Textual Content Research Articles

Facilitating phenotyping from clinical texts: the medkit library.

Phenotyping consists in applying algorithms to identify individuals associated with a specific, potentially complex, trait or condition, typically out of a collection of Electronic Health Records (EHRs). Because a lot of the clinical information of EHRs are lying in texts, phenotyping from text takes an important role in studies that rely on the secondary use of EHRs. However, the heterogeneity and highly specialized aspect of both the content and form of clinical texts makes this task particularly tedious, and is the source of time and cost constraints in observational studies. To facilitate the development, evaluation and reproducibility of phenotyping pipelines, we developed an open-source Python library named medkit. It enables composing data processing pipelines made of easy-to-reuse software bricks, named medkit operations. In addition to the core of the library, we share the operations and pipelines we already developed and invite the phenotyping community for their reuse and enrichment. medkit is available at https://github.com/medkit-lib/medkit. Documentation, examples and tutorials are available at https://medkit-lib.org/.

Reading comics: The effect of expertise on eye movements

The theory of expertise suggests that there should be observable differences in the eye movement patterns between experts and non-experts. Previous studies have investigated how expertise influences eye movement patterns during cognitive tasks like reading. However, the impact of expertise on eye movements in comics, a multimodal form of text, remains unexplored. This article reports on a study that uses eye tracking to examine patterns in the ways that experts and non-experts read comics. Expert participants (14) with experience in reading comics than non-expert participants (17). When controlling for variables such as layout and text quantity, we found significant differences in visual scanning between experts and non-experts. Experts exhibited more frequent saccades and greater amplitude of saccades. Further analysis revealed distinct strategies in processing text and image content between the two groups: the interaction between expertise level and content type in specific AOI showed significant differences across multiple visual measurement metrics, including Average duration of fixations, number of fixations, and number of saccades within AOI. These findings not only support the applicability of the expertise level theory in the field of comic reading but also provide a new perspective for understanding the reading processing of multimodal texts.

FAQ-Gen: An automated system to generate domain-specific FAQs to aid content comprehension

Frequently Asked Questions (FAQs) refer to the most common inquiries about specific content. They serve as content comprehension aids by simplifying topics and enhancing understanding through succinct presentation of information. In this paper, we address FAQ generation as a well-defined Natural Language Processing task through the development of an end-to-end system leveraging text-to-text transformation models. We present a literature review covering traditional question-answering systems, highlighting their limitations when applied directly to the FAQ generation task. We propose a system capable of building FAQs from textual content tailored to specific domains, enhancing their accuracy and relevance. We utilise self-curated algorithms to obtain an optimal representation of information to be provided as input and also to rank the question-answer pairs to maximise human comprehension. Qualitative human evaluation showcases the generated FAQs as well-constructed and readable while also utilising domain-specific constructs to highlight domain-based nuances and jargon in the original content.

Application of Content Analysis in Research of Political Discourse

Content analysis is one of the most frequently applied methods for the research of various forms of discourse, including political discourse. As a research technique, it was developed in studies on communication and is now frequently used in linguistics, for example, in text analysis, systematic studies of written text or transcribed speech, as well as in research on nontextual message content. The following article deals with the application of content analysis in research into political discourse and discusses the crucial concepts of the research method. Specifically, it describes the manifest and latent content of political texts and presents two notions particularly useful in the evaluation of content analysis, i.e., ‘reliability’ and ‘validity.’ Additionally, two broad approaches to content analysis are discussed. The first is qualitative content analysis, and the second is quantitative content analysis.

Multimodal Sentiment Analysis in Natural Disaster Data on Social Media

INTRODUCTION: With the development of the Internet, users tend to express their opinions and emotions through text, visual and/or audio content. This has increased the interest in multimodal analysis methods. OBJECTIVES: This study addresses multimodal sentiment analysis on tweets related to natural disasters by combining textual and visual embeddings.METHODS: The use of textual representations together with the emotional expressions of the visual content provides a more comprehensive analysis. To investigate the impact of high-level visual and texual features, a three-layer neural network is used in the study, where the first two layers collect features from different modalities and the third layer is used to analyze sentiments. RESULTS: According to experimental tests on our dataset, the highest performance values (77% Accuracy, 71% F1-score) are achieved by using the CLIP model in the image and the RoBERTa model in the text. CONCLUSION: Such analyzes can be used in different application areas such as agencies, advertising, social/digital media content producers, humanitarian aid organizations and can provide important information in terms of social awareness.

