integrative learning. We also triangulated our results by content analyzing the studies using a software-based text analysis tool. The findings indicate five crucial drivers of integrative learning in software teams: social harmony, process agility, team design, technological maturity, and project environment. The study also highlights the value of recognizing the inter-team and intra-team contexts in team learning. Based on the study findings, we propose a model of integrative learning in software teams, discuss future research agendas, and offer practical insights and recommendations to software professionals.

Automating Large-scale Health Care Service Feedback Analysis: Sentiment Analysis and Topic Modeling Study.

BackgroundObtaining patient feedback is an essential mechanism for health care service providers to assess their quality and effectiveness. Unlike assessments of clinical outcomes, feedback from patients offers insights into their lived experiences. The Department of Health and Social Care in England via National Health Service Digital operates a patient feedback web service through which patients can leave feedback of their experiences in structured and free-text report forms. Free-text feedback, compared with structured questionnaires, may be less biased by the feedback collector and, thus, more representative; however, it is harder to analyze in large quantities and challenging to derive meaningful, quantitative outcomes.ObjectiveThe aim of this study is to build a novel data analysis and interactive visualization pipeline accessible through an interactive web application to facilitate the interrogation of and provide unique insights into National Health Service patient feedback.MethodsThis study details the development of a text analysis tool that uses contemporary natural language processing and machine learning models to analyze free-text clinical service reviews to develop a robust classification model and interactive visualization web application. The methodology is based on the design science research paradigm and was conducted in three iterations: a sentiment analysis of the patient feedback corpus in the first iteration, topic modeling (unigram and bigram)–based analysis for topic identification in the second iteration, and nested topic modeling in the third iteration that combines sentiment analysis and topic modeling methods. An interactive data visualization web application for use by the general public was then created, presenting the data on a geographic representation of the country, making it easily accessible.ResultsOf the 11,103 possible clinical services that could be reviewed across England, 2030 (18.28%) different services received a combined total of 51,845 reviews between October 1, 2017, and September 30, 2019. Dominant topics were identified for the entire corpus followed by negative- and positive-sentiment topics in turn. Reviews containing high- and low-sentiment topics occurred more frequently than reviews containing less polarized topics. Time-series analysis identified trends in topic and sentiment occurrence frequency across the study period.ConclusionsUsing contemporary natural language processing techniques, unstructured text data were effectively characterized for further analysis and visualization. An efficient pipeline was successfully combined with a web application, making automated analysis and dissemination of large volumes of information accessible. This study represents a significant step in efforts to generate and visualize useful, actionable, and unique information from free-text patient reviews.

Stigmatizing Attitudes Across Cybersuicides and Offline Suicides: Content Analysis of Sina Weibo

BackgroundThe new reality of cybersuicide raises challenges to ideologies about the traditional form of suicide that does not involve the internet (offline suicide), which may lead to changes in audience’s attitudes. However, knowledge on whether stigmatizing attitudes differ between cybersuicides and offline suicides remains limited.ObjectiveThis study aims to consider livestreamed suicide as a typical representative of cybersuicide and use social media data (Sina Weibo) to investigate the differences in stigmatizing attitudes across cybersuicides and offline suicides in terms of attitude types and linguistic characteristics.MethodsA total of 4393 cybersuicide-related and 2843 offline suicide-related Weibo posts were collected and analyzed. First, human coders were recruited and trained to perform a content analysis on the collected posts to determine whether each of them reflected stigma. Second, a text analysis tool was used to automatically extract a number of psycholinguistic features from each post. Subsequently, based on the selected features, a series of classification models were constructed for different purposes: differentiating the general stigma of cybersuicide from that of offline suicide and differentiating the negative stereotypes of cybersuicide from that of offline suicide.ResultsIn terms of attitude types, cybersuicide was observed to carry more stigma than offline suicide (χ21=179.8; P<.001). Between cybersuicides and offline suicides, there were significant differences in the proportion of posts associated with five different negative stereotypes, including stupid and shallow (χ21=28.9; P<.001), false representation (χ21=144.4; P<.001), weak and pathetic (χ21=20.4; P<.001), glorified and normalized (χ21=177.6; P<.001), and immoral (χ21=11.8; P=.001). Similar results were also found for different genders and regions. In terms of linguistic characteristics, the F-measure values of the classification models ranged from 0.81 to 0.85.ConclusionsThe way people perceive cybersuicide differs from how they perceive offline suicide. The results of this study have implications for reducing the stigma against suicide.