Pattern Language Research Articles

Language Choices and Digital Identity of High School Student Text Messages in the New Capital City of Indonesia: Implication for Language Education

This research investigates the language choices and digital identities represented in text messages exchanged by high school students in the New Capital City of Indonesia (Ibu Kota Nusantara/IKN) and the implications of these linguistic practices for language teaching. Using a corpus of messages transmitted via WhatsApp collected from 100 high school students, this study produced 2.1 million tokens of 83,414 word types. This study uses corpus linguistic analysis to investigate the distinctive features of language usage, lexical changes, and communication trends in the digital discourse of IKN high school students. Data analysis utilizes features from AntConc corpus tools, including word lists, collocations, concordances, and N-grams. Concerning the features uncovered the subtle nuances that contribute to the unique linguistic identity of the student authors. This research highlights how digital language patterns attribute individual identity within the context of IKN high school students. The data analysis discovers three linguistic patterns in the electronic texts produced by IKN High School students, reflecting their general digital identity as language users in IKN, namely (1) lexical choice, (2) orthographic selection, and (3) lexical bundles. Furthermore, the study reveals the complex construction of digital identity through language, with students negotiating social positions, connections, and personal identities via their linguistic choices in electronic texts. The research findings contribute to the implications of language teaching in the IKN area and a deeper understanding of how students within the IKN area express themselves through language in the virtual realm, thus shaping their digital identities.

The Pragmatics of Gaze Patterns in a Local Family Sign Language from Guatemala

In this study, we document the coordination of eye gaze and manual signing in a local sign language from Nebaj, Guatemala. We analyze gaze patterns in two conversations in which signers described the book Frog Where Are You to an interlocutor. The signers include a deaf child who narrated the book to a hearing interlocutor and her grandfather, who is also deaf, as he described the same book to his hearing grandson during a separate conversation. We code the two narratives for gaze target and sign type, analyzing the relationship between eye gaze and sign type as well as describing patterns in the sequencing of eye gaze targets. Both signers show a strong correlation between sign type and the direction of their eye gaze. As in previous literature, signers looked to a specialized medial space while producing signs that enact the action of characters in discourse in contrast to eye gaze patterns for non-enacting signs. Our analysis highlights both pragmatic–interactional and discursive–narrative functions of gaze. The pragmatic–interactional use of gaze primarily relates to the management of visual attention and turn-taking, while the discursive–narrative use of gaze marks the distinction between narrator and character perspective within stretches of narration.

Echoes of Culture: Relationships of Implicit and Explicit Attitudes With Contemporary English, Historical English, and 53 Non-English Languages

Attitudes are intertwined with culture and language. But to what extent? Emerging perspectives in attitude research suggest that cultural representations in language are more related to implicitly measured (vs. explicitly measured) attitudes, and that such relationships persist across history and diverse languages. We offer a comprehensive test of these ideas by correlating (a) attitudes toward 55 topics (e.g., Rich/Poor, Dogs/Cats, Love/Money) from ~100,000 U.S. English-speaking participants with (b) representations of those same topics in word embeddings from contemporary English text, 200 years of English books, and 53 non-English languages. Strong and robust relationships emerged between representations in contemporary English and implicitly but not explicitly measured attitudes. Moreover, strong correlations with implicitly measured attitudes persisted across 200 years of books, and most non-English languages. Results provide new insights into the nature of implicitly measured attitudes and how they are intertwined with cultural representations that are relatively hidden in patterns of language across time and place.

Revealing the Unseen: AI Chain on LLMs for Predicting Implicit Data Flows to Generate Data Flow Graphs in Dynamically-Typed Code

