Text Identification Research Articles

Identifying artificial intelligence–generated content in online Q&A communities through interpretable machine learning

This study aims to construct a comprehensive feature system for identifying artificial intelligence–generated content (AIGC) in online Q&A communities, thus uncovering the key factors and mechanisms influencing the identification of AIGC. First, based on the theory of systemic functional linguistics (SFL) and information quality (IQ), this article extracts vocabulary, content, structure, and emotional features from the text, and identifies the AIGC through nine mainstream machine learning algorithms. Subsequently, three widely used resampling strategies are exploited to address the category imbalance problem. The grid search optimisation algorithm fine-tunes different combinations of parameters to improve the performance of the identification classifier. Finally, SHAP values are introduced to evaluate and elucidate the global feature importance and feature influence mechanism. A Chinese corpus from the Zhihu Q&A community is constructed to verify the validity of these methods. The experimental results show that the eXtreme Gradient Boosting (XGBoost) model optimised with hybrid sampling and grid search parameters exhibits excellent performance in identifying AI-generated text, which achieves an F1-score of 0.9935, an improvement of 0.11 percentage points over the original model. In addition, all four dimensions of features constructed in this article contribute to AI-generated text identification, and the results of feature interpretability analysis show the greatest impact of features that focus on content readability. The study facilitates the identification and labelling of AIGC in online Q&A communities, thereby enhancing transparency and accountability of information shared online.

Artificial Intelligence and Linguistic Landscape research

Abstract This article explores applications of artificial intelligence (AI) technologies in Linguistic Landscape research. Traditionally, LL research has relied on manual data collection and analysis, often involving photographs of public signage, advertisements, and other visual language displays. However, this manual approach can present challenges, including time-consuming data collection, inconsistent data quality, and potential researcher bias. Two AI technologies in particular hold promise for addressing these challenges in LL research: computer vision (CV) and large language models (LLMs). CV automates the identification and extraction of text from images, improving data accuracy and enabling large-scale image analysis. LLMs, based on natural language processing, can detect, translate, and interpret multilingual text. This article explores the affordances and challenges of using AI technologies in LL research and discusses methods to improve data collection, enhance accuracy, and support the analysis of multilingual environments. It also raises ethical issues and limitations of the technologies.

Investigating the Linguistic Fingerprint of GPT-4o in Arabic-to-English Translation Using Stylometry

This study explores the linguistic and stylistic characteristics of machine-generated texts, focusing on the output of GPT-4o. Using various natural language processing (NLP) techniques, including word frequency and stopword count analysis, readability and sentence structure metrics, lexical diversity measures, syntactic frequency analysis, and named entity recognition (NER), the research aims to uncover the stylometric fingerprints present in machine-generated content. The results reveal that GPT-4ogenerated texts exhibit moderate lexical diversity and syntactic complexity, with certain chapters reflecting higher readability and more varied sentence structures, while others lean toward simpler linguistic patterns. The findings also highlight thematic variation across chapters, as observed in the distribution of named entities, which contributes to understanding the model’s handling of different contextual content. The research suggests that while GPT-4o maintains a consistent style in its generated text, there are distinguishable characteristics that may serve as indicators of machine authorship. This provides valuable insights for stylometric analysis, authorship attribution, and the identification of machine-generated texts in various contexts. Future research could extend this work by exploring deeper stylometric features, conducting cross-model comparisons, and developing advanced authorship detection algorithms tailored for AI-generated content. Moreover, the ethical implications of stylometric analysis in the context of AI-generated texts warrant further investigation, particularly as machine-generated content becomes increasingly prevalent across different domains.

Evaluating GPT and BERT models for protein-protein interaction identification in biomedical text.

Detecting protein-protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. As biomedical literature continues to grow rapidly, there is an increasing need for automated and accurate extraction of these interactions to facilitate scientific discovery. Pretrained language models, such as generative pretrained transformers and bidirectional encoder representations from transformers, have shown promising results in natural language processing tasks. We evaluated the performance of PPI identification using multiple transformer-based models across three manually curated gold-standard corpora: Learning Language in Logic with 164 interactions in 77 sentences, Human Protein Reference Database with 163 interactions in 145 sentences, and Interaction Extraction Performance Assessment with 335 interactions in 486 sentences. Models based on bidirectional encoder representations achieved the best overall performance, with BioBERT achieving the highest recall of 91.95% and F1 score of 86.84% on the Learning Language in Logic dataset. Despite not being explicitly trained for biomedical texts, GPT-4 showed commendable performance, comparable to the bidirectional encoder models. Specifically, GPT-4 achieved the highest precision of 88.37%, a recall of 85.14%, and an F1 score of 86.49% on the same dataset. These results suggest that GPT-4 can effectively detect protein interactions from text, offering valuable applications in mining biomedical literature. The source code and datasets used in this study are available at https://github.com/hurlab/PPI-GPT-BERT.