Aesthetic Literacies in Digital Learning Ecologies: Tracing Aesthetic Tools and Functions in a Youth Literary Salon

Concerned with disinformation, fake news, and a posttruth era, literacy research on digital learning ecologies has focused on content of the texts that youth encounter but less explicitly on aesthetics—issues of form or the way a text is written. Drawing on critical sociocultural theories, this article examines youth aesthetic meaning-making with non-neutral digital texts. The data analyzed are part of a multiyear multimethod study of the aesthetic literacy practices of queer youth of color and allies. Data were collected during an online summer literary salon where youth discussed textual content alongside issues of aesthetic forms. Findings illustrate that youth drew on a broad range of aesthetic tools to achieve (1) a poetic function concerned with how aesthetic forms elicit figurative, connotative, and storied meanings; (2) a catalytic function concerned with how aesthetic forms persuade, manipulate, or encourage particular actions; and (3) an ideological function concerned with how aesthetic forms inscribe or contest social inequalities. These tools and functions, complicate previous understandings of disinformation, fake news, and critical reasoning in literacies research. This study suggests a critical sociocultural approach to aesthetics as territory for further scholarship across many areas of literacy research.

Pragmatic particles as distinguishing features of registers

Abstract The study explores the potential of utilizing particle use data to differentiate between seven Estonian registers: everyday conversation, institutional interaction, printed media (newspapers), prose fiction, academic prose, instant messaging, and internet comments. The objective is to develop a simple yet effective model that enables researchers to comprehend the internal logic behind register differentiation based on particle use. Particles are considered promising differentiators due to their independence from text content. The article outlines the chosen method, the model creation, and the testing process. A key finding reveals that hierarchical relationships between particles within registers prove more reliable indicators than general use frequencies. The method involves establishing correspondences between particle pairs and register pairs, facilitating the measurement of distances between registers. During testing, the model demonstrates high accuracy across registers, encountering some difficulties in categorizing fiction and institutional interaction. Overall, the study confirms the efficacy of the proposed method in distinguishing registers based on particle use, underscoring the significance of particles in linguistic analysis.

Borders and Bridges in Gender Discourse: A Comparative Study of Feminist Stand-up Comedy in China and the United States

This paper selects the textual content of Yang Li’s and Iliza’s stand-up comedy for a comparative analysis, aiming to foster a dialogue between Chinese and American feminist stand-up comedy. This research findings uncover numerous similarities as well as several noteworthy differences. Additionally, to further explore the female discourse in Chinese and American feminist stand-up comedy, this paper employed quantitative analysis to assess the audience sentiment in the comment sections of the two comedians’ videos, aiming to analyze the discourse power and expression rights of these two feminist comedians. The findings offer insight into the potential and difficulties faced by female comedians tackling gender problems by highlighting the cultural quirks and social attitudes toward feminist humor in both nations. In addition, the study looks at how these comedians’ performances affect the public conversation about feminism and gender equality, offering insights into how feminist comedy is changing in China and the US.

Analysis and Recommendations on Empowering Non-Art Background College Students to Generate Visual and Textual Content Using AIGC Tools

This study selects students from Shenzhen Polytechnic University with non-art backgrounds as the sample group. By integrating questionnaire surveys with specific case studies, it explores how AIGC tools empower these students to generate both visual and textual content and design related works. However, their usage primarily concentrates on basic functions such as generating texts and images. It is therefore recommended that systematic courses be developed to guide non-art background students in using AIGC tools more effectively and comprehensively, enabling them to perform more complex tasks.

Hearing Rhythm, Seeing Rhythm: A Research Approach to Reimagining Traditions of Chinese Poetic ‘rules of rhythm’

Throughout the history of China, from early dynasties into contemporary times, aspects of poetry, painting, and writing have been grounded in varied but deeply connected ‘rules of rhythm’. These ‘rules’ permeate in diverse yet identifiable and recognizable forms. The research in this project is aimed at both adopting and adapting these rules of rhythm or rhythmic patterns in an experimental form that reimagines a tradition of ‘lyric aesthetics’ in the context of the contemporary interplay between sound, image, and writing. In this sense, the investigation is at once one of ‘reclamation’ and extension. The basic ‘question’ in these experiments is whether it is possible to represent sonic patterns themselves — that is, by both visualizing sound structures and ‘sonifying’ visual forms (irrespective of textual content). The text focuses on the historical establishment of methodological approaches (as a way to identify their forms) and then transforming/translating these in combinations made possible by blending the sonic and visual. As a particular attribute of Chinese expression, these ‘rules’ or forms are cultural traits that have endured through many centuries and challenges and, more importantly in this case, can be (re)iterated in dynamic, fluid, and revitalized contemporary artistic configurations that aim at seeing sound, writing sound, and performing rhythm. <style> /* rules to make button only show up in META */ .download-accessible { display:none; } .meta-right-col .download-accessible { display: inline-block; padding: 9px; margin-bottom:25px; border: 1px solid black; background-color:white; } </style> <a class="download-accessible" href="download-media?work=3155507&file=3155517" title="This accessible PDF is a derivative of the original which it is meant to support and not replace.">Download Accessible PDF</a>

