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The development of digital technology has significantly changed the way students interact and present themselves in social life, especially through social media such as Instagram, TikTok, and Twitter. One of the emerging social phenomena is flexing, which refers to the behavior of showing off lifestyle, achievements, or ownership to gain attention and social recognition. This study aims to analyze how Sociology students of Sriwijaya University interpret flexing on social media and whether flexing is more dominant as a lifestyle or as a form of social pressure. This research uses a qualitative method with a literature review approach by examining various scientific articles, journals, and previous studies related to flexing, self-presentation, symbolic consumption, social validation, and Fear of Missing Out (FoMO). The results show that flexing is not only a form of self-presentation and symbolic consumption, but also a response to social pressure in the digital environment.

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.

The advancement of information and communication technology has significantly altered the ways adolescents interact and establish social relationships, particularly through social media. This study aims to examine the impact of human relations on adolescents' interactions and behaviors on social media platforms, taking into account psychological, sociological, and cultural dimensions. Employing a qualitative approach with a literature review method, this research explores interaction patterns among adolescents on platforms such as Instagram, TikTok, and Twitter. The findings reveal that social media creates virtual spaces that enhance social networks but also pose risks such as reduced quality of face-to-face interactions, social anxiety, and mental health issues. This study emphasizes how the intense use of social media reshapes traditional communication patterns and influences adolescents' behavior and self-concept. Based on these findings, strategic recommendations are proposed for parents, educators, and policymakers to guide healthy social media use, balance its benefits and drawbacks, and foster the development of more meaningful social relationships among adolescents.

This paper concerns the analysis of New Word Found on Twitter Using the Process of Word-Formation. The aim of this study is to analyze word-formation on words that have just appeared on social media, especially Twitter. The research used in this analysis is a qualitative research method. Source of research data in the study came from the Twitter app found in captions, comment fields, and retweet quotes. When collecting the data, the researchers used several steps, first searched the data in the Twitter app from captions, comment fields, and retweet quotes. Then, collect data containing word formation and classify the data by type. The data is collected and researchers analyze the type and process of word-formation. The results of this study indicate that there are 5 types of word-formation processes found on Twitter. They were derivation, abbreviation, blending, acronyms, and clipping. Of these types, abbreviations were the most common word-formation on Twitter.

Pada tahun 2024, Indonesia akan menyelenggarakan pemilihan umum serentak yang meliputi pemilihan presiden dan pemilihan wakil rakyat di seluruh Indonesia. Masyarakat menanggapi kejadian ini dengan perasaan campur aduk, membagikan pemikirannya di situs media sosial seperti Twitter. Penelitian analisis sentimen calon presiden Indonesia tahun 2024 dilakukan terkait peristiwa ini. Sebanyak 1458 tweet digunakan dalam penelitian ini. Dengan 40,31% responden menyatakan sikap positif dan 43,46% menyatakan sentimen negatif, temuan analisis menunjukkan keseimbangan antara kedua sentimen tersebut. Menggunakan frasa "calon presiden," program Python di situs web Google Colab mengambil data twitter. Pendekatan K-Nearest Neighbor digunakan dalam proses klasifikasi. Selain itu data latih dibagi 6 : 4. 40% data uji dan 60% data latih. Nilai evaluasi yang diperoleh dari pengujian model dengan teknik K-Nearest Neighbor adalah akurasi sebesar 90,95%, presisi sebesar 62,17%, recall sebesar 62,33%, dan F-Measure sebesar 61,87%.

According to the Jakarta Air Quality Index (AQI US) 12 July 2023, 200 indicates unhealthy air quality with an index value between 151 and 200. This figure even shows that Jakarta is currently the second most polluted city in Southeast Asia. (CNN Indonesia., 2023). This incident gave rise to responses from the public which were expressed via social media Twitter. From this incident, sentiment analysis was carried out regarding Jakarta's air quality. The amount of data used for this research was 500 tweet data. The results of the positive and negative sentiment analysis show that negative sentiment appears more frequently than positive sentiment with a percentage of 7% positive sentiment and 14% negative sentiment, by using the Rstudio application. This method uses the naïve Bayes classifier. Data division in the dataset with training data 1:499 and test data 1:476. It was found that the results of the Accuracy, Precision, Recall, and F1-Score values were Accuracy 87.50%, Precision 87.50 Recall 93.33%, and F1-Score 82.35%.       

Twitter is a social media that provides information for its users. Interactions on Twitter may increase the amount of data from its users. Business development cannot be separated from product competition between business actors. One way for companies to win the competition is to know the purchasing power of customers. A product's selling power can be known one way or another through user interaction on Twitter. User-generated content (UGC) results from data on Twitter. UGC data can be used to determine the product supply scheme taken by laptop products in this study. The method used to analyze the interaction is Social Network Analysis (SNA) by comparing the properties of the network. Social network modeling on UGC data was carried out on three laptops: Lenovo v14, Asus ZenBook S, and Acer Swift 3. From the graph visualization, it was found that Acer excels in two properties, namely "Size" and "Average Degree." Meanwhile, the Asus ZenBook S dominates the "Modularity" property.