ChatGPT in Undergraduate Education: Performance of GPT-3.5 and Identification of AI-Generated Text in Introductory Neuroscience

ChatGPT in Undergraduate Education: Performance of GPT-3.5 and Identification of AI-Generated Text in Introductory Neuroscience

Tahlilan dan Tawasul (Perspektif KH. Misbah Al-Musthafa dalam Tafsir Al-Iklil fi Ma'ani Al-Tanzil)

Abstract: This research aims to examine views of KH. Misbah Al-Musthafa, a renowned scholar in Java who discusses the practices of tahlilan and tawassul in his Quranic tafsir entitled Al-Iklil fī Ma'ani Al-Tanzil. The study employs a qualitative method with a literature review, with the primary source being the Quranic tafsir entitled Al-Iklil fī Ma'ani Al-Tanzil by KH. Misbah Al-Mushtafa. Data was collected through the identification and analysis of texts related to tahlilan and tawassul in the aforementioned book. Additionally, other relevant references such as books, journals, and articles were used to support the analysis. In the interpretation of Al-Iklil fī Ma'ani Al-Tanzil, KH. Misbah Al-Musthafa provides a deep understanding of these two practices. He attempts to analyze the Quranic and hadith evidence related to tahlilan and tawassul, and provides a comprehensive interpretation based on the understanding of the Quran and Sunnah. The conclusion of this research is that KH. Misbah Al-Musthafa views tahlilan and tawassul as religious practices that have legitimacy in the Javanese Islamic tradition, but they must be based on a correct theological understanding and carried out in accordance with the teachings of the religion.

Language awareness: On the semiotics of talk and text

The article argues for a necessity to increase human awareness of language as functional biological behavior rather than simply a tool in the service of communication, by emphasizing the difference between talk and text as ontologically different semiotic phenomena characteristic of the human cognitive domain. The established tradition to view written words as linguistic signs leads the studies of natural language astray, effectively hiding its nature as biologically functional orientational behavior in a consensual domain that evolved with the evolution of our species and was not a cultural invention. Because of the identification of text with talk in linguistic semiotics, the empirical validity of the core semiotic concept of natural linguistic sign, based on the so-called semiotic triangle, is undermined. While talk is a dynamic fact of nature, text is a static artifact; it is argued, therefore, that the analytical approach to linguistic signs as objects in the world is inadequate, and the notions of first- and second-order semiotics are introduced. It is concluded that awareness of the cognitive-semiotic difference between talk and text and their respective roles in the evolution of humans may facilitate further research into the nature and origin of humanness.

Unveiling AI-Generated Financial Text: A Computational Approach Using Natural Language Processing and Generative Artificial Intelligence

This study is an in-depth exploration of the nascent field of Natural Language Processing (NLP) and generative Artificial Intelligence (AI), and it concentrates on the vital task of distinguishing between human-generated text and content that has been produced by AI models. Particularly, this research pioneers the identification of financial text derived from AI models such as ChatGPT and paraphrasing tools like QuillBot. While our primary focus is on financial content, we have also pinpointed texts generated by paragraph rewriting tools and utilized ChatGPT for various contexts this multiclass identification was missing in previous studies. In this paper, we use a comprehensive feature extraction methodology that combines TF–IDF with Word2Vec, along with individual feature extraction methods. Importantly, combining a Random Forest model with Word2Vec results in impressive outcomes. Moreover, this study investigates the significance of the window size parameters in the Word2Vec approach, revealing that a window size of one produces outstanding scores across various metrics, including accuracy, precision, recall and the F1 measure, all reaching a notable value of 0.74. In addition to this, our developed model performs well in classification, attaining AUC values of 0.94 for the ‘GPT’ class; 0.77 for the ‘Quil’ class; and 0.89 for the ‘Real’ class. We also achieved an accuracy of 0.72, precision of 0.71, recall of 0.72, and F1 of 0.71 for our extended prepared dataset. This study contributes significantly to the evolving landscape of AI text identification, providing valuable insights and promising directions for future research.

