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Understanding lexico‐semantic processing is crucial for dissecting the complexities of language and its disorders. Relatedness‐based measures, or those which investigate the degree of relatedness in meaning between either task items or items produced by participants, offer the opportunity to harness novel computational and analytical techniques from cognitive network science. Recognizing the need to deepen our understanding of lexico‐semantic deficits through diverse experimental and analytical approaches, this review explores the use of such measures in research into language disorders. A comprehensive search of four electronic databases covering publications from the last 11 years (October 2013–September 2024) identified 38 original experimental studies employing relatedness‐based measures in populations with language disorders or other neurological conditions. Articles were examined for the types of tasks used, populations studied, item selection methods and analytical approaches. The predominant use of category fluency tasks emerged across studies, with a notable absence of relatedness judgement tasks or comparable paradigms. Commonly discussed populations included individuals with post‐stroke aphasia, mild cognitive impairment and schizophrenia. Analytical methods varied significantly, ranging from more traditional approaches of clustering and switching to more sophisticated computational techniques. Despite the evident utility of category fluency tasks in research and clinical settings, the review underscores a critical need to diversify experimental paradigms and probe lexico‐semantic processing in a more multifaceted manner. A broadened approach in future language disorder research should incorporate innovative analytical techniques, investigations of neural correlates and a wider array of tasks employing relatedness‐based measures already present in healthy populations. [ABSTRACT FROM AUTHOR]

Creative block is a familiar foe to any who attempt to create and is especially related to "writers block". While significant effort has been focused on developing methods to break such blocks, it remains an active challenge. Here, we focus on the role of semantic memory structure in driving creative block, by having people get "stuck" in a certain part of their semantic memory network. We directly examine whether we can "pull out" a participant from where they got "stuck" in their semantic memory, breaking their creative impasse. Our Associative Creativity Sparker (ACS) is a cognitive network science-based online tool that aims to spark creative ideas and break creative impasse: Once a participant runs out of ideas in a creative idea generation task, word recommendations are suggested to prime new ideas. These word recommendations are either towards or away from previous ideas, as well as close or far from the target object, based on a conceptual space extracted from the participants responses using online text analysis. In Study 1, 121 participants use the ACS to generate creative alternative uses for five different objects and completed creativity and Gf tasks. In Study 2, we repeat the design of Study 1, but further examine the impact of writing experience on the ACS, by examining 120 novice and 120 experienced writers. Across both studies, our results indicate that the location of word recommendations affects the fluency and originality of one's ideas, and that novice and experienced writers differently benefit from these word recommendations. [ABSTRACT FROM AUTHOR]

Author networks in science often rely on citation analyses. In such cases, as in others, network interpretation usually depends on supplementary data, notably about authors' research domains when disciplinary interpretations are sought. More general social networks also face similar interpretation challenges as to the semantic content specificities of their members. In this research-in-progress, we propose to infer author networks not from citation analyses but from topic similarity analyses based on a topic-model of published documents. Such author networks reveal, as we call them, "hidden communities of interest" (HCoIs) whose semantic content can easily be interpreted by means of their associated topics in the model. We use an astrobiology corpus of full-text articles (N = 3,698) to illustrate the approach. Having conducted an LDA topic-model on all publications, we identify the underlying communities of authors by measuring author correlations in terms of topic distributions. Adding publication dates makes it possible to examine HCoI evolution over time. This approach to social networks supplements traditional methods in contexts where textual data are available. [ABSTRACT FROM AUTHOR]

