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This research study examines the dynamics of organizational communication and public communication in the recruitment process in State-Owned Enterprises (SOEs), with a particular focus on the tension between transparency efforts and the ongoing practice of entrusting positions. Despite the government's introduction of the Joint Recruitment of SOEs (RBB) program, which aims to digitize and standardize the selection of employees, a discrepancy emerges between the program's stated objectives and the perceptions of both the government and the public. The prevalence of complaints pertaining to the absence of information transparency, the lack of feedback mechanisms regarding unsuccessful outcomes, and the emergence of the term "insider" within the digital domain are indicative of deficiencies in two-way communication and a decline in public trust in the BUMN recruitment process. The present research employs a descriptive qualitative approach, utilizing a case study method and thematic analysis. The data presented herein were obtained through meticulous documentation studies of official documents from the FHCI, the Ministry of SOEs, and online media, as well as netnographic observations of public interactions on social media such as Instagram and Twitter. The analysis focused on public narratives, institutional communication patterns, and their impact on institutional reputation and legitimacy. The findings indicate that organizational communication within the RBB process remains hierarchical, failing to align with the ideal of reciprocal communication. The absence of information disclosure and the lack of a designated public forum for clarification engender significant discord between the assertions of institutional entities and the actual experiences of participants. This research recommends the implementation of measures to enhance the effectiveness of the aforementioned processes.

Mining activities in the Raja Ampat area have sparked various public reactions, both supportive and critical, particularly on social media platforms such as Twitter. This study aims to analyze public sentiment regarding the mining operations by employing two classification algorithms. A total of 500 tweets related to Raja Ampat were collected from the X platform, and after data cleaning, 168 were identified as positive sentiments and 303 as negative. Sentiment analysis was conducted using text mining techniques by comparing two algorithms: Support Vector Machine (SVM) and Naïve Bayes. To address the issue of data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The analysis results showed that SVM achieved an accuracy of 80%, outperforming Naïve Bayes, which reached only 68%. This indicates that SVM performed better in classifying sentiment. Additionally, the application of SMOTE effectively enhanced both algorithms’ abilities to detect positive sentiment, as reflected in the precision, recall, and F1-score metrics. For SVM, precision reached 85%, recall 80%, and F1-score 80%, while Naïve Bayes recorded a precision and recall of 69%, and an F1-score of 68%.

The digital transformation of journalism has reshaped how news is communicated and consumed. In an ecosystem dominated by visual content and rapid engagement, emoji have emerged as essential tools for conveying emotional tone, shaping narratives, and enhancing audience interaction. This phenomenon is referred to as emojournalism, the use of emoji within journalistic content to foster emotional resonance and public engagement. This study employs a qualitative approach with a constructivist paradigm and phenomenological strategy. Data were collected through in-depth interviews, netnographic observation, and content analysis of online news shared on platforms like Instagram and Twitter. The analysis utilized Roland Barthes’ semiotic framework, supported by triangulation techniques to ensure data validity. Emoji function as emotional signifiers in news content, influencing audience interpretation and increasing digital interaction. Specific emojis—such as

The plan to offer birth control to teenagers, outlined in Government Regulation (PP) No. 28 of 2024, has sparked different responses in the public, especially on social media sites like Twitter. This research intends to look into how people feel about this plan by using the Naïve Bayes Classifier technique. Information was gathered from Twitter by using data collection methods with the snscrape tool and the Python coding language. A total of 1,000 tweets related to the topic of the policy were gathered and went through initial processing steps like cleaning, breaking into words, changing cases, and removing common words. The Naïve Bayes Classifier technique was employed to sort the public's feelings into three groups: positive, negative, and neutral. The findings showed that half of the tweets (50%) had a negative view on the policy, while 35% had a positive outlook, and 15% were neutral. The accuracy of the method used was 78%, with a precision of 74%, a recall of 79%, and an F1-score of 76%. The findings from this research offer a summary of how the public feels about the birth control policy for teenagers, which can help the government assess and create policies that better meet the community's needs and worries. Additionally, this research highlights how well the Naïve Bayes Classifier method works for analyzing sentiments on social media, even though there are some challenges when it comes to understanding language subtleties like sarcasm.

spam is posting unsolicited messages or advertising on social media, particularly Twitter. These messages are normally designed to sell specific products and services or links. In this research, we developed a fuzzy control system to detect Arabic spam tweets based on deep learning with a large language model. Initially, we performed text cleaning and further transformed text into vectors with the help of AraGpt and AraBert. Subsequently, we employed a multi-layer perceptron network model in feature extraction of essential features. Finally, we adopted the fuzzy logic control system for classifying spam tweets using features filtered from deep networks. Employing the proposed Fuzzy logic control system provided nearly a 100% comparative to only utilizing the deep neural networks, which yielded an almost 99% throughput for both large language models Aragpt and Arabert, with a 100% F1 score for the Aragpt model and 99% for Arabert model respectively.