One of the digital media frequently used by business practitioners for promotion is Instagram. Instagram is utilized as a promotional medium due to its more appealing interface compared to Facebook or Twitter. Instagram is an application for capturing, editing, and sharing photos within the Instagram user community. The research was conducted using a descriptive quantitative approach. Research data was collected through a questionnaire distributed randomly to 99 respondents who were followers of the "Fanaya Catering" social media account. The research results indicate that the use of social media has a positive impact on increasing the sales revenue of catering products.

The Halal Product Assurance Agency (BPJPH) is an agency under the auspices of the Ministry of Religion with the task of ensuring the halalness of products in Indonesia. BPJPH has become a public concern after establishing the new halal logo. On February 10, 2022 the new halal logo was ratified by the Head of BPJPH, Muhammad Aqil Irham. This has become a topic of public discussion either directly or through social media, one of which is social media twitter. The number of opinion tweets about the change of the halal logo can be used as a data source to obtain information about public opinion on the change of the halal logo through sentiment analysis. Sentiment analysis can be done by machine learning approach, one of these is the SVM algorithm . In this research, oversampling and undersampling are applied to handle data that has an unbalanced sentiment class. The results showed that the Support Vector Machine (SVM) model using oversampling training data got the highest accuracy, recall, precision, and f1-score, namely 71% accuracy, 67% precision, 61% recall, and 61% f1-score while training using undersampling training data has the lowest performance, namely getting 56% accuracy, 51% precision, 57% recall, and 52% f1-score.

In today's digital era, we often hear or see the use of slang words, both among teenagers and adults. We often find slang words on social media users, one of which is Twitter. The rapid development of communication technology in this digital era, so that social media is very easy to access, in general social media such as twitter most of its users are teenagers. In the end, it has a major influence on how to interact in social life both in cyberspace and the real world. Some experts state that the characteristics of the generation in the current era are different from the previous generation, one of the reasons is because the current generation was born and grew up in a modern environment and surrounded by digital technology. This study aims to identify the various forms of slang found in captions and comment columns on social media twitter.  The data in this study is in the form of slang words obtained by applying observation methods with screenshot and record techniques. The data sources used were @collegemenfess Twitter social media accounts and @kdrama_menfess. The results in this study are various forms of slang words in the form of (1) mager, (2) bucin, (3) baper, (4) gabut, (5) bm, (6) gaje, (7) gercep, (8) mode. The analysis showed mixed results, but most of the participants stated that they got slang words to interact with in social circles and obtained those slang words from memes and other social media. 

Penelitian ini membahas kajian sosilinguistik, tepatnya analisis penggunaan campur kode pada laman Twitter Collegemenfess. Laman Twitter ini merupakan salah satu auto base dengan pengikut terbanyak di Indonesia. Penelitian dilakukan dengan metode deskriptif kualitatif. Setelah dilakukan penelitian, ditemukan penggunaan campur kode pada cuitan di Collegemenfess. Data yang ditemukan berupa sepuluh campur kode ke luar dan lima campur kode ke dalam. Penggunaan campur kode yang ditemukan berupa kata dasar, frasa, istilah dan kalimat. Penggunaan campur kode ke luar yang ditemukan yakni pada bahasa Inggris, lalu penggunaan campur kode ke dalam ditemukan pada bahasa Jawa, Sunda, dan Medan. Dapat disimpulkan, pengguna auto base Collegemenfess dalam berinteraksi di Twitter sering kali menggunakan campur kode untuk menyampaikan maksudnya.        

Organisasi Kesehatan Dunia (WHO) secara resmi menyebut virus Covid-19 sebagai pandemi global, oleh karena itu semua negara di dunia berusaha meminimalkan dampak yang ditimbulkan oleh virus tersebut, yaitu dengan mengembangkan vaksin. Dalam konteks pandemi Covid-19, pemerintah Indonesia juga meminta dan mendorong masyarakat untuk turut serta mendukung vaksinasi, namun upaya tersebut sebenarnya memiliki kelebihan dan kekurangan, sehingga banyak masyarakat yang mengutarakan pendapatnya di jejaring sosial salah satunya Twitter. Penelitian ini bertujuan untuk mengetahui hasil penerapan analisis sentimen dan mengukur performansi algoritma Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM) terhadap data vaksin Covid-19 dengan cara mengklasifikasikan data tersebut ke dalam kelas positif dan negatif. Data tweet yang didapat kemudian dilakukan text preprocessing untuk mengoptimalkan pengolahan data. Terdapat 4 tahapan text preprocessing antara lain Case Folding, Tokenizing, Filtering, dan Stemming. Penelitian ini mengkaji kinerja Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM) dengan menambahkan teknik TF-IDF (Term Frequency-Inverse Document Frequency) yang bertujuan untuk memberikan bobot pada hubungan kata (term) sebuah dokumen. Kemudian melakukan splitting data yaitu membagi data training 80% dan data testing 20% dengan harapan mendapatkan model dengan performansi terbaik dan yang terakhir melakukan visualisasi data tweet dengan menggunakan Word Cloud agar bisa menarik sebuah kesimpulan. Hasil klasifikasi data tweet vaksin Covid-19 menggunakan algoritma Naïve Bayes Classifier mendapatkan nilai accuracy sebesar 81%, precision sebesar 80%, recall sebesar 99%, dan f1-score sebesar 89%, Sedangkan untuk algoritma Support Vector Machine mendapatkan nilai accuracy sebesar 87%, precision sebesar 88%, recall sebesar 96%, dan f1-score sebesar 92%.