Defect detection is a crucial technology that is extensively employed in the manufacturing industry to monitor and ensure the quality of output. Deep learning models have shown remarkable potential for defect detection. However, the success of these models heavily relies on voluminous training data. Collecting substantial amounts of defect data is challenging in practical settings, and the tedious process of pixel-level defect annotation further complicates the task. Among the common defects encountered in manufacturing, scratches are particularly significant. To address these challenges, this study proposes a two-phase generative adversarial network (GAN) approach for synthesizing defect images and generating semi-automatic pixel-wise labels for anomaly detection. The first phase primarily focuses on synthesizing images, while the second phase involves the pixel-wise labeling of the images. The synthesized paired images generated by the GANs serve as input to the semantic network. Notably, the proposed methodology requires only a few real defect samples for training and a small amount of annotated data, making it practical and computationally efficient for implementation in the manufacturing industry. Experimental results indicate the effectiveness of the proposed deep-learning solution in defect detection, specifically in identifying scratches on various textured and patterned surfaces. A notably high detection accuracy is achieved, validating the potential of the approach in real-world manufacturing scenarios. [ABSTRACT FROM AUTHOR]

"Dogs" are connected to "cats" in our minds, and "backyard" to "outdoors." Does the structure of this semantic knowledge differ across people? Network-based approaches are a popular representational scheme for thinking about how relations between different concepts are organized. Recent research uses graph theoretic analyses to examine individual differences in semantic networks for simple concepts and how they relate to other higher-level cognitive processes, such as creativity. However, it remains ambiguous whether individual differences captured via network analyses reflect true differences in measures of the structure of semantic knowledge, or differences in how people strategically approach semantic relatedness tasks. To test this, we examine the reliability of local and global metrics of semantic networks for simple concepts across different semantic relatedness tasks. In four experiments, we find that both weighted and unweighted graph theoretic representations reliably capture individual differences in local measures of semantic networks (e.g., how related pot is to pan versus lion). In contrast, we find that metrics of global structural properties of semantic networks, such as the average clustering coefficient and shortest path length, are less robust across tasks and may not provide reliable individual difference measures of how people represent simple concepts. We discuss the implications of these results and offer recommendations for researchers who seek to apply graph theoretic analyses in the study of individual differences in semantic memory. [ABSTRACT FROM AUTHOR]

This paper presents the introduction of frame semantics in SciE-Lex, a lexical database of biomedical English, in order to establish frame-based semantic networks among the lexical units contained in the database and draw attention to the domain-specific meanings and syntactic patterns that they exhibit. Taking the general English FrameNet database as a reference and the verbs diminish, compete, and perturb as an illustration, this paper shows how modelling the syntacticosemantic properties of biomedical English results in three scenarios: (a) the general English FrameNet analysis is fully adequate for biomedical usage (b) the frame elements in the general English FrameNet do not fully characterize the semantic and syntactic properties of the lexical items in biomedical English, so an existing frame needs to be customized (c) the frame(s) evoked in general English are different, so either the customization of an existing frame or the addition of a new frame is necessary. [ABSTRACT FROM AUTHOR]

We analyze here how pre-service teachers explicate their views about the waveparticle duality of photons and what role it plays in their arguments supporting the quantum nature of light. The data for the analysis is provided by 12 written reports about the double-slit experiment with feeble light. The analysis is based on constructing semantic networks corresponding to pre-service teachers’ written texts. Contingency-like associative correlation between word-pairs is used to differentiate between word-pairs, where associations of two terms or words is systematic. Such associations indicate connections, which are significant for key term vocabularies in construction of inferences and arguments. Based on that information of the key vocabulary we then construct the structure of pre-service teachers’ argument for the nature of the photon and its wave-particle duality, in the form of directed argument graphs (DAGs). The results show that argument structures in four to six out of 12 cases meet the goals set for pre-service teacher education. In these cases, experimental aspects and wave-particle duality play an important role in the pre-service teachers’ argument and its structure. [ABSTRACT FROM AUTHOR]