Data flow graphs (DFGs) capture definitions (defs) and uses across program blocks, which is a fundamental program representation for program analysis, testing and maintenance. However, dynamically-typed programming languages like Python present implicit data flow issues that make it challenging to determine def-use flow information at compile time. Static analysis methods like Soot and WALA are inadequate for handling these issues, and manually enumerating comprehensive heuristic rules is impractical. Large pre-trained language models (LLMs) offer a potential solution, as they have powerful language understanding and pattern matching abilities, allowing them to predict implicit data flow by analyzing code context and relationships between variables, functions, and statements in code. We propose leveraging LLMs’ in-context learning ability to learn implicit rules and patterns from code representation and contextual information to solve implicit data flow problems. To further enhance the accuracy of LLMs, we design a five-step Chain of Thought (CoT) and break it down into an AI chain, with each step corresponding to a separate AI unit to generate accurate DFGs for Python code. Our approach’s performance is thoroughly assessed, demonstrating the effectiveness of each AI unit in the AI Chain. Compared to static analysis, our method achieves 82% higher def coverage and 58% higher use coverage in DFG generation on implicit data flow. We also prove the indispensability of each unit in the AI Chain. Overall, our approach offers a promising direction for building software engineering tools by utilizing foundation models, eliminating significant engineering and maintenance effort, but focusing on identifying problems for AI to solve.

Everyday positivity: An appraisal analysis of online identity in food blogs

This paper analyses a number of “About me” sections of Danish food blogs to the purpose of investigating how bloggers discursively construct online identities. The blogs belong to a hybrid genre, containing both commercial products (recipes) and the personal narratives of bloggers on their everyday lives. Because of the highly subjective character of the narratives, the appraisal framework from systemic functional linguistics was chosen as an analytical tool to uncover patterns in evaluative language. Through the selection of content and the way it is discursively presented, bloggers seek to create a bond with an imagined audience, with whom they form a so-called ambient affiliation. Two such bonds are identified: a positive-thinking bond and a feeling-of-safety bond.

Development of the Fetal Brain Corticocortical Structural Network during the Second-to-Third Trimester Based on Diffusion MRI.

During the second-to-third trimester, the neuronal pathways of the fetal brain experience rapid development, resulting in the complex architecture of the interwired network at birth. While diffusion MRI-based tractography has been employed to study the prenatal development of structural connectivity network (SCN) in preterm neonatal and postmortem fetal brains, the in utero development of SCN in the normal fetal brain remains largely unknown. In this study, we utilized in utero dMRI data from human fetuses of both sexes between 26 and 38 gestational weeks to investigate the developmental trajectories of the fetal brain SCN, focusing on intrahemispheric connections. Our analysis revealed significant increases in global efficiency, mean local efficiency, and clustering coefficient, along with significant decrease in shortest path length, while small-worldness persisted during the studied period, revealing balanced network integration and segregation. Widespread short-ranged connectivity strengthened significantly. The nodal strength developed in a posterior-to-anterior and medial-to-lateral order, reflecting a spatiotemporal gradient in cortical network connectivity development. Moreover, we observed distinct lateralization patterns in the fetal brain SCN. Globally, there was a leftward lateralization in network efficiency, clustering coefficient, and small-worldness. The regional lateralization patterns in most language, motor, and visual-related areas were consistent with prior knowledge, except for Wernicke's area, indicating lateralized brain wiring is an innate property of the human brain starting from the fetal period. Our findings provided a comprehensive view of the development of the fetal brain SCN and its lateralization, as a normative template that may be used to characterize atypical development.

Exploring the influence of user characteristics on verbal aggression towards social chatbots

ABSTRACT Chatbots possess great potential benefits, yet concerns persist regarding users adopting inappropriate, offensive language. This research delved into the influence of user characteristics on verbally aggressive behaviours towards social chatbots. Employing a mixed-method study, we examined individual characteristics such as personal dispositions, offensive language patterns, academic majors, and prior experiences with conversational agents. Findings from a ten-day field experiment involving 33 participants using a real-world Telegram-based chatbot app unveiled that users' anthropomorphism, computer-related major, and gender significantly impact their moral emotions and evaluations of the chatbot's capabilities. Moreover, employing offensive language towards the chatbot detrimentally impacted users' perceptions of its abilities, helpfulness, and likability. The research findings advocate for ongoing monitoring and effective resolution of users' behaviours regarding the use of offensive language in their interactions with a chatbot. Additionally, the results underscore the importance of incorporating diverse perspectives into chatbot design to address biases and offensive utterances.

All Watched Over by Our Data Double

Abstract The term data double denotes information generated by and collected from users of networked communications to construct relational databases in marketing and other domains. Each subject (user) of this surveillance-panoptical system inevitably informs and objectifies all other subjects. Social media experiences are based on our continuously “tracked” engagement. Surveillance functions across written and spoken language, biometrics, geolocation, and visual and behavioral patterns. This text is primarily concerned with visual media and its production and (re)circulation as the accumulation of data through uploading, viewing, liking, commenting, remixing, and sharing. The article explores how selected media artists reflect upon the potential of recirculating information to reveal our data doubles and the surveillance-panoptical system.