The union of linguistics and prompt engineering: linguistic features of prompts to neural networks

This article offers a linguistic perspective on the study of the interaction between humans and Artificial Intelligence. The focus of the work is on analyzing human communication with neural networks which generate textual content. With the advent of text-based interfaces for neural networks, where programming skills are no longer required for human-AI communication, and it is sufficient to create text queries (prompts), one of the key skills for prompt engineers (specialists in prompt creation) has become the ability to ‘program’ and obtain AI responses corresponding to the query while using natural language resources. This work is aimed at studying, identifying, and describing the linguistic features of prompts and their parameters that influence the stylistic correctness of the generated responses by the neural networks.

Determinants of receiving government support by small and medium-sized enterprises in Russia

Government support measures help companies overcome various restrictions impeding their growth and develop ment, which is of special relevance for small and medium-sized enterprises (SMEs). These measures, therefore, must reach the target companies and ensure efficient allocation of resources. The article reveals key characteristics of SMEs in Russia that increase the probability of them receiving government support. The research methodology is based on public interest theory, public choice theory and the concept of economic freedom. The research method is a two-step regression analysis, in which the binary choice model is first evaluated, and then the LASSO model is used to assess the impact of the textual content of the companies’ websites. The dataset of the study is represented by data on 2,176 Russian metallurgical enterprises for 2020–2023, of which 57% were receiving government support. The results of the first step of evaluation showed that the organization’s age and size meas ured by revenue were the key factors associated with the probability of receiving government support. At the second step, we found that the mention of certain goods or materials on the websites, the enterprise’s location and orientation towards foreign stakeholders were significantly related to the likelihood of getting state support. The study offers tools for analyzing the determi nants of government support for SMEs and highlights the importance of this analysis for more efficient allocation of government resources.

Systemic Functional Linguistic Based on Semantic Web

This research aims to combine a functional and systematic linguistic approach with Semantic Web technology. The main aim of this research is to develop a framework that can facilitate systematized analysis and language modeling in the context of the semantic web. The research method used is a qualitative approach with focused data analysis. The data collected will involve text and web content relevant to the Semantic Web framework. The analysis was carried out using the principles and tools of systematic functional linguistic analysis to identify language structure, communicative function, and meaning in web content. The results of this research are expected to provide new insights into the understanding of how language can be organized and analyzed regularly in the context of the semantic web. Apart from that, this research is also expected to contribute to the development of Semantic Web technology by using a functional linguistic approach. It is hoped that the conclusions of this research will provide a deeper understanding of the role of language in the context of the semantic web and provide a basis for the development of more sophisticated and effective Semantic Web-based applications and systems.

Enhancing domain-specific text generation for power grid maintenance with P2FT.

The digitization of operation and maintenance in the intelligent power grid equipment relies on a diverse array of information for smart decision-making. In the domain of intelligent decision generation, proficiency is contingent upon extensive learning from copious amounts of text. This necessitates not only robust processing capabilities but also a high level of specialization. In addressing situations where authorization is lacking, pre-trained language models (PLMs) have already provided ideas when confronted with specialized domains or tasks. In consideration of the complexity of textual content in the field of the power grid, which encompasses a multitude of specialized knowledge and involves an abundance of proprietary terminology, we have undertaken an exploration of pre-trained model specialization using the power grid domain as an example, specifically for the task of generating maintenance strategies. A two-stage fine-tuning approach (P2FT) is employed, utilizing a large-scale pre-training model specifically designed for natural language processing. The efficacy and practical value of this method were evaluated through multiple metrics, juxtaposed with other advanced approaches involving low-parameter or parameter-free fine-tuning methods. Through a meticulous analysis and validation of experimental outcomes, we have corroborated the feasibility and practical application value of employing this approach for pre-trained model specialization. Additionally, it has furnished valuable guidance for text generation within both the Chinese language domain and the power grid domain.