Copyright of Artnodes is the property of Universitat Oberta de Catalunya and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the realm of linguistics, the concept of "semanticity" was recently introduced as a novel measure designed to study linguistic networks. In a given text, semanticity is defined as the ratio of the potential number of meanings associated with a word to the number of different words with which it is linguistically linked. This concept provides a quantitative indicator that reflects a word's semantic complexity and its role in a language. In this pilot study, we applied the semanticity measure to the Catalan language, aiming to investigate its effectiveness in automatically distinguishing content words from function words. For this purpose, the measure of semanticity has been applied to a large corpus of texts written in Catalan. We show that the semanticity of words allows us to classify the word classes existing in Catalan in a simple way so that both the semantic and syntactic capacity of each word within a language can be integrated under this parameter. By means of this semanticity measure, it has been observed that adverbs behave like function words in Catalan. This approach offers a quantitative and objective tool for researchers and linguists to gain insights into the structure and dynamics of languages, contributing to a deeper understanding of their underlying principles. The application of semanticity to Catalan is a promising pilot study, with potential applications in other languages, which will allow progress to be made in the field of theoretical linguistics and contribute to the development of automated linguistic tools. [ABSTRACT FROM AUTHOR]

This is a review of a range of empirical studies that use digital text algorithms to predict and model response patterns from humans to Likert-scale items, using texts only as inputs. The studies show that statistics used in construct validation is predictable on sample and individual levels, that this happens across languages and cultures, and that the relationship between variables are often semantic instead of empirical. That is, the relationships among variables are given a priori and evidently computable as such. We explain this by replacing the idea of “nomological networks” with “semantic networks” to designate computable relationships between abstract concepts. Understanding constructs as nodes in semantic networks makes it clear why psychological research has produced constant average explained variance at 42% since 1956. Together, these findings shed new light on the formidable capability of human minds to operate with fast and intersubjectively similar semantic processing. Our review identifies a categorical error present in much psychological research, measuring representations instead of the purportedly represented. We discuss how this has grave consequences for the empirical truth in research using traditional psychometric methods. [ABSTRACT FROM AUTHOR]

Increased schizotypal traits have previously been associated with atypical semantic cognition in community samples. However, no study has yet examined whether adults diagnosed with schizotypal personality disorder (SPD) display atypical semantic fluency and memory. We hypothesized that 24 adults diagnosed with SPD would name more idiosyncratic words on the semantic fluency task and show decreased semantic recall for animal and fruit category words compared with 29 participants with borderline personality disorder (BPD) and a community sample of 96 age-matched controls. We examined whether atypical semantic cognition was specifically associated with disorganized and eccentric speech and thinking, or more broadly with pathological personality traits and personality functioning. Our main hypothesis was confirmed, as the SPD participants named more idiosyncratic words and recalled fewer semantically related words compared with controls. Surprisingly, participants with BPD likewise named more atypical words compared with controls. More idiosyncratic semantic fluency was associated with more eccentric speech and thinking. Increased idiosyncratic semantic fluency and reduced semantic recall were both coupled to increased detachment and lowered personality functioning, while reduced semantic recall further was related to increased interpersonal problems. Our findings suggest that persons with SPD, and to a lesser degree BPD, show atypical semantic cognition, which is associated with eccentric speech and thinking, and more broadly with impaired personality function, social withdrawal, and emotional flatness. The idiosyncratic semantic cognition may worsen difficulties with social reciprocity seen in SPD and BPD. [ABSTRACT FROM AUTHOR]

Searching social media to find relevant semantic domains often results in large text files, many of which are irrelevant due to cross-domain content resulting from word polysemy, abstractness, and degree centrality. Through an iterative pruning process, Cascaded Semantic Fractionation (CSF) systematically removes these cross-domain links. The social network procedure performs community detection in semantic networks, locates the semantic groups containing the terms of interest, excludes intergroup links, and repeats community detection on the pruned intragroup network until the domain of interest is clarified. To illustrate CSF, we analyzed public Facebook posts, using the CrowdTangle app for historical data search, from February 3, 2020, to March 13, 2021, about the possible Wuhan lab leak of COVID-19 over a daily interval. The initial search using keywords located six multi-day bursts of posts of more than 500 per day among 95 K posts. These posts were network analyzed to find the domain of interest using the iterative community detection and pruning process. CSF can be applied to capture the evolutions in semantic domains over time. At the outset, the lab leak theory was presented in conspiracy theory terms. Over time, the conspiratorial elements washed out in favor of an accidental release as the issue moved from social to mainstream media and official government views. CSF identified the relevant social media semantic domain and tracked its changes. [ABSTRACT FROM AUTHOR]