SOFTWARE CAPABILITIES FOR TEXT IDENTIFICATION: COMPARISON FOR SIMILARITY, IDENTITY ESTABLISHMENT, UNIQUENESS CHECK

The study was carried out within the framework of identification linguistics and translational linguistics. The article describes some methods for determining the degree of similarity of texts: shingle algorithm, Levenshtein distance, systems for detecting plagiarism. The purpose of the work is to test the software capabilities of comparing texts for similarity, establishing their identity, and checking uniqueness. In a broad sense, these tasks fall within the area of text identification. In a qualitative (manual) assessment of the similarity of texts, identifying parameters are selected and selected specifically for the text under study. The use of electronic resources is determined by the desire for objectivity of the methods used to establish the identity of texts and the objectivity of the results obtained. Software products also make it possible to establish another, quantitative, characteristic — the degree of similarity of texts to each other or the degree of originality of the text. The work used services whose tasks include 1) comparing the similarity of two texts; 2) calculation of the Levenshtein distance; 3) detection of borrowing. The research material was an excerpt from an interview with Foreign Minister Sergei Lavrov. Reverse machine translation texts served as options for comparison with the source text. Reverse machine translation as a translation product is part of artificial intelligence and a model of the process of understanding and interpreting natural language. The results of using the proposed services made it possible to arrange five reverse machine translation options from the most unique text to the text that is most identical to the invariant. The study showed that the programs generally produce similar results, which can be applicable to solving research and applied problems related to establishing the identity and difference of texts. The prospect of the study is to identify lexical parameters that make it possible to classify reverse machine translation texts as the most or least identical with respect to the invariant.

An Empirical Analysis of Language Detection in Dravidian Languages

Objectives: Language detection is the process of identifying a language associated with a text. The proposed system aims to detect the Dravidian language that is associated with the given text using different machine learning and deep learning algorithms. The paper presents an empirical analysis of the results obtained using the different models. It also aims to evaluate the performance of a language agnostic model for the purpose of language detection. Method: An empirical analysis of Dravidian language identification in social media text using machine learning and deep learning approaches with k-fold cross validation has been implemented. The identification of Dravidian languages, including Tamil, Malayalam, Tamil Code Mix, and Malayalam Code Mix, is performed using both machine learning (ML) and deep learning algorithms. The machine learning algorithms used for language detection are Naive Bayes (NB), Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). The supervised Deep Learning (DL) models used include BERT, mBERT and language agnostic models. Findings: The language agnostic model outperform all other models considering the task of language detection in Dravidian languages. The results of both the ML and DL models are analyzed empirically with performance measures like accuracy, precision, recall, and f1-score. The accuracy associated with different machine learning algorithms varies from 85% to 89%. It is evident from the experimental result that the deep learning model outperformed with an accuracy of 98%. Novelty: The proposed system emphasizes on the use of the language agnostic model to implement the process of detecting Dravidian languages associated with the given text which provides a promising result of 98% accuracy which is higher than the existing methodologies. Keywords: Language, Machine learning, Deep learning, Transformer model, Encoder, Decoder

Tracking the Creativity of Autobiography and Biography Text Through Poster Media for Higher Education

This research aims to analyze students' creativity in writing autobiography and biography texts in Arabic using poster media in higher education. Researchers used a qualitative approach with a case study method on 35 students. Data collection techniques used interviews, documentation and observation in Arabic writing skills learning activities, while the data analysis techniques used included triangulation of data sources identified through three stages, namely data collection, data reduction, data presentation and drawing conclusions. The research results show that: 1) Student creativity in writing autobiographical and biographical texts using posters is more varied with the choice of colors, images and identification of introductory texts, experiences and life learning; 2) the sentence structure used consists of five structures, namely the structure of verbs, nouns, adverbial attributes, properties and prepositions. Research findings show that the use of poster media in learning writing skills is able to develop creativity and a variety of sentence structures. This research is limited to only two texts, so the researcher recommends further research to analyze a variety of other texts using poster media and more varied research methods

An automatic humor identification model with novel features from Berger’s typology and ensemble models

Humor identification in text is essential for natural language understanding, impacting human–computer interactions and emotion recognition. This study introduces a novel approach to automatic humor identification in text using ensemble learning techniques. The objective is to classify text into two categories: humor and not humor. This research delves into humor detection in customer reviews, focusing on specific domains, and leverages diverse text features, including novel features derived from Berger’s typology. State-of-the-art machine learning models, such as Support Vector Machine, Multilayer Perceptron, and Naïve Bayes, are employed. The study conducts an in-depth analysis of the Yelp review dataset, evaluating the performance of various ensemble learning methods. The proposed approach demonstrates exceptional results, achieving an 83.32% F-score with the Support Vector Machine classifier and an 85.68% accuracy using the stacking-based ensemble learning method with Random Forest on the Yelp Review dataset. Furthermore, this research includes an ablation study to assess the effectiveness of Berger’s novel typology-based features in humor identification. This research contributes to the advancement of humor identification in text and highlights the effectiveness of ensemble learning techniques, especially when incorporating novel features inspired by Berger’s typology.

