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Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

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.

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%

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.

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

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.

This article aims to describe the influence of social media on the formation and spread of slang among students at Muhammadiyah University, Maumere. Social media, such as Instagram, TikTok, and Twitter, are not only a means of interaction, but also a major catalyst in creating and disseminating new terms. This phenomenon reflects students’ linguistic creativity and is also a reflection of the dynamics of digital culture. In the context of Maumere Muhammadiyah University students, the use of slang is not just a trend, but also a tool to adapt to a dynamic social environment. New terms that are popular on social media are often adopted in daily conversations as symbols of modern and contemporary identity. However, this phenomenon also raises challenges, especially related to the preservation of formal language and traditional values in communication. This article discusses the characteristics of slang that emerges from social media, its role in forming social identity, and its impact on the use of formal language. In conclusion, social media plays a significant role in shaping student communication patterns, but efforts are needed to maintain a balance between language creativity and the use of standard language in formal contexts.

The study used a qualitative descriptive approach method. The purpose of this study was to determine the acquisition of slang lexicon by teenagers on the X application, providing insight into language in the modern era, the role of social media in language development, understanding slang in forming individual and community identities in social media, and understanding rapid language changes due to technological developments. Language variety is a variation of language that differs according to the context of use, including the relationship between the speaker and the listener. Slang, as a form of variation that continues to develop, is heavily influenced by social media, especially the X application (formerly Twitter), which is the main platform for teenagers. Through interactions on social media, users learn and absorb new vocabulary quickly, which shapes their social identity. This study used a qualitative approach to analyze the slang lexicon that appears in the application, finding categories such as abbreviations, foreign words, and phonological changes. Although slang enriches communication, there is a risk of reducing the use of formal language that needs to be balanced with language literacy education.

The growth of the internet has influenced the tourism industry because the internet makes it easier for individuals to obtain reviews about places to visit and because the internet is a tool used by tourist site managers to assess the quality of their offerings. The increase in the number of tourists of almost two million in just three years in West Sumba is proof of this influence. Social media is a tool that people use to interact with each other online; some people have multiple accounts on platforms such as Instagram, WhatsApp, Facebook, Telegram, Twitter, and so on. Tourists can receive recommendations for tourist attractions based on price and type of trip desired through a tourist attraction recommendation system that uses the KNN algorithm. Three factors were used in this research: activity, type of tourism, and type of price. An accuracy of 63.16% is found in the test results using the KNN algorithm and the Rapid Miner application with a K value of 5. The analysis results show that the K-Nearest Neighbor (K-NN) approach can be used as a guideline for recommending tourist destinations to visitors in West Sumba.

Twitter has become a social media that is widely used to communicate, communicate information, obtain information, and is often used as a place to discuss issues such as the Israeli-Palestinian conflict. The @erlanishere account is one of the humanitarian activists who regularly posts about the Palestinian situation. The stories presented not only provide information, but also arouse the emotions of the public and raise awareness and solidarity with the suffering of the Palestinian people. So this account is appointed as the Commander of the Netizen Julid Anti-Israeli Special Operations Unit. Only by using the tag, @erlanishere can coordinate netizens to voice the Israeli conflict in Gaza. In this way, the information can spread to netizens throughout Indonesia. The research into @erlanishere's account aims to understand the narrative of Israeli atrocities in Palestine and their impact on public perceptions. Using the critical discourse analysis method by Teun A.Van Dijk, the results of the research on this @erlanishere account were, able to mobilize public support and sympathy for Palestine, through the use of emotional language, visualizing violence, and presenting critical facts about Israeli actions.    

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.

Innovation in Hajj and Umrah management through the use of social media holds significant potential to enhance service efficiency and quality. Social media platforms like Facebook, Instagram, Twitter, and WhatsApp can be used to quickly and accurately provide education and information to pilgrims. Organizers can deliver guidelines, health tips, and real-time updates about conditions in the holy land, and respond to pilgrims' questions and complaints more efficiently. This research explores how social media can be integrated into Hajj and Umrah management to improve service quality and risk management, focusing on the role of social media in education, responsiveness, and risk management. The findings indicate that social media is effective in disseminating information and enhancing interaction between organizers and pilgrims, though challenges exist in technology access gaps and the risk of spreading inaccurate information.    

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%.       

In the rapidly developing digital era, social media such as Twitter has become part of everyday life and facilitates the rapid dissemination of information, including information about criminals. This research aims to analyze public sentiment towards information about criminals spread on Twitter using the Naive Bayes algorithm. This algorithm was chosen because of its simplicity and effectiveness in text classification. Data was collected through a crawling process from Twitter, followed by a preprocessing stage to remove noise. The research results show that public sentiment towards information about criminals on Twitter is divided into three categories: positive, neutral and negative. After classification, it was found that neutral sentiment increased significantly to 63.4%, while positive and negative sentiment decreased to 10.5% and 26.1%. These findings indicate that people tend to be more careful in reacting to sensitive information. This research provides important insights for related parties in managing information about criminals on social media and can be a reference for developing further policies and strategies.

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.

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.

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.

The government that is running at this time is also not spared from public comments on Twitter, especially regarding the increase in subsidized fuel. There are at least 4 impacts felt by the community when subsidized fuel prices increase, namely a decrease in people's purchasing power, an increase in basic prices, an increase in the unemployment rate and an increase in the poverty rate. This study aims to implement the Naïve Bayes Classifier and KNN algorithms in classifying a tweet of an increase in subsidized fuel so that it can be identified as belonging to a class with positive or negative sentiments. The data used in this research are 560 tweets. The data is divided into 2, namely 500 training data from tweet data and 60 test data from tweet data stored in xlsx format. The results of the accuracy with the Naïve Bayes Classifier algorithm is 85% while the KN algorithm is 86.8% so it can be concluded that the KNN method is better than the Naïve Bayes Classifier method in classifying tweets of increases in subsidized fuel. Keywords: Subsidized BBM, Naive Bayes, KNN

The widespread use of social media among the public has created a new need, namely the urgency to create space for conducting business activities, causing the platform to turn into a place for communication, interaction, as well as a trading space. These changes bring various impacts, one of which is the formation of new types of crime in cyberspace. Fraud in electronic transactions via social media (Twitter) is a crime that targets the internet, computers and related technology as its target. Based on the position of the case, the fraud that occurs can be classified as a crime of computer-related fraud or a crime to gain personal gain and/or harm others. The handling of fraud cases can be carried out using the legal basis contained in Law Number 19 of 2016 concerning Information and Electronic Transactions, namely Article 28 paragraph (1) in conjunction with Article 45 paragraph (2). The use of these two articles is based on the principle of lex specialist derogat legi generali. In addition, if examined using a victimological point of view, victims of fraud cases that occur are included in the category of participating victims where the tendency of victims to be unaware of their attitudes/behaviors in certain circumstances is a reason for someone to act. commit crimes against them. The research method used to answer these problems is normative legal research with a case study approach in the form of legal behavior products.