The purpose of this work is to describe the orkg-Leaderboard software designed to extract leaderboards defined as task–dataset–metric tuples automatically from large collections of empirical research papers in artificial intelligence (AI). The software can support both the main workflows of scholarly publishing, viz. as LaTeX files or as PDF files. Furthermore, the system is integrated with the open research knowledge graph (ORKG) platform, which fosters the machine-actionable publishing of scholarly findings. Thus, the systemsss output, when integrated within the ORKG's supported Semantic Web infrastructure of representing machine-actionable 'resources' on the Web, enables: (1) broadly, the integration of empirical results of researchers across the world, thus enabling transparency in empirical research with the potential to also being complete contingent on the underlying data source(s) of publications; and (2) specifically, enables researchers to track the progress in AI with an overview of the state-of-the-art across the most common AI tasks and their corresponding datasets via dynamic ORKG frontend views leveraging tables and visualization charts over the machine-actionable data. Our best model achieves performances above 90% F1 on the leaderboard extraction task, thus proving orkg-Leaderboards a practically viable tool for real-world usage. Going forward, in a sense, orkg-Leaderboards transforms the leaderboard extraction task to an automated digitalization task, which has been, for a long time in the community, a crowdsourced endeavor. [ABSTRACT FROM AUTHOR]

Previous studies showed that word learning is affected by children's existing knowledge. For instance, knowledge of semantic category aids word learning, whereas a dense phonological neighbourhood impedes learning of similar‐sounding words. Here, we examined to what extent children associate similar‐sounding words (e.g., rat and cat) with objects of the same semantic category (e.g., both are animals), that is, to what extent children assume meaning overlap given form overlap between two words. We tested this by first presenting children (N = 93, Mage = 22.4 months) with novel word‐object associations. Then, we examined the extent to which children assume that a similar sounding novel label, that is, a phonological neighbour, refers to a similar looking object, that is, a likely semantic neighbour, as opposed to a dissimilar looking object. Were children to preferentially fixate the similar‐looking novel object, it would suggest that systematic word form‐meaning relations aid referent selection in young children. While we did not find any evidence for such word form‐meaning systematicity, we demonstrated that children showed robust learning for the trained novel word‐object associations, and were able to discriminate between similar‐sounding labels and also similar‐looking objects. Thus, we argue that unlike iconicity which appears early in vocabulary development, we find no evidence for systematicity in early referent selection. [ABSTRACT FROM AUTHOR]

TikTok’s rapid growth in the past few years, especially in the younger demographic, may signal a market shift. With children, teens, and young adults reportedly making up 40% to 60% of its user base, the platform is becoming the strongest challenger to YouTube, Facebook, and Instagram. The most followed TikTok celebrities are mostly young people who have either grown up with the platform or recently extended their popularity from other platforms to reach new audiences. This research investigates the discursive strategies and persona performances employed by the top 25 TikTok celebrities under the age of 25 in both popular content and content marked as advertising. A large sample of TikTok content metadata was collected using API interrogation. From each of the 25 young TikTok celebrities, up to 1,000 videos per user (N = 22,650) are explored using quantitative approaches. Two subsamples are analysed using visual, rhetorical, and narrative analysis to evaluate the most popular content (Np = 226) and content marked as advertising using the TikTok ad flagging (Na = 213). The findings include the identification of seven persona performance types and a significant difference in terms of performed ordinariness in content marked as advertising.

