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This study aims to analyze the semantic shift in language within digital culture through the perspectives of linguistics and the philosophy of language. The focus of the study is directed toward the phenomenon of memes, emojis, abbreviations, and popular expressions that have rapidly developed on social media platforms and often deviate from their standard meanings. The research employs a descriptive qualitative method using content analysis combined with a philosophical literature review. Data were obtained from various digital platforms such as Instagram, TikTok, and Twitter/X, as well as relevant academic sources. The results indicate that semantic shifts in digital language occur through patterns of meaning expansion, metaphorization, polysemy, and linguistic innovation. From the standpoint of the philosophy of language, these findings align with Wittgenstein's meaning is use theory and Austin's speech act theory, both of which emphasize that meaning is determined by the practical use of language and speech acts within social contexts. Thus, the changes in meaning in digital culture are not merely linguistic but also reflect the social, cultural, and technological dynamics that shape modern communication patterns.

Bea Cukai has recently been in the public spotlight, especially regarding the supervision of goods from abroad. News and public responses regarding Bea Cukai's supervision create pros and cons, thus triggering a variety of responses from the public. This study aims to analyze the sentiment of Indonesian people towards the performance of Bea Cukai in monitoring goods from abroad by utilizing Twitter social media. In this research, the Support Vector Machine (SVM) algorithm is applied to classify public comments on Twitter into positive or negative sentiments. Through the crawling process carried out from June 1, 2023, to May 12, 2024, 9,051 entries of data were collected. The analysis results showed an accuracy of 93.87%, precision 94%, recall 93%, and F1-score 94%. These results show that the SVM method is effective in analyzing public sentiment, especially related to Bea Cukai's supervision.

Social media has become a new communication space that allows for the rapid and widespread dissemination of information, including on public issues. One of the platforms that is often used is social media X (formerly Twitter), where users can express their opinions and shape public perception. This study is entitled "The Influence of Hate Speech by Admin @keretacepatid on Patrick Kluivert (National Team Coach) on the Level of Followers' Perception on Social Media X" which aims to determine how hatred influences the formation of perceptions of social media users. This study uses a quantitative approach with a survey method and data collection techniques through online questionnaires to followers of the @keretacepatid account. The results of the study indicate that there is a significant influence of hatred on followers' perceptions. Some respondents showed a dominant attitude, namely agreeing with the negative narrative built by the account against Patrick Kluivert, while others were in a negotiating position, namely understanding the context of the speech but still considering Kluivert's professional background as a national team coach. This finding is the importance of ethical and responsible digital communication management in shaping public opinion in the era of social media.

The phenomenon of code-switching and code-mixing among university students has become increasingly prevalent alongside the rapid growth of social media. This article aims to analyze the challenges and implications of mixed language practices in students’ digital interactions, while reflecting on their significance for the development of the Indonesian language in the global era. The study applies a literature-based analytical approach, using thematic synthesis to identify patterns, motivations, and functions of code-switching across various social media platforms. Findings show that students’ code-switching practices vary depending on the digital ecology of each platform. On Instagram, code-mixing often reflects prestige and modern identity construction. Twitter or X emphasizes communicative efficiency and community solidarity, while TikTok highlights creativity, humor, and stance-taking. The main driving factors include identity expression, practicality, prestige, and online community norms. At a global level, this phenomenon resonates with the trend of digital translanguaging, which broadens the linguistic repertoire of young generations. The study concludes that code-switching in digital spaces presents a dual implication: a potential erosion of Indonesian language norms on one hand, and an opportunity for multilingual enrichment on the other. Therefore, adaptive language policies and digital-linguistic literacy strategies are necessary to safeguard the Indonesian language while embracing creative multilingual practices among students.

This research aims to analyze GoPay user sentiments on the X social media platform (formerly known as Twitter) using the Naive Bayes Classifier algorithm. Sentiment analysis was conducted to understand user perceptions and satisfaction levels towards GoPay digital payment services based on their shared comments and reviews. Data was collected through a tweet crawling process containing the keyword "GoPay" within a specific period. The research stages included data preprocessing (case folding, tokenizing, filtering, and stemming), sentiment labeling (positive, negative), word weighting using TF-IDF, and classification using the Naive Bayes algorithm. The results showed that from a total of 1,431 analyzed tweets, 797 data contained positive sentiments, and 643 data contained negative sentiments. With a classification accuracy rate reaching 82.94%. The most frequently positively commented factors included ease of use and offered promotions, while the main complaints were related to technical issues and customer service. This research provides insights for GoPay developers to improve services according to user feedback.  

