Indonesian Online News Research Articles

Anti-vaccine COVID-19 narratives: A critical textual analysis of American and Indonesian online news

This study utilized Critical Discourse Analysis (CDA), grounded in Van Dijk’s framework to investigate how online news articles from American and Indonesian outlets portrayed anti-vaccine views related to COVID-19. Through systematic online searches, the research examined the data through the lenses of macrostructure, superstructure, and microstructure. Eight articles were selected for this study: four from American sources and four from Indonesian sources. The selected articles were published during specific periods: February 2022 to February 2023 for the U.S., and January 2021 to January 2022 for Indonesia. These articles, ranging from 500 to 1500 words, focused on ‘Anti-Vaccine COVID-19’ topics. The findings revealed distinct thematic differences. The macrostructure analysis showed that American articles highlighted distrust in specific vaccines and opposition to mandates, fostering skepticism, while Indonesian articles examined a wider range of cultural, religious, and personal factors influencing vaccine hesitancy, including anti-vaccine sentiments among expatriates and athletes. The superstructure analysis indicated that American reports emphasized controversy, whereas Indonesian reports explored motivations more deeply. Additionally, linguistic choices in the microstructure influenced portrayals, with American articles using dysphemism and active voice to emphasize danger, while Indonesian articles employed strong language to convey diverse perspectives. Despite its emphasis on textual analysis, this study offered significant insights into the sophisticated discussions of anti-vaccine sentiments in the context of the COVID-19 pandemic, as reflected in American and Indonesian online news.

Now Everybody is a Friend; How Native Advertising Influences Indonesian Journalistic Field

Native advertising is increasingly the primary practice of online news media to gain economic benefits. The nature of native advertising resembling news content is considered effective in influencing the public. The media study in Indonesia focused heavily on media democratization following the authoritarian media system for more than three decades. However, Indonesian media's economic competition is becoming more challenging, and media personnel are looking for new ways to earn money from native advertising. This study investigates the contemporary development of Indonesian journalism concerning the increased native advertising practice. It utilizes the field theory of Pierre Bourdieu's journalistic and journalism social responsibility approach. Data was collected through semi-structured interviews of fifteen personnel from three Indonesian online news media. The study showed that Indonesian online news media highly practice native advertising to earn revenue. The increasing practice of native advertising in Indonesia urges journalists to be involved in advertising content production and dissemination. There is resistance from media personnel in native advertising practice. However, native advertising has become irresistible because most media are in an economic crisis. Native advertising drives media to make friends with every prospective client, including the Government. The media has become less independent and less concerned about their social responsibility as a watchdog because the Government and state apparatus are their clients. The media develop new norms and traditions to accommodate native advertising. Keywords: Indonesian journalism, journalistic field, media social responsibility, native advertising, online news media.

Subjektų nuotaikų, susijusių su kuro kainų padidėjimu, analizė Indonezijos internetinėje žiniasklaidoje, naudojant „Netray“ stebėjimo įrankį

Introduction. This study aims to find the sentiment in Indonesian online news media surrounding the government’s decision to increase fuel prices on September 3rd, 2022. Additionally, this study investigates the discourses of situational crisis communication strategies (SCCT) around the critical entities in each positive and negative sentiment. Method. The researchers conduct entity sentiment analysis by collecting news data, identifying the primary entities, and analyzing the sentiment using the Netray monitoring tool. The researchers employ critical discourse analysis to find how the media portrays the SCCT strategies surrounding the key entities in positive and negative sentiment news. Analysis. The dominant strategies for positive sentiment are rebuild and bolstering strategies, whereas for negative sentiment, diminish strategies prevail. Results. At the outset, the discourses reflect the strategies positively encompassing the primary entity “price” as the representation of government policy. The subsequent discourses depict the strategies negatively, focusing on “Pertamina” as responsible for the policy’s operational aspects. Positive discourses for “Pertamina” are consistently distributed, adeptly constructing a positive image in the news. The discourses highlight the “fishermen” as a prominent group to convey both positive and negative sentiments. Conclusions. The government and PT Pertamina should monitor the narrative surrounding primary entities as it can enhance or harm their reputation.

Deteksi Clickbait pada Judul Berita Online Berbahasa Indonesia Menggunakan FastText

The rise of people accessing news portals has created intense competition between online media to get readers or visitors to maximize their revenue. This is what triggers the development of clickbait. Clickbait can reduce the quality of the news itself, and it also has the potential to be misinformation regarding to news contents as known as fake news. Therefore, it is necessary to detect news titles that contain clickbait. This study aims to obtain an optimal clickbait news title classification model using FastText. To get the optimal model can be done by cleaning the data and optimizing the model's hyperparameters. The model was trained using 9600 training data collected from Indonesian online news. The best model obtained in this study has performance with an accuracy of 77% and an F1-Score of 69%.