This is a review of a range of empirical studies that use digital text algorithms to predict and model response patterns from humans to Likert-scale items, using texts only as inputs. The studies show that statistics used in construct validation is predictable on sample and individual levels, that this happens across languages and cultures, and that the relationship between variables are often semantic instead of empirical. That is, the relationships among variables are given a priori and evidently computable as such. We explain this by replacing the idea of “nomological networks” with “semantic networks” to designate computable relationships between abstract concepts. Understanding constructs as nodes in semantic networks makes it clear why psychological research has produced constant average explained variance at 42% since 1956. Together, these findings shed new light on the formidable capability of human minds to operate with fast and intersubjectively similar semantic processing. Our review identifies a categorical error present in much psychological research, measuring representations instead of the purportedly represented. We discuss how this has grave consequences for the empirical truth in research using traditional psychometric methods.

Searching social media to find relevant semantic domains often results in large text files, many of which are irrelevant due to cross-domain content resulting from word polysemy, abstractness, and degree centrality. Through an iterative pruning process, Cascaded Semantic Fractionation (CSF) systematically removes these cross-domain links. The social network procedure performs community detection in semantic networks, locates the semantic groups containing the terms of interest, excludes intergroup links, and repeats community detection on the pruned intragroup network until the domain of interest is clarified. To illustrate CSF, we analyzed public Facebook posts, using the CrowdTangle app for historical data search, from February 3, 2020, to March 13, 2021, about the possible Wuhan lab leak of COVID-19 over a daily interval. The initial search using keywords located six multi-day bursts of posts of more than 500 per day among 95 K posts. These posts were network analyzed to find the domain of interest using the iterative community detection and pruning process. CSF can be applied to capture the evolutions in semantic domains over time. At the outset, the lab leak theory was presented in conspiracy theory terms. Over time, the conspiratorial elements washed out in favor of an accidental release as the issue moved from social to mainstream media and official government views. CSF identified the relevant social media semantic domain and tracked its changes.

We analyze here how pre-service teachers explicate their views about the wave-particle duality of photons and what role it plays in their arguments supporting the quantum nature of light. The data for the analysis is provided by 12 written reports about the double-slit experiment with feeble light. The analysis is based on constructing semantic networks corresponding to pre-service teachers’ written texts. Contingency-like associative correlation between word-pairs is used to differentiate between word-pairs, where associations of two terms or words is systematic. Such associations indicate connections, which are significant for key term vocabularies in construction of inferences and arguments. Based on that information of the key vocabulary we then construct the structure of pre-service teachers’ argument for the nature of the photon and its wave-particle duality, in the form of directed argument graphs (DAGs). The results show that argument structures in four to six out of 12 cases meet the goals set for pre-service teacher education. In these cases, experimental aspects and wave-particle duality play an important role in the pre-service teachers’ argument and its structure.

Introduction: The current context of information manipulation advocates computational data processing for the generation of detailed and precise information in the academic field. Objective: Present a Systematic Literature Review (SLR) supported by bibliometrics on semantic networks of concept maps (RSMC) - networks of meanings in which the nodes are concepts belonging to a collection of concept maps and the edges are semantic relations established between these concepts. Methodology: The SLR followed Kitchenham's proposal and was implemented in the online tool Parsifal. To provide breadth to the research, bib files from ACM, ScienceDirect and Engineering Village sources were included in the methodological conduction through metadata treatment, which increased the number of documents found and analyzed. Results: As a result, it was found, in relation to the specialized bibliometric procedure, that the addition of academic bases resulted in an increase in the search breadth by 56.8% and, consequently, in the results of the bibliographic and the bibliometric analysis conducted. Conclusion: In relation to the documents analyzed, the most relevant conclusions were: a) that the concept of RSMC is not yet formalized in academic literature and b) that there is continued interest in pedagogical practices involving, mainly, conceptual maps and, in addition, semantic networks and lexical semantic relations in the search scope performed. The study presented here can contribute to qualifying the practice of systematic literature review with the adoption of bibliometric procedures supported by technological tools, such as the Bibliometrix package. [ABSTRACT FROM AUTHOR]