Bloodstream infections and multidrug resistant bacteria acquisition among burns patients in Australia and New Zealand: A registry-based study

IntroductionThis study interrogates infection related data in the Burns Registry of Australia and New Zealand (BRANZ), to examine associations of multi-drug resistant organisms (MDROs) and blood stream infection (BSI). MethodsData between July 2016 and June 2021 were analysed to determine prevalence, risk factors and outcomes associated with BSIs and MDROs: Methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), carbapenem-resistant Pseudomonas spp. (CRP), and carbapenem-resistant Enterobacter (CRE). Data completeness and value for quality improvement activity were assessed. ResultsWe found a low incidence (3.4%) of the resistant organisms of interest, and no change over the study period. Fequency varied between services and increased with age and size of burn. MRSA was the commonest organism in all age groups. A positive BSI result occurred in 1.6% of patients (12.1% of cultures taken) at a median time of 10.2 days post injury. Free text identification of organisms was inconsistently documented. ConclusionsThe low rate and patterns of acquisition of MDROs of interest and BSIs is comparable with reports from countries with low incidence of massive burns. Wider adoption of a standardized laboratory reporting framework would help realise the potential of clinical quality registries to provide data which supports evidence based infection prevention initiatives.

Content-Based Text Extraction from Image using Deep Learning

Text extraction proves beneficial in various scenarios, as it allows the conversion of information stored in non-text formats, like images or documents, into machine-readable and searchable text. In contemporary times, this technique serves as a time-efficient tool across different sectors such as real estate, finance, law, food ordering and delivery, and e-commerce. Industries are increasingly adopting text extraction methods. Previously, numerous models centered around text extraction utilized OCR, CNN, and RNN. When it comes to extracting text from images using content-based approaches, CNNs play a crucial role in recognizing and locating text regions within the images. In situations where the identification and transcription of text from images are essential, RNNs prove valuable for content-based text extraction. While CNNs and RNNs independently yield accurate outcomes in content-driven text extraction from photos, the combined utilization of both methods surpasses the individual effectiveness of each. The proposed CRNN system stands out in various aspects compared to existing methodologies. It not only demonstrates heightened accuracy and efficiency but also exhibits superior performance overall. Our investigation's findings highlight that the CRNN methodology, when applied, outperforms previous approaches by recognizing text in images with a reduced latency and more precise recognition

Deep ensemble transfer learning framework for COVID-19 Arabic text identification via deep active learning and text data augmentation

Deep ensemble transfer learning framework for COVID-19 Arabic text identification via deep active learning and text data augmentation

Cyberbullying Text Identification based on Deep Learning and Transformer-based Language Models

In the contemporary digital age, social media platforms like Facebook, Twitter, and YouTube serve as vital channels for individuals to express ideas and connect with others. Despite fostering increased connectivity, these platforms have inadvertently given rise to negative behaviors, particularly cyberbullying. While extensive research has been conducted on high-resource languages such as English, there is a notable scarcity of resources for low-resource languages like Bengali, Arabic, Tamil, etc., particularly in terms of language modeling. This study addresses this gap by developing a cyberbullying text identification system called BullyFilterNeT tailored for social media texts, considering Bengali as a test case. The intelligent BullyFilterNeT system devised overcomes Out-of-Vocabulary (OOV) challenges associated with non-contextual embeddings and addresses the limitations of context-aware feature representations. To facilitate a comprehensive understanding, three non-contextual embedding models GloVe, FastText, and Word2Vec are developed for feature extraction in Bengali. These embedding models are utilized in the classification models, employing three statistical models (SVM, SGD, Libsvm), and four deep learning models (CNN, VDCNN, LSTM, GRU). Additionally, the study employs six transformer-based language models: mBERT, bELECTRA, IndicBERT, XML-RoBERTa, DistilBERT, and BanglaBERT, respectively to overcome the limitations of earlier models. Remarkably, BanglaBERT-based BullyFilterNeT achieves the highest accuracy of 88.04% in our test set, underscoring its effectiveness in cyberbullying text identification in the Bengali language.