Portraying people with disability in Indonesian online news reports: a corpus-assisted discourse study

This article investigates representations of individuals with disabilities in Indonesian news media by drawing on corpus-assisted discourse study, social actor analysis, and news models of disability. The data comprises news articles which refer to disability published on popular Indonesian online news platforms over a ten-year period (2011–2020). The findings reveal prevalent portrayals of disabled people as passive beneficiaries and objects of representation, which implies a tendency to diminish their active involvement in society, supporting the social pathology model of disability. The progressive models, including the civil rights and consumer models, which highlight the active participation of disabled people, are limited. The article concludes by emphasising the need for a more balanced portrayal of disabled individuals by incorporating more news articles that underscore their agency. It also recommends the integration of diverse viewpoints from individuals with disabilities to attain a more comprehensive understanding of their challenges and perspectives.

Neo-Exoticism Discourses in Indonesian Online Media: Normalizing Cultural Tourism Regime amid Market Economy

This article explores the construction of neo-exoticism discourses in Indonesian online news media. Drawing on online news media research, we show how the transformation of ethnic cultures in Banyuwangi, East Java, Indonesia were positioned as the basis of various tourism events using the representation theory with the discursive constructionist approach. We focus on how some Indonesian online news media reported and positioned Banyuwangi Festival through quotations of government’s official’s statements and descriptions of cultural events that emphasize the transformation of ethnic cultures to meet national and global tourism markets. In doing so, Indonesian online news media actively produced, promoted, and distributed discourses of neo exoticism by emphasizing the beauty of certain of traditional rites, fashions, and arts in various carnivals and festivals. In addition, the media constructed neo-exoticism discourses as a formulas to popularize cultural and touristic events which proved to be beneficial, economically, for local people. However, we argue that broadcasting neo-exotic narratives also created online news media discursive subjects that normalize tourism regimes as managed by the cooperation of government and investors.

Two-Stage Sentiment Analysis on Indonesian Online News Using Lexicon-Based

The image of a supplier company is often associated with the well-known brand it supplies, and its reputation can be influenced by online news circulation. To maintain a positive image, it is crucial for the company to monitor and manage online news to rectify any false information. Failure to maintain a good company image can lead to customer order loss and even company shutdown. This paper aims to conduct a two-stage sentiment analysis on Indonesian news articles regarding unilateral layoffs by company XYZ. The first stage will analyze sentiment in the circulating news about the layoffs, while the second stage will assess sentiment after the company releases a press release to provide accurate information. The VADER lexicon-based method, utilizing the InSet and SentiStrength_ID Indonesian dictionaries, will be employed to analyze sentiment before and after the press release. This will enable us to compare sentiment and evaluate the effectiveness of the press release and the Indonesian dictionaries in analyzing sentiment in the news. The research findings indicate that the company's press release, aimed at correcting false information, had a positive impact by reducing negative sentiment and generating a more positive sentiment in the second stage. Moreover, the selection of the sentiment analysis dictionary also plays a critical role in determining the sentiment analysis results.

Administration Of Fear Through Symbols About Covid-19 In Indonesian Online News

The Covid-19 outbreak is a global fear. This fear is partly triggered by diverse media representation. Information containing representations of COVID-19 has the potential to reduce fear. However, in reality, representations through these symbols have the potential to be used by those in power to exercise their power, especially to get rid of their political opponents through representation. The representation of Habib Rizieq Shihab as a violator of health protocols is suspected to be an attempt by the authorities to eliminate his political opponents through fear of the COVID-19 outbreak in symbols spread in the media. This research wants to answer how the administration of fear in symbols regarding violations of the COVID-19 protocol by religious figures in the representation of Habib Rizieq Shihab in Indonesian online news. This research explores the concept and criticizes the concept of administration of fear, which is a construct from dromology-based theories by Paul Virilio. This research uses Barthes' textual semiotics method (Barthes' 5 codes), which analyzes several online news texts in Indonesia. From the results of the analysis, it is revealed that the media representation of Habib Rizieq Shihab is a form of administration of fear carried out by the authorities through symbols about COVID-19 in the media by constructing logistics of perception and environmental construction that represents Habib Rizieq based on fear of the epidemic and fear of Islam (Islamophobia).

Classification of Indonesian Online News Topics Using Text Mining

Online news is in great demand by many people along with the rapid development of information technology. It needs to develop a system that can automatically classify news according to news categories using text mining method. In this research, the methods used in the classification are K-Nearest Neighbor. News classification is done to classify news into 4 categories, namely automotive, technology, money and food. Each topic contains 300 news data. Before the classification began, data in the form of texts will first be carried out in the preprocessing stage and weighted using TF-IDF. Based on the evaluation using the confusion matrix with the division of the dataset into 960 training data and 240 test data, with the value of k=7 it obtained accuracy of 93.75%, Precision of 94.09%, Recall of 93.56%, and F1-Score of 93.71%.