Identity is personal elaboration built based on interaction within sociocultural historical context. The masculinity identity has its foundation in gender roles, which influence and determine the behavior of men. The main aim is analyze the psychological meaning of masculine identity in men of different generations. The mixed methodology, applied semantic network technique to 268 men, from three generational cohorts and of sexual diversity. The results show that the concept of "being a man" is well defined in the three populations, being the young and those of diversity who, with the transition from modernity to postmodernity, are forced to transform their masculine identity. [ABSTRACT FROM AUTHOR]

This article is part of the findings of an investigation on the ideological processes of environmental conscience in undergraduate students of the Autonomous University of Zacatecas. The objective of this research was to know, to explain and analyze environmental conscience (also called "ecological") in its political and social issues. It is considered that the concept of "sustainability" is an obligatory reference in contemporary societies to think and act with respect to the different environmental problems on global and local levels. The technique of natural semantic networks was applied to obtain information on the psychosocial meaning of sustainability among students from six different academic units. Although the results and their interpretation are typical of the delimited social group, there are some inferences that would be worth reflecting on more broadly. In this way, it is concluded that sustainability serves as a macrocultural device to promote a new model of economic development that seeks to resolve the contradictions of contemporary capitalism. [ABSTRACT FROM AUTHOR]

Copyright of Mediterranean Journal of Communication / Revista Mediterránea de Comunicación is the property of Revista Mediterranea de Comunicacion / Mediterranean Journal of Communication and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the face of the rapid erosion of both tangible and intangible cultural heritage globally, the urgency for effective, wide-ranging preservation methods has never been greater. Traditional approaches in cultural preservation often focus narrowly on specific niches, overlooking the broader cultural tapestry, particularly the preservation of everyday cultural elements. This article addresses this critical gap by advocating for a comprehensive, scalable model for cultural preservation that leverages machine learning and big data analytics. This model aims to document and archive a diverse range of cultural artifacts, encompassing both extraordinary and mundane aspects of heritage. A central issue highlighted in the article is the dominance of English, especially in popular culture, and its role in reshaping cognitive patterns, leading to the loss of traditional languages and cultures. This trend, favoring orality over literacy, amplifies the need for the preservation actions proposed. The article underscores the importance of establishing a more meaningful and comprehensive lingua franca as the first step in this endeavor. This approach is not just about safeguarding languages but also about preserving the nuanced ways in which different linguistic paradigms shape our understanding of the world. The paper explores how language evolution, translation nuances, and the unique perspectives inherent in various linguistic paradigms contribute to a rich cultural diversity. It also examines the impact of technology and social media on traditional cultural norms and the critical need for deliberate efforts to reinforce endangered languages and practices. The proposed model aims to bridge the gap between past and present cultural expressions, using technology to reinforce mother-tongue contexts and enhance the appreciation of diverse human identities and worldviews. Through this, the article presents a compelling case for a more inclusive and holistic approach to preserving the global tapestry of cultural heritage. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Lingüística y Lenguas Aplicadas (RLLA) is the property of Universidat Politecnica de Valencia, Department of Applied Linguistics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Demand planning in the semiconductor industry can be complicated due to challenges such as extended cycle times, rapid innovation cycles, and the Bullwhip Effect. Approaches that provide a deeper understanding of customer orders and their associated demand are crucial to enhance demand planning accuracy. Previous studies have employed convolutional neural networks (CNNs) on heat map representations of customer order transactions to effectively classify customer order behaviors (COBs), leading to improved insights into customer behavior. However, these approaches have primarily focused on analyzing customer order patterns without considering contextual information, such as financial or market-related data, which can benefit the classification process. Therefore, we propose a Semantic Context Information Modeling methodology for Neural Networks (SCIM-NN) based on ontologies, knowledge graph embeddings, and multi-stream neural networks to include context information for a classification task. We show the application of SCIM-NN on a use case in the domain of COB and evaluate the performance of the context-aware model on customer data of Infineon Technologies AG. Results indicate that including context information improves the overall classification performance compared to a benchmark CNN. [ABSTRACT FROM AUTHOR]