Humour, politainment and fandom: A semiotic-narrative analysis of unofficial profiles of Spanish politicians

In today’s media landscape, concepts like Americanization and professionalization of politics are evolving, driven by technological advancements and social media usage. The internet has transformed political communication, emphasizing emotional appeal and user response, leading to a rise in politainment and celebrity politicians. This shift towards ‘pop politics’ blurs the line between entertainment and information, with virality and humour playing key roles. This study conducts a semiotic-narrative analysis of unofficial social media profiles of Spanish political representatives, examining their language, content, and usage patterns resembling fan communities. Parodic Twitter profiles of prominent figures like Pedro Sánchez, Yolanda Díaz and Isabel Díaz Ayuso, along with Ada Colau's Instagram account, are analyzed. The findings reveal a trend towards employing politainment strategies to engage social media users, fostering emotional connections akin to non-political fan communities.

The Image of the Patriot and the Enemy of the People in Selected Documents from the State Security Archive

The paper explores the linguistic devices and strategies used by State Security collab-orators to characterize the personalities and actions of targets of operational interest. On the one hand, the text examines documents from the State Security archives (re-cruitment offers and recruitment reports) that describe the ways in which new collabo-rators were recruited. On the other, the descriptions of the subjects of the reports in the reports are of interest. The research focuses on the communicative language pat-terns used and described in the relevant documents. As a source of excerpted material for scientific analysis, we used the book "Pastors Agents of the State Security Service" by Angel Pilev. Through an applied combination of research methods - lexical-semantic and discourse analysis, we search for lexical means with connotative load, as well as characterizing lexemes, building the opposite images - of the patriot informer and the enemy of the people reasons. By applying the close reading method, we aim at explicating the reader's point of view with minimal historical context. The linguistic material shows that the documents of the State Security build a very negative image of the enemy of the people. The strategy of the recruiters is also analyzed, emphasizing patriotic feelings, patriotic duty, expression of love for the motherland, and the noble deed to be done by the agents. The text emphasizes that it is to linguistics, along with other humanities that expectations are addressed for an objective, publicly accessible, and convincing interpretation of the era of socialism.

Development of a Sign Language recognizer using LSTM

Sign language recognition plays a crucial role in bridging communication gaps between the deaf community and the hearing population. This paper presents the development of a sign language recognizer using Long Short-Term Memory (LSTM) networks, a type of recurrent neural network well-suited for sequence prediction tasks. The proposed model aims to accurately interpret hand gestures into text, enhancing accessibility and communication for individuals with hearing impairments. Our approach utilizes a custom-built dataset of sign language gestures, which undergoes preprocessing to extract relevant features such as hand position, orientation, and movement. The LSTM-based architecture is designed to capture the temporal dynamics of these gestures, enabling the recognition of complex sign language patterns. We train the model using a supervised learning approach. Our method demonstrates superior accuracy in recognizing sign language gestures compared to traditional machine learning methods and other deep learning architectures. This advancement contributes significantly to the field of assistive technologies. Future work will explore the integration of additional modalities, such as facial expressions and body movements, to further enhance the system's performance and usability.

A Rumors Detection Method Using T5-Based Prompt Learning

Accurately identifying rumor information is crucial for efficient information assessment. Pretrained Language Models (PLMs) are trained on large text data, understanding and generating human-like language patterns, and fine-tuning for specific NLP tasks. However, adjusting PLMs typically requires modifications for specific tasks, creating a gap between pretraining and task execution. Prompt learning reduces this gap. We introduce “Prompt Learning for Rumor Detection” (PLRD) based on T5, which utilizes T5-generated prompt templates to transform the detection task into a prompt-driven learning framework. Through precise prompt guidance, PLRD leverages model knowledge, enhancing rumor detection capabilities, especially in data-scarce scenarios. Experimental validation on Weibo and Twitter datasets confirms PLRD's superiority over existing methods, particularly in scenarios with limited data. Comparative analysis against state-of-the-art methods highlights PLRD's competitiveness and advancement in rumor detection.