Text Command Intelligent Understanding for Cybersecurity Testing

Research on named entity recognition (NER) and command-line generation for network security evaluation tools is relatively scarce, and no mature models for recognition or generation have been developed thus far. Therefore, in this study, the aim is to build a specialized corpus for network security evaluation tools by combining knowledge graphs and information entropy for automatic entity annotation. Additionally, a novel NER approach based on the KG-BERT-BiLSTM-CRF model is proposed. Compared to the traditional BERT-BiLSTM model, the KG-BERT-BiLSTM-CRF model demonstrates superior performance when applied to the specialized corpus of network security evaluation tools. The graph attention network (GAT) component effectively extracts relevant sequential content from datasets in the network security evaluation domain. The fusion layer then concatenates the feature sequences from the GAT and BiLSTM layers, enhancing the training process. Upon successful NER execution, in this study, the identified entities are mapped to pre-established command-line data for network security evaluation tools, achieving automatic conversion from textual content to evaluation commands. This process not only improves the efficiency and accuracy of command generation but also provides practical value for the development and optimization of network security evaluation tools. This approach enables the more precise automatic generation of evaluation commands tailored to specific security threats, thereby enhancing the timeliness and effectiveness of cybersecurity defenses.

How dark traits and regulatory focus manifest in online hotel reviews: a textual content analysis

ABSTRACT This study seeks to explore how Dark Triad of psychopathy, Machiavellianism, and narcissism interact with consumers’ regulatory focus (prevention-focus and promotion-focus) to influence online consumer reviews in the hotel industry. We conducted an empirical analysis of 1,185,940 hotel reviews from TripAdvisor.com, utilizing the textual content of online reviews and employing Language Inquiry and Word Count analytical tools. The research reveals a multifaceted link between regulatory focus and Dark Triad in online reviews. The results showed that individuals with a promotion focus tend to give higher ratings than those with a prevention focus. Dark Triad significantly influences how regulatory focus impacts online reviews. This study initiates a new academic dialogue on the interaction between regulatory focus and Dark Triad traits within online consumer behavior studies. It provides practical guidance for the hotel industry to refine their management and response strategies by acknowledging the impact of consumer personality traits on online reviews.

Kárpátalja egyházi fenntartású iskoláinak virtuális nyelvi tájképe

We live in an age where everywhere we look, thousands of pieces of information are streaming towards us. This fact in itself highlights the importance of the linguistic landscape in our everyday lives, as everything that surrounds us, everything we perceive influences our thinking and therefore our use of language. In the 21st century, we need to pay particular attention to the virtual world, as a large percentage of the younger and even older generations receive a significant proportion of their stimuli through some types of electronic communication tools or social platforms. Schools have a particularly important role in language development and formation, as young people, the future generation, are most shaped within their walls. In the present study, we analysed a sample of posts published in the last few years on the social platforms (official website and Facebook) of the ecclesiastical schools of Transcarpathia. We relied on statistical and comparative analysis of the textual content of these entries. In addition, we examined the meta-linguistic information of the entries. The overall visual appearance of the website was also taken into consideration, i.e., the types of images published were also investigated. In the research, it was assumed that all the official webpages of the schools under investigation contain entries in both Ukrainian and Hungarian. We also assessed whether a higher percentage of entries in Hungarian appeared on the schools’ online interfaces. Results showed that the number of interactions by people who followed the sites was higher for Hungarian language posts, advertisements and articles. We assumed that religious posts and symbols regularly appeared on the websites and Facebook pages, and that a higher percentage of these posts and symbols appeared in Hungarian. It was also hypothesized that the volume of texts in Ukrainian was smaller than the amount of texts in Hungarian. The research has confirmed that both Ukrainian and Hungarian entries are found on the websites, with a higher share of entries in Hungarian. It also confirmed our hypothesis that followers of the websites tend to respond more frequently to posts in Hungarian, due to the fact that the majority of followers use Hungarian as their mother tongue. Last but not least, the research revealed that religious posts and symbols regularly appear on the official websites and Facebook pages of the lyceums, and the language of these posts is mostly Hungarian.