While there are well-known demonstrations that children can use distributional information to acquire multiple components of language, the underpinnings of these achievements are unclear. In the current paper, we investigate the potential pre-requisites for a distributional learning model that can explain how children learn their first words. We review existing literature and then present the results of a series of computational simulations with Vector Space Models, a type of distributional semantic model used in Computational Linguistics, which we evaluate against vocabulary acquisition data from children. We focus on nouns and verbs, and we find that: (i) a model with flexibility to adjust for the frequency of events provides a better fit to the human data, (ii) the influence of context words is very local, especially for nouns, and (iii) words that share more contexts with other words are harder to learn. [ABSTRACT FROM AUTHOR]

In the realm of linguistics, the concept of “semanticity” was recently introduced as a novel measure designed to study linguistic networks. In a given text, semanticity is defined as the ratio of the potential number of meanings associated with a word to the number of different words with which it is linguistically linked. This concept provides a quantitative indicator that reflects a word’s semantic complexity and its role in a language. In this pilot study, we applied the semanticity measure to the Catalan language, aiming to investigate its effectiveness in automatically distinguishing content words from function words. For this purpose, the measure of semanticity has been applied to a large corpus of texts written in Catalan. We show that the semanticity of words allows us to classify the word classes existing in Catalan in a simple way so that both the semantic and syntactic capacity of each word within a language can be integrated under this parameter. By means of this semanticity measure, it has been observed that adverbs behave like function words in Catalan. This approach offers a quantitative and objective tool for researchers and linguists to gain insights into the structure and dynamics of languages, contributing to a deeper understanding of their underlying principles. The application of semanticity to Catalan is a promising pilot study, with potential applications in other languages, which will allow progress to be made in the field of theoretical linguistics and contribute to the development of automated linguistic tools.

We investigate the psycho-linguistic features of the online discourse over climate change, focusing on its modifications throughout the years 2017–2019 as a result of collective actions emerging and spreading worldwide. We seek to understand the emerging connection between digital activism and the psychological processes related to its social drives. To this end, a semantic network is derived from the social platform Twitter, and its evolution is traced over time, tracking textual proxies of social identity and empowerment. Original proposals are made to identify communities and highlight the most important semantic contents of the corpus from a network perspective. These evaluations on semantic communities of related concepts further detail the shift in the rhetoric of collective actions. Finally, we explore projection of the ingroup to the future in the online discourse about climate change, which can point to developments of pro-environmental campaigns. • We have recently experienced a steady increase of collective engagement for climate. • Ranking and community detection uncover the key elements of the online discourse around climate change. • Climate activism is fostered by social drives common to general collective protest. • Affiliation and empowerment characterize the calling to action for climate. • Ingroup mobilization toward the future is specific to 2019 climate activism. [ABSTRACT FROM AUTHOR]

TikTok's rapid growth in the past few years, especially in the younger demographic, may signal a market shift. With children, teens, and young adults reportedly making up 40% to 60% of its user base, the platform is becoming the strongest challenger to YouTube, Facebook, and Instagram. The most followed TikTok celebrities are mostly young people who have either grown up with the platform or recently extended their popularity from other platforms to reach new audiences. This research investigates the discursive strategies and persona performances employed by the top 25 TikTok celebrities under the age of 25 in both popular content and content marked as advertising. A large sample of TikTok content metadata was collected using API interrogation. From each of the 25 young TikTok celebrities, up to 1,000 videos per user (N =22,650) are explored using quantitative approaches. Two subsamples are analysed using visual, rhetorical, and narrative analysis to evaluate the most popular content (Np =226) and content marked as advertising using the TikTok ad flagging (Na =213). The findings include the identification of seven persona performance types and a significant difference in terms of performed ordinariness in content marked as advertising. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article correlates fine-grained semantic variability and change with measures of occurrence frequency to investigate whether a word's degree of semantic change is sensitive to how often it is used. We show that this sensitivity can be detected within a short time span (i.e., 20 years), basing our analysis on a large corpus of German allowing for a high temporal resolution (i.e., per month). We measure semantic variability and change with the help of local semantic networks, combining elements of deep learning methodology and graph theory. Our micro-scale analysis complements previous macro-scale studies from the field of natural language processing, corroborating the finding that high token frequency has a negative effect on the degree of semantic change in a lexical item. We relate this relationship to the role of exemplars for establishing form–function pairings between words and their habitual usage contexts. [ABSTRACT FROM AUTHOR]