Using cognitive measures from linguistic relativity research to assess thinking in Yucatec Maya-Spanish bilingual children

This article uses cognitive measures previously developed within linguistic relativity research to explore the thinking patterns of Yucatec Maya-Spanish bilingual children in the Yucatan peninsula. These measures were designed to detect cognitive patterns associated with specific language patterns. Here, these measures are used to test whether 12 Yucatec Maya-Spanish bilingual children aged 9–11 years old differ cognitively from a similar sample of monolingual Maya children (nine Yucatec Maya-speaking and three Spanish-speaking). The study assesses cognitive associations for two language domains: number marking, a structure-based contrast, and spatial frames of reference, a usage-based contrast. For both number marking and spatial frames of reference, both bilingual and monolingual children provided cognitive responses more like Yucatec Maya monolingual speakers than like Spanish monolinguals. The study also assessed whether the results were affected by factors that have emerged in other studies of bilingual cognition: language status, social community, and language of assessment (‘language mode’). For number marking, there was an effect of the language of assessment, but not of social community or language status. For spatial frames of reference, there were effects of social community and language status but not of the language of assessment. Overall, the results suggest that Yucatec Maya's cognitive habits may continue for some time during the shift from Yucatec Maya monolingualism to bilingualism but with some sensitivity to these other factors. This study offers a positive outlook for efforts to maintain and revitalize the Yucatec Maya language in the Yucatan peninsula.

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Recently, the analysis of emotions in social media has been considered a significant NLP task in digital and social-media-driven environments due to their pervasive influence on communication, culture, and consumer behavior. In particular, the task of Aspect-Based Emotion Analysis (ABEA), which involves analyzing the emotions of various targets within a single sentence, has drawn attention to understanding complex and sophisticated human language. However, ABEA is a challenging task in languages with limited data and complex linguistic properties, such as Korean, which follows spiral thought patterns and has agglutinative characteristics. Therefore, we propose a Korean Target-Attention-Based Emotion Classifier (KOTAC) designed to utilize target information by unveiling emotions buried within intricate Korean language patterns. In the experiment section, we compare various methods of utilizing and representing vectors of target information for the attention mechanism. Specifically, our final model, KOTAC, shows a performance enhancement on the MTME (Multiple Targets Multiple Emotions) samples, which include multiple targets and distinct emotions within a single sentence, achieving a 0.72% increase in F1 score over a baseline model without effective target utilization. This research contributes to the development of Korean language models that better reflect syntactic features by innovating methods to not only obtain but also utilize target-focused representations.

User Dynamics and Thematic Exploration in r/Depression During the COVID-19 Pandemic: Insights From Overlapping r/SuicideWatch Users.

In 2023, the United States experienced its highest- recorded number of suicides, exceeding 50,000 deaths. In the realm of psychiatric disorders, major depressive disorder stands out as the most common issue, affecting 15% to 17% of the population and carrying a notable suicide risk of approximately 15%. However, not everyone with depression has suicidal thoughts. While "suicidal depression" is not a clinical diagnosis, it may be observed in daily life, emphasizing the need for awareness. This study aims to examine the dynamics, emotional tones, and topics discussed in posts within the r/Depression subreddit, with a specific focus on users who had also engaged in the r/SuicideWatch community. The objective was to use natural language processing techniques and models to better understand the complexities of depression among users with potential suicide ideation, with the goal of improving intervention and prevention strategies for suicide. Archived posts were extracted from the r/Depression and r/SuicideWatch Reddit communities in English spanning from 2019 to 2022, resulting in a final data set of over 150,000 posts contributed by approximately 25,000 unique overlapping users. A broad and comprehensive mix of methods was conducted on these posts, including trend and survival analysis, to explore the dynamic of users in the 2 subreddits. The BERT family of models extracted features from data for sentiment and thematic analysis. On August 16, 2020, the post count in r/SuicideWatch surpassed that of r/Depression. The transition from r/Depression to r/SuicideWatch in 2020 was the shortest, lasting only 26 days. Sadness emerged as the most prevalent emotion among overlapping users in the r/Depression community. In addition, physical activity changes, negative self-view, and suicidal thoughts were identified as the most common depression symptoms, all showing strong positive correlations with the emotion tone of disappointment. Furthermore, the topic "struggles with depression and motivation in school and work" (12%) emerged as the most discussed topic aside from suicidal thoughts, categorizing users based on their inclination toward suicide ideation. Our study underscores the effectiveness of using natural language processing techniques to explore language markers and patterns associated with mental health challenges in online communities like r/Depression and r/SuicideWatch. These insights offer novel perspectives distinct from previous research. In the future, there will be potential for further refinement and optimization of machine classifications using these techniques, which could lead to more effective intervention and prevention strategies.