Legal Equivalence in International Agreements Case Study: Morocco-European Union Agreement Association

This study is comparative, qualitative and exploratory in nature. It aims at exploring the types of equivalence and translation procedures used in legal translations. The representative data consists of three authentic versions of the Morocco-European Union Association Agreement. This study has started by providing a brief overview of the translation theories and schools of thought as well as the issue of equivalence in translation, important translation techniques and procedures often employed in the translation of law are presented, discussing each translation procedure and its applicability to legal translation, and then a succinct description of the Morocco-EU FTA Association. This is followed by a comparative analysis of the English, French and Arabic translations in relation to the translation procedures used by translators. Because this study is mainly descriptive, Gideon Toury‘s comparative analysis translation approach will be used. It consists of the identification of the source text, and then the mapping of target text’s segments on their counterparts in order to determine the relationships that link a target text to its source.

Identification of paraphrased text in research articles through improved embeddings and fine-tuned BERT model

Identification of paraphrased text in research articles through improved embeddings and fine-tuned BERT model

AraCovTexFinder: Leveraging the transformer-based language model for Arabic COVID-19 text identification

In light of the pandemic, the identification and processing of COVID-19-related text have emerged as critical research areas within the field of Natural Language Processing (NLP). With a growing reliance on online portals and social media for information exchange and interaction, a surge in online textual content, comprising disinformation, misinformation, fake news, and rumors has led to the phenomenon of an infodemic on the World Wide Web. Arabic, spoken by over 420 million people worldwide, stands as a significant low-resource language, lacking efficient tools or applications for the detection of COVID-19-related text. Additionally, the identification of COVID-19 text is an essential prerequisite task for detecting fake and toxic content associated with COVID-19. This gap hampers crucial COVID information retrieval and processing necessary for policymakers and health authorities. Addressing this issue, this paper introduces an intelligent Arabic COVID-19 text identification system named ‘AraCovTexFinder,’ leveraging a fine-tuned fusion-based transformer model. Recognizing the challenges posed by a scarcity of related text corpora, substantial morphological variations in the language, and a deficiency of well-tuned hyperparameters, the proposed system aims to mitigate these hurdles. To support the proposed method, two corpora are developed: an Arabic embedding corpus (AraEC) and an Arabic COVID-19 text identification corpus (AraCoV). The study evaluates the performance of six transformer-based language models (mBERT, XML-RoBERTa, mDeBERTa-V3, mDistilBERT, BERT-Arabic, and AraBERT), 12 deep learning models (combining Word2Vec, GloVe, and FastText embedding with CNN, LSTM, VDCNN, and BiLSTM), and the newly introduced model AraCovTexFinder. Through extensive evaluation, AraCovTexFinder achieves a high accuracy of 98.89 ± 0.001%, outperforming other baseline models, including transformer-based language and deep learning models. This research highlights the importance of specialized tools in low-resource languages to combat the infodemic relating to COVID-19, which can assist policymakers and health authorities in making informed decisions.

An Attempt to Gain Gender Equality and Women’s Empowerment In Social Media

Social media plays a crucial role in shaping public opinion and crafting narratives around gender issues. The active participation of the community on various social media platforms enriches discussions about gender with diverse perspectives and experiences. Through campaigns, hashtags, and visual content, social media not only provides a space for narratives but also shapes paradigm shifts and social norms related to gender issues. This research aims to identify how the Instagram account @unwomen represents gender equality and women's empowerment issues and explores how @unwomen can influence individuals' perceptions of these issues. The method used is qualitative descriptive with a critical paradigm. Data were collected using a random selection method on posts from the Instagram account @unwomen to provide a fair and representative overview. The posts contain content related to women's rights, employment, closing gender gaps, and the role of women in development. The analysis process involves using critical discourse analysis by Sara Mills, incorporating two concepts: subject-object position and writer-reader position. The analysis includes the identification of text, narratives, and images reflecting gender equality and women's empowerment issues, followed by noting keywords or phrases used in the discourse. Research findings reveal that social media, particularly through the @unwomen account, significantly influences public opinion on gender issues. The subject-object position in gender discourse is reflected in the active role of UN Women as the subject designing messages to influence public opinion as the object. The writer-reader position is reflected in the active role of UN Women as writers responsible for creating messages and content shared using language, images, and specific formats to convey messages and influence the audience. Readers (audience) are individuals or groups consuming and responding to content shared by the @unwomen account. They shape perceptions, interpret messages, and provide feedback. This research integrates Mills' concepts of subject-object and writer-reader positions in the context of social media, highlighting the importance of understanding power dynamics in shaping gender narratives. The results contribute to a deeper understanding of how social media can be used to advocate for gender equality and women's empowerment. Globally, the implications can guide further strategies in addressing gender issues and women's empowerment through social media platforms.