To date, the application of semantic network methodologies to study cognitive processes in psychological phenomena has been limited in scope. One barrier to broader application is the lack of resources for researchers unfamiliar with the approach. Another barrier, for both the unfamiliar and knowledgeable researcher, is the tedious and laborious preprocessing of semantic data. We aim to minimize these barriers by offering a comprehensive semantic network analysis pipeline (preprocessing, estimating, and analyzing networks), and an associated R tutorial that uses a suite of R packages to accommodate the pipeline. Two of these packages, SemNetDictionaries and SemNetCleaner, promote an efficient, reproducible, and transparent approach to preprocessing linguistic data. The third package, SemNeT, provides methods and measures for estimating and statistically comparing semantic networks via a point-and-click graphical user interface. Using real-world data, we present a start-to-finish pipeline from raw data to semantic network analysis results. This article aims to provide resources for researchers, both the unfamiliar and knowledgeable, that reduce some of the barriers for conducting semantic network analysis. [ABSTRACT FROM AUTHOR]

ABSTRACT Semantic portals, such as socio-technical artifacts, involve different actors that are aligned to offer communication and information search services more quickly and accurately. The objective of this study is to describe the network of actors involved in the construction of semantic portals and their interrelationships. The precepts of the Actor-Network Theory were used as a methodology to design a network of semantic portals from selected studies in the literature. The results indicated that the design of the network of semantic portals points to actions of human actors: portal administrators, portal developers and end users, but also of non-human actors, which are made up mainly of technologies. Each actor has his or her interest contemplated as the other actors fulfill their roles towards a common goal - information of high added value. It can be concluded that the study of the features of the portal actors' network can guide changes in current standards, focusing on the quality of research results and the informational needs of the user community.

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This paper attempts to explore the nature of polysemous verbs using a cognitive linguistic framework from the perspective of second-language (L2) users: specifically, English as a foreign language (EFL) students in a Saudi Arabian university context. Drawing on Pulman (1983), this analysis examines multiple senses of the verbs break, run, and draw using prototype theory and seeks to identify any semantic patterns in the priority accorded to the polysemous senses attached to each verb by the learner. A questionnaire with sentences as placeholders embodying different senses of each verb and a 4-point goodness-of-fit scale were prepared. The results of this study show that non-native learners of English (EFL learners) are aware that there is a semantic network of meanings for polysemous verbs, and that these meanings exist on a continuum from more central (about three to four senses for each verb, which could be considered core/generic meanings) to more peripheral (between one to three senses, which could be considered more specific meanings). Correlation analyses between learners' perceptions of a verb's polysemy and vocabulary size and years of learning English were performed, yielding few positive relationships, and those only weakly significant. [ABSTRACT FROM AUTHOR]

We report data from the Small World of Singlish Words (SWOSW) Project, where word associations are obtained for a list of common Singlish words. Singapore English, colloquially known as Singlish, is a dialect of English spoken in Singapore. Free association data on these words were collected from a young adult population of university undergraduates using an online survey platform. This data enables the construction of semantic networks, allowing one to examine the semantic structure of individual words in the Singlish lexicon, as well as to compare differences in semantic structure across groups of participants.