Beyond Text: Multimodal Credibility Assessment Approaches for Online User-Generated Content

User-generated content (UGC) is increasingly becoming prevalent on various digital platforms. The content generated on social media, review forums, and question–answer platforms impacts a larger audience and influences their political, social, and other cognitive abilities. Traditional credibility assessment mechanisms involve assessing the credibility of the source and the text. However, with the increase in how user content can be generated and shared (audio, video, and images), multimodal representation of UGC has become increasingly popular. This article reviews the credibility assessment of UGC in various domains, particularly identifying fake news, suspicious profiles, and fake reviews and testimonials, focusing on both textual content and the source of the content creator. Next, the concept of multimodal credibility assessment is presented, which also includes audio, video, and images in addition to text. After that, the article presents a systematic review and comprehensive analysis of work done in the credibility assessment of UGC considering multimodal features. Additionally, the article provides extensive details on the publicly available multimodal datasets for the credibility assessment of UGC. In the end, the research gaps, challenges, and future directions in assessing the credibility of multimodal UGC are presented.

Kiswahili Translation in Digital Media Platforms: Bridging Language Gaps in the AI Age

Purpose: As digital media platforms continue to proliferate globally, the need for multilingual content in these platforms has become increasingly apparent. Translation of online content is a necessity for netizens and other digital platform users who seek knowledge and information around a myriad of user-generated content on diverse issues and topics. Being a widely spoken language that goes beyond the borders of East Africa, Kiswahili’s significance in bridging linguistic barriers and facilitating communication and information access for Kiswahili speakers is clear. The purpose of this exploratory research paper was to examine the state of Kiswahili as a language of translation through different technologies across varied translation apps and social media platforms such as Facebook, You Tube, Tik Tok and Netflix. Methodology: The study adopted a descriptive research design. Data collection was done through online data mining tools including API access for web scrapping and manual extraction of content through random search engine searches. The data for the study included 10 sentences, 10 videos and 25 specific word forms such as proverbs, spiritual/religious words, similes and cultural words for food, beauty and family ties. Content analysis for both text and multimedia content such as written sentences, social media posts and videos was done. The study analyzed translation outputs of 14 Kiswahili translation apps in order to establish the status and accuracy of these translations. Of key consideration was the role of AI technology in the translation apps and the effect it has on the translated texts across different apps. The challenges and emerging issues facing Kiswahili translation through digital technologies were also explored. Findings: The findings of this study showed that the Apps used for Kiswahili translation have demonstrated great translation accuracy in basic semantic forms, with an average score of 60% accuracy upon random searches and between 10-30% accuracy for cultural forms and idiomatic expressions. Unique Contribution to Theory, Practice and Policy: The study was guided by the Skopos theory by Hans Vermeer (1978) which was complemented by the Relevance Theory by Dan Sperber and Deidre Wilson (1986). These Apps seem to have the feature of interoperability in that they seem to produce the same results suggesting that the AI supported feature in the apps may be interconnecting the apps during translation. The study advocates for more AI integration translation apps as this will help standardize Kiswahili translation corpus.

Improved bidirectional attention flow (BIDAF) model for Arabic machine reading comprehension

Abstract Machine reading comprehension (MRC) refers to the process of instructing machines to comprehend and respond to inquiries based on a provided text. There are two primary methodologies for achieving this: extracting answers directly from the text or predicting them. Extracting answers involves anticipating the specific segment of text containing the answer, pinpointed by its starting and ending indices within the paragraph. Despite the increasing interest in MRC, exploration within the framework of the Arabic language faces limitations due to various challenges. A significant impediment arises from the inadequacy of resources available for Arabic textual content, which impedes the development of effective models. Furthermore, the inherent intricacies of Arabic, manifesting in its diverse linguistic forms including classical, modern standard, and colloquial, present distinctive hurdles for tasks involving language comprehension. This paper proposes an enhanced version of the bidirectional attention flow (BIDAF) model for Arabic MRC, constructed upon the Arabic Span-Extraction-based Reading Comprehension Benchmark (ASER). ASER comprises 10,000 sets of questions, answers, and passages, partitioned into a training set constituting 90% of the data and a testing set making up the remaining 10%. By introducing a new input feature based on parts-of-speech (POS) word embeddings and replacing Bidirectional Long Short-Term Memory (bi-LSTM) with bidirectional gated recurrent unit, significant improvements were observed. Eight different POS word embeddings were generated using both Continuous Bag of Words (CBOW) and Skip-gram methods, with varying dimensionalities. Evaluation metrics, including exact match (EM) and F1-measure, were utilized to assess model performance, with emphasis on the latter for its accuracy. The proposed enhanced BIDAF model achieved a remarkable accuracy of 75.22% on the ASER dataset, demonstrating its efficacy in Arabic MRC tasks. Additionally, rigorous statistical evaluation using a two-tailed paired samples t-test further validated the findings, highlighting the significance of the proposed enhancements in advancing Arabic language processing capabilities.