Causal Event Graph-Guided Language-based Spatiotemporal Question Answering

Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question answering (QA). However, due to encoding a text-based projection of the world, they have also been shown to lack a full bodied understanding of such events, e.g., a sense of intuitive physics, and cause-and-effect relationships among events. In this work, we propose using causal event graphs (CEGs) to enhance language understanding of spatiotemporal events in language models, using a novel approach that also provides proofs for the model’s capture of the CEGs. A CEG consists of events denoted by nodes, and edges that denote cause and effect relationships among the events. We perform experimentation and evaluation of our approach for benchmark spatiotemporal QA tasks and show effective performance, both quantitative and qualitative, over state-of-the-art baseline methods.

Association of plasma biomarkers of Alzheimer's disease and related disorders with cognition and cognitive decline: The MYHAT population-based study.

Plasma biomarkers of Alzheimer's disease and related dementias predict global cognitive performance and decline over time; it remains unclear how they associate with changes in different dementia syndromes affecting distinct cognitive domains. In a prospective study with repeated assessments of a randomly selected population-based cohort (n=787, median age 73), we evaluated performance and decline in different cognitive domains over up to 8 years in relation to plasma concentrations of amyloid beta 42/40 (Aβ42/40) ratio, phosphorylated tau181 (p-tau181), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP). Cross-sectionally, memory showed the strongest associations with p-tau181, and attention, executive, and visuospatial functions with NfL.Longitudinally, memory decline was distinguishable with all biomarker profiles dichotomized according to data-driven cutoffs, most efficiently with Aβ42/40. GFAP and Aβ42/40 were the best discriminators of decline patterns in language and visuospatial functions, respectively. These relatively non-invasive tests may be beneficial for clinical screening after replication in other populations and validation through neuroimaging or cerebrospinal fluid analysis. We performed a prospective study with up to 8 years of repeated domain-specific cognitive assessments and baseline plasma Alzheimer's disease and related dementias biomarker measurements in a randomly selected population-based cohort. We considered distinct growth curves of trajectories of different cognitive domains and survival bias induced by missing data by adding quadratic time and applying joint modeling technique. Cross-sectionally, memory showed the strongest associations with plasma phosphorylated tau181, while attention, executive, and visuospatial functions were most strongly associated with neurofilament light chain. Longitudinally, memory and visuospatial declines were most efficiently distinguished by dichotomized amyloid beta 42/40 profile among all plasma biomarkers, while language was by dichotomized glial fibrillary acidic protein. These relatively non-invasive tests may be beneficial for clinical screening; however, they will need replication in other populations and validation through neuroimaging and/or cerebrospinal fluid assessments.

Songs tell a story: The Arc of narrative for music.

Research suggests that a core lexical structure characterized by words that define plot staging, plot progression, and cognitive tension underlies written narratives. Here, we investigate the extent to which song lyrics follow this underlying narrative structure. Using a text analytic approach and two publicly available datasets of song lyrics including a larger dataset (N = 12,280) and a smaller dataset of greatest hits (N = 2,823), we find that music lyrics tend to exhibit a core Arc of Narrative structure: setting the stage at the beginning, progressing the plot steadily until the end of the song, and peaking in cognitive tension in the middle. We also observe differences in narrative structure based on musical genre, suggesting different genres set the scene in greater detail (Country, Rap) or progress the plot faster and have a higher rate of internal conflict (Pop). These findings add to the evidence that storytelling exhibits predictable language patterns and that storytelling is evident in music lyrics.

Evaluating structure and content of parent-implemented early logopaedic intervention models following the three stages of communicative development in children with cleft lip and/or palate: Systematic literature review with narrative synthesis.