Koenyaheun is a small business in Bandung that sells food that has been established since 2010. Initial observation results indicate a need for online marketing assistance for this business. During the community service activities, several things were found including more online customers than customers who ordered directly from this business, word of mouth marketing and also the use of social media. Observations and interviews with business owners and employees were conducted to obtain information in this community service activity. Several problems were also found such as logos, product photos, packaging, marketing. Solutions to the problems have been conveyed to the business owner so that it is hoped that this business will gain greater profits and better future prospects. It is recommended to market products on various social media platforms such as Tokopedia, TikTok, Instagram, Facebook, YouTube, Twitter, WhatsApp or cooperation with food delivery service applications such as GrabFood, GoFood, ShopeeFood, so that they can reach a wider range of consumers.

This study comprehensively examines the impact of social media on the formation and intensification of collective trauma in the Middle East through a digital meta-analytical approach synthesizing 47 empirical studies, encompassing a total of 31,842 participants, published between 2015 and 2024. The results reveal a strong and statistically significant correlation between the intensity of social media use and levels of collective trauma, with a correlation coefficient of r = 0.67 and a p-value of < 0.001, indicating a consistent and substantive relationship. Furthermore, regression analysis indicates that exposure to violent content through social media accounts for 43.2 percent of the variance in communal post-traumatic stress symptoms, affirming the role of digital media as a significant catalyst in amplifying collective psychological responses to conflict in the Middle East. Daily social media use exceeding five hours was found to significantly increase the risk of experiencing collective trauma by 2.8 times, with an odds ratio of 2.84 and a 95 percent confidence interval ranging from 2.31 to 3.49. Platforms such as Facebook and Twitter demonstrated a more substantial influence in widely disseminating traumatic experiences, with a beta coefficient of 0.58, compared to Instagram, which had a relatively lower influence with a beta value of 0.34, indicating that the structural and technological logic of each platform mediates the psychological transmission effect. Thematic analysis across studies revealed three primary mechanisms through which trauma is transmitted via social media: first, the amplification of traumatic narratives, accounting for 41.3 percent of identified patterns; second, the normalization of violence at 32.7 percent; and third, the reinforcement of collective identity based on shared traumatic experiences at 26.0 percent, thereby creating a digital ecosystem prone to the social accumulation of negative emotional states. These findings substantially expand the scope of prior research, such as that conducted by Atallah in 2017 and Nasciutti and Rahbari-Jawoko in 2021, which focused more narrowly on individual trauma, by highlighting a broader collective dimension and emphasizing the specific roles of various digital platforms in reinforcing these psychosocial dynamics. This study also identifies a novel pattern of both theoretical and practical significance, namely that algorithmic content recommendation contributes significantly to the formation of closed psychological echo chambers of trauma, intensifying exposure to traumatic content and deepening the affective impact of Middle Eastern conflict within digital spaces, with a significance level of p < 0.001. Accordingly, these findings underscore the urgent need for strategically designed and contextually grounded digital interventions to mitigate the burden of collective trauma in communities affected by protracted armed conflict in the Middle East.

The rapid growth of the Muslim population, both in Indonesia and globally, has driven an increasing need for products to support worship, including Muslim socks. PT Soka Cipta Niaga (PT SCN), a producer of Muslim socks under the brand "SOKA", faces the challenge of low brand awareness, where consumers are more familiar with the product as "wudu socks". This research aims to analyze effective marketing strategies for the "SOKA" brand. The research results show that social media-based promotional strategies, such as Twitter, Facebook, and LinkedIn, are able to reach potential consumers more widely. In addition, activities such as bazaars, Muslim seminars, and Collaboration with Hajj and Umrah organizers has proven effective in expanding the market. The momentum of Ramadan and the Hajj season is a strategic time to increase sales.

