Publication Search

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.