The development of communication, speech and language follows three stages (development of the parent-child relationship, interactions and actual speech and language acquisition). Children born with cleft lip and/or palate are at increased risk of communicative problems while parents may be going through an emotionally difficult time. Early parent-implemented logopaedic intervention that supports both parents and child is important. Three systematic reviews have examined the effects of early speech and language interventions, but not their structure and content. To investigate which early parent-implemented logopaedic interventions already exist for children with cleft lip and/or palate, and to evaluate their structure, content and time of onset against the three stages of communicative development. Six databases (PubMed, Embase, Web of Science, APA PsycInfo, Cinahl and Scopus) were searched between inception and 31 March 2023 to identify published articles that reported early parent-implemented logopaedic interventions in children with cleft lip and/or palate, aged 0 to 3 years, clearly describing the strategies used to train parents. Two authors independently assessed the eligibility of the studies. Quality assessment was conducted using the Physiotherapy Evidence Database quality assessment tool, Single-Case Experimental Design tool and the National Institutes of Health pre-post-study tools. The structure and content of the interventions were analysed taking into account the needs and difficulties of both the parents and the child according to the three stages of communicative development. The systematic literature search identified four studies that met the inclusion criteria. Three of them had a Level of Evidence III and one study had a Level of Evidence IV. Strategies appropriate for Stage 1 of communicative development (parent-child relationship) are well represented in only one study, but the psychosocial needs of parents are currently not included in these programmes. However, research shows that parental emotional difficulties can adversely impact a child's communicative development. Strategies appropriate for Stage 2 (promoting social interactions) are better represented. However, strategies appropriate for Stage 3 (acquiring correct speech and language patterns) are most represented in all intervention programmes. Three out of four intervention programmes focus on Stage 3 (actual speech and language stimulation). Stage 1 is underrepresented and the psychosocial needs of parents are currently not included in existing intervention programmes. Further research is needed in close collaboration with psychologists to construct a comprehensive, longitudinal, developmentally appropriate intervention programme that equally represents the three stages of communicative development and considers the psychosocial needs of parents. What is already known on the subject Children with cleft lip and/or palate are at increased risk of speech and language problems from birth. Parents of these children often have emotional problems following their child's diagnosis. The effectiveness of early intervention to facilitate the child's speech and language development has already been proven. Early intervention is recommended for both parents and child, but little is known about early parent-implemented logopaedic interventions that also provide psychosocial support for parents. What this paper adds to existing knowledge This review has shown that existing early parent-implemented logopaedic interventions for children with cleft lip and/or palate focus mainly on facilitating responsive interactions and actual speech and language development (Stages 2 and 3 of communicative development). However, Stage 1, where the parent-child relationship develops, is currently not included, even though this stage is a prerequisite of subsequent stages. If parents are struggling with emotional problems (following their child's diagnosis) this can negatively impact their mental health, the parent-child relationship, attachment and their child's development. What are the potential or actual clinical implications of this work? A clinical implication of the findings in this review is that more attention should be paid to Stage 1 of communicative development in early parent-implemented logopaedic interventions. By working closely with the psychologist of the cleft (and craniofacial) team, any psychosocial needs of the parents can be included in the counselling. As a result, the parents and their child are seen and supported as a unit and the parent-child relationship can develop optimally.

Exploring Discourse Markers in Analyzing Gender Discrimination in Pakistani Drama Serials: A Corpus-Based Study

This research examines gender-related language patterns and discourse markers in Pakistani drama serials, focusing on the lexical choices made by male and female authors. Drawing on Lakoff's model, we conduct qualitative content analysis on the selected episodes to uncover gender dynamics and power imbalances. Results show that men use words like "absolutely", "decide", "go", "listen", and "stop" along with self-referential pronouns "I" and "he" more frequently. Women employ words like "doubt", "maybe", "very", "excuse me", "wonder", "we", and "might", as well as pronouns "she", and "we" more often. These findings reveal gender stereotypes in Pakistani drama serials, underscoring the need for more diverse and inclusive narratives challenging traditional roles. This research impacts the broader media landscape, emphasizing media's role in shaping societal perceptions and the importance of promoting gender equality in representations. Moreover, by analyzing the corpus of Pakistani drama serials authored by both genders, our methodology employs qualitative content analysis, contributing to a more equitable and inclusive media environment by dissecting gender-related language patterns and discourse markers in these serials.