Perkembangan teknologi layanan internet di Indonesia memengaruhi berbagai sektor industri, termasuk sektor makanan di PT Sarimelati Kencana Tbk. Perusahaan awalnya menggunakan layanan IndiHome, namun menghadapi tantangan seperti tingginya biaya dan ketidakstabilan jaringan yang berdampak pada efisiensi operasional. Sebagai alternatif, layanan Starlink hadir dengan jaringan satelit yang menjangkau wilayah luas dan mendapat respons publik yang lebih positif. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap IndiHome dan Starlink menggunakan metode Naïve Bayes yang dievaluasi melalui K-Fold Cross Validation. Data dikumpulkan dari media sosial X (sebelumnya Twitter) dengan total 14.200 tweet (7100 tweet untuk IndiHome dan 7100 untuk Starlink), yang kemudian diproses melalui tahapan preprocessing seperti cleansing, tokenizing, stopword removal, dan stemming. Hasil pengujian menunjukkan bahwa metode Naïve Bayes memberikan akurasi terbaik pada data Starlink sebesar 83,74%, sedangkan pada IndiHome sebesar 73,31%. Analisis preferensi menunjukkan bahwa sentimen positif terhadap IndiHome sebesar 34%, sedangkan Starlink mencapai 47%.Temuan ini menunjukkan bahwa Starlink lebih unggul dalam persepsi publik dibandingkan IndiHome. Oleh karena itu, hasil penelitian ini dapat menjadi dasar pertimbangan strategis bagi PT Sarimelati Kencana Tbk. dalam memilih penyedia layanan internet yang lebih stabil, efisien, dan mendukung kelancaran operasional perusahaan.

The free lunch program is a goverment initiative aimed at addressing the issue of stunting in Indonesia. This program focuses on toddlers, school-age children and pregnant women. Various opinions have emerged from the public regarding this initiative, especially through sosial media platform X (Twitter) and news portals. In this research, sentiment analysis was conducted to understand public responses to the program, whether they are positive, neutral or negative. To evaluate the accuracy of the sentiment analysis perfomed, a deep learning approach was applied using the Long Short-Term Memory (LSTM) algorithm. The results show that public sentiment varies responses, on social media X tend to be negative, while those on news portals tend to be positive toward the free lunch program in Indonesia. Through LSTM-based testing, sentiment analysis on tweet data achieved an accuracy of 88.6%, with a precision of 84.6%, recall of 88.6% and an F1-Score of 86.3%. Meanwhile, sentiment analysis on news portal data reached an accuracy of 89%, with a precision of 81.7%, recall of 89% and an F1-Score of 85.1%.

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 development of social media has triggered a dynamic shift in the meaning of language used in everyday communication. This article aims to analyze the lexical meaning changes and expansions in the Indonesian language as used on social media platforms, particularly Twitter and Instagram. This study employs a descriptive semantic approach with content analysis methods applied to selected viral posts. The findings show that many words experience semantic shift, the formation of new meanings (neologisms), and altered connotations based on digital context. These results highlight the importance of semantic understanding in interpreting the evolving nature of language in the digital era.

The rapid development of financial technology (fintech), particularly digital wallet applications like OVO, has significantly transformed transaction patterns in society. However, issues such as server instability and unsatisfactory user experiences frequently emerge on social media platforms. This study aims to analyze user sentiments toward OVO on platform X (formerly Twitter) by comparing the performance of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Data were collected through web scraping from 1,000 Indonesian-language tweets containing the keyword "OVO." The research methodology included text preprocessing (data cleaning, tokenization, stopword removal), feature extraction using TF-IDF, and sentiment classification (positive, negative, neutral). Evaluation results demonstrated that SVM achieved the highest accuracy of 85.2%, while Naïve Bayes reached 78.5%. SVM also outperformed in precision (87%) and recall (83%) due to its ability to handle non-linear data. These findings provide actionable recommendations for OVO developers to enhance server stability and features based on user feedback. Additionally, this study serves as a reference for future sentiment analysis research employing algorithmic comparisons.

Football is a popular sport in the world and is enjoyed by people of all ages. The Indonesia U-16 national team played in the ASEAN CUP 2024 event in this field. Twitter users gave their support through #timnasday during the event. This provided many forms of support for the Indonesian national team which made it difficult to identify positive, neutral, and negative sentiments. This requires the use of lexicon-based textblob to perform automatic labeling. In the labeling results using textblob from a total of 1138 user tweet data resulted in positive sentiment values of 50.9% or 579 positive data, neutral 33.7% or 384 neutral data, and negative 15.4% or 175 negative data. In the test results using one of the machine learning from the naïve bayes classifier, namely gaussian naïve bayes with the division of test data and training data of 0.3 and 0.7, the accuracy value is 98.53%

