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This study rigorously explores the linguistic strategies employed by Pakistani universities for identity construction on social media platforms, focusing primarily on X (formerly known as Twitter). Utilizing a robust methodology of corpus-assisted discourse analysis, we scrutinized a substantial dataset comprising 2861 tweets. These were collected from the official X accounts of two leading Pakistani universities, National University of Sciences and Technology (NUST) and Lahore University of Management Sciences (LUMS), during the academic year 2021/2022. Our in-depth analysis uncovered distinctive linguistic patterns, particularly in the use of pronouns and specific multi-word key terms that each university employs to construct its identity. Intriguingly, the linguistic choices made by these institutions were found to significantly shape their digital identities, reflecting both individuality and broader cultural themes. The study augments existing research in the field by offering novel and context-specific insights into the dynamics of identity construction via social media. It underscores the pivotal role language plays in the organizational branding landscape. Furthermore, the findings from this research serve as a valuable resource for informing and refining communication strategies, not only for educational institutions but also for organizations at large that aim to construct and effectively manage their digital identities on social media platforms.

Big data pertains to extensive and intricate compilations of information that necessitate the implementation of proficient and cost-effective evaluation and analysis tools to derive insights and support decision making. The Fermatean fuzzy set theory possesses remarkable capability in capturing imprecision due to its capacity to accommodate complex and ambiguous problem descriptions. This paper presents the study of the concepts of dynamic ordered weighted aggregation operators in the context of Fermatean fuzzy environment. In numerous practical decision making scenarios, the term "dynamic" frequently denotes the capability of obtaining decision-relevant data at various time intervals. In this study, we introduce two novel aggregation operators: Fermatean fuzzy dynamic ordered weighted averaging and geometric operators. We investigate the attributes of these operators in detail, offering a comprehensive description of their salient features. We present a step-by-step mathematical algorithm for decision making scenarios in the context of proposed methodologies. In addition, we highlight the significance of these approaches by presenting the solution to the decision making problem and determining the most effective big data analytics platform for YouTube data analysis. Finally, we perform a thorough comparative analysis to assess the effectiveness of the suggested approaches in comparison to a variety of existing techniques. [ABSTRACT FROM AUTHOR]

To better understand QAnon's anti-immigration rhetoric, the study conducted a meta-discursive analysis of one of the group's active Telegram channels by drawing on Hyland's (2005) model of interaction. Specifically, engagement markers in their immigration-related discourse were analyzed to see how they contribute to endorsing the group's macro conspiratorial arguments. The results illustrate a complex rhetorical manipulation and nearly unanimous expression of hate toward immigrants, with the use of 'directives' and 'questions' being the most prevalent metadiscursive markers. The former suggests that the commenters were more assertive or commanding in their tone, while the latter located within rhetorical persuasion, aimed at stimulating negative attitude toward immigrants. Conversely, a low frequency of the 'shared knowledge' markers indicates a lack of interest to establish credibility and constructive dialogue with the audience. The study aimed to unpack the nature of anti-immigration extremist discourse on social media, and its potential to incite violence among public. [ABSTRACT FROM AUTHOR]