Pemerintah merencanakan pemindahan ibu kota negara (IKN) ke Kalimantan Timur, yang telah disepakati oleh DPR RI pada awal 2022. Meskipun terdapat lebih dari enam pertimbangan utama, alasan ini belum cukup untuk menggerakkan IKN dari DKI Jakarta ke Kalimantan Timur. Penelitian ini mengevaluasi kebijakan publik terkait pemindahan IKN menggunakan pendekatan deskriptif kualitatif, menganalisis substansi dan implementasi kebijakan, serta meneliti tanggapan para pemangku kepentingan. Proses ini menyoroti pro dan kontra dalam persiapan, pembiayaan, dan dampak ekonomi serta sosial-ekologis dari pemindahan ini, sambil membandingkannya dengan pengalaman negara lain. Penelitian ini mengelompokkan faktor pendorong dan penghambat dalam pemindahan IKN, yang memiliki potensi strategis untuk transformasi ekonomi namun harus mempertimbangkan aspek sosiologis dan geografis. Media sosial, khususnya Twitter, berperan penting dalam membentuk opini publik mengenai perencanaan pemindahan IKN. Penelitian yang dilakukan dari Maret hingga Mei 2022 ini menggunakan metode deskriptif kualitatif dengan studi pustaka dan analisis data melalui aplikasi Nvivo 12 Plus, menemukan bahwa teori agenda setting mempengaruhi opini publik di Twitter terkait pemindahan IKN.

The rapid proliferation of misinformation across digital platforms has emerged as a critical challenge, undermining public trust, shaping opinion, and eroding the quality of democratic discourse. As reliance on digital platforms for information dissemination intensifies, the regulation of online content has become a focal issue for scholars, policymakers, and platform operators. This study examines the intersection of cyber law, platform governance, and content moderation, analyzing how platforms manage misinformation while balancing freedom of expression. Employing a normative and socio-legal approach, the research utilizes comparative methodology to assess national and international cyber law frameworks, alongside case studies of platforms such as Facebook, YouTube, and Twitter. Public policy analysis is conducted to evaluate the effectiveness of current governance models. Findings reveal significant variation in regulatory responses, with the European Union’s Digital Services Act offering a robust framework but facing enforcement challenges. Platforms, acting as non-state regulators, are criticized for inconsistent moderation and limited transparency. The study concludes that hybrid regulatory models combining state intervention with platform self-regulation hold promise for addressing misinformation effectively while safeguarding digital rights. This research contributes to ongoing debates on balancing free speech, accountability, and social control in the digital age.

This research aims to explore the role of social media in Indonesia's digital democracy, by highlighting the tension between its function as a tool of hegemony or a means of emancipation. The background of this study is the increasing use of social media by political actors and civil society in voicing, shaping or criticizing public narratives ahead of the 2024 elections. This study uses a critical qualitative approach with a descriptive study design, and applies the Critical Discourse Analysis method and netnographic observation of political content on three main platforms: Twitter, TikTok, and Instagram. Data was collected through literature studies, digital documentation, and observation of user interactions in digital political campaigns. The results show that the digital space is dominated by hegemonic actors such as political elites, partisan buzzers, and platform algorithms that reinforce certain narratives. However, there are also spaces of emancipation formed by digital communities and independent content creators who use social media as a means of political education and symbolic resistance. Counter-narratives that emerge tend to be temporary and are often limited by distribution and visibility controls. These findings have important implications for the development of more critical and participatory digital literacy policies. In addition, this study contributes to the enrichment of critical communication theory, by affirming the importance of viewing social media as a complex pedagogical and ideological field in contemporary democratic practice.

This study uses a qualitative method to explore how social media listening, combined with a machine learning approach, can be utilized to predict consumer trends in modern marketing strategies. In today’s digital era, social media serves as a rich data source for capturing consumer preferences, needs, and behaviors in real time. With machine learning algorithms such as natural language processing (NLP) and sentiment analysis, data from platforms like Twitter, Instagram, and TikTok can be processed to identify patterns that indicate market trends. This approach not only enables companies to respond quickly to consumer dynamics but also allows them to craft more targeted and data-driven marketing strategies. This study examines five major brands that implement social media listening as part of their digital strategy by observing consumer conversations, dominant emotions, and viral issues. The findings show that the integration of social media listening and machine learning can serve as an effective predictive tool in developing adaptive and contextual marketing campaigns.

This study investigates the complexity of contextual meaning in political texts posted by public figures on the social media platform X (formerly Twitter). Grounded in Halliday and Hasan’s theory of situational context and Searle’s speech act theory, the research analyzes how political utterances function through locutionary, illocutionary, and perlocutionary acts. The data, drawn from tweets during the 2024 Indonesian presidential election period, were collected using documentation and structured observation methods. The findings indicate that political texts on X are highly layered, combining explicit facts with implicit criticism, ideological stance, and persuasive strategies. Through representative, expressive, and directive speech acts, public figures construct meaning that influences public perception, evokes emotional responses, and fosters political alignment. This study contributes to the understanding of digital political discourse by highlighting how pragmatic strategies shape the interpretation of meaning in online political communication.