A corpus-assisted discourse study on the construction of ‘obesity’ in Indonesian news media

Obesity is one of the health issues that is susceptible to discursive contestation and negotiation. The way it is portrayed in news media can affect public opinions and policymakers. This study aims to investigate the construction of ‘obesity’ in Indonesian online news media over a five-year period (2017-2021). The analysis was based on a corpus consisting of 1.418 news articles that mentioned the word ‘obesitas’ (obesity) by drawing upon corpus-assisted discourse study framework and legitimation theory. The findings showed that ‘obesity’ was predominantly constructed as a problem of personal responsibility, where individuals were viewed as solely responsible for minimising the risk of obesity and its associated health issues by losing or regulating their weight. This was frequently legitimised through expert authority by referring to medical experts’ interviews, or through impersonal authority by referring to scientific research. Meanwhile, the societal responsibility discourse which focused on the role of government and the food industry in creating conditions that led to obesity was very scarce. It is argued that this may reflect the presence of prevailing neoliberal ideology in Indonesian society. More balanced reporting, therefore, is recommended to lessen the stigmatisation of people with obesity and to recognise the variety of determinants of and solutions to obesity, reflecting its complexity more accurately.

Indonesian online news and digital culture: a media ecology perspective

The number of Internet users in Indonesia continues to increase, with more and more people using electronic gadgets connected to the Internet. It is also quite clear to see the phenomenon related to the emergence of online news in the digital realm. The researchers review and characterise Indonesian online news from the media ecology perspective. By doing library research, a literature review will be composed and used to describe online news and digital culture in Indonesia in media ecology. The result shows that the increasing use of the Internet has resulted in the rise of online news media, which seem to have replaced traditional media. In the context of Indonesia's digital culture, multimedia features in online news have the potential to attract readers' interest. At the same time, social media provides space for independent online news and community participation in the digital world to shape social, political, and cultural values.

Flagging clickbait in Indonesian online news websites using fine-tuned transformers

Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using hyperbolic and interesting words, sometimes unfinished sentences in a headline to purposefully tease the readers. Some Indonesian online news sites also joined the party of clickbait, which indirectly degrade other established news sites' credibility. A neural network with a pre-trained language model multilingual bidirectional encoder representations from transformers (BERT) that acted as an embedding layer is then combined with a 100 node-hidden layer and topped with a sigmoid classifier was trained to detect clickbait headlines. With a total of 6,632 headlines as a training dataset, the classifier performed remarkably well. Evaluated with 5-fold cross-validation, it has an accuracy score of 0.914, an F1-score of 0.914, a precision score of 0.916, and a receiver operating characteristic-area under curve (ROC-AUC) of 0.92. The usage of multilingual BERT in the Indonesian text classification task was tested and is possible to be enhanced further. Future possibilities, societal impact, and limitations of clickbait detection are discussed.

Hegemony and authority in health: The World Health Organization in Indonesian online news coverage on COVID-19 pandemic

The idea of hegemony and authority in language practice do exist in the development of linguistics and critical discourse studies. It attracts great attention at any time, moreover during time of crisis, such as the COVID-19 Pandemic. This study uncovers the realization of the hegemony and authority of the World Health Organization (WHO) in the coverage of the COVID-19 Pandemic by online Indonesian media. More specifically, the focus is on the representation of the hegemony and authority of the WHO and their linguistic realization in Indonesian news discourse. The study used the van Dijk’s framework of Critical Discourse Studies to examine five news reports from five Indonesian media related to the WHO stance and standards in the handling of the COVID-19 Pandemic. The study employed van Dijk’s macrostructure analysis to uncover the surface of the news; and Halliday’s transitivity as the microstructure analysis. Overall, the study found that the online Indonesian media coverage on COVID-19 Pandemic automatedly reinforced and maintained the power and the authority of the WHO. On the one hand, the fact is beneficial for the readers since they will receive coverage with reputable sources. On the other hand, there is a potential that the WHO’s power and authority can be misused to enhanced some parties’ interests, for instance in promoting one’s vaccine brand.

Challenging the elite-public divide; Representing former Indonesian first lady, Ani Yudhoyono in online news discourse

Women in politics invariably attract heightened levels of attention due to their marked difference to the masculine political norm. With the rise of social media and online news, political women can achieve visibility, but also experience even more intense scrutiny. Former first lady, Ani Yudhoyono became an iconic figure in the lead up to the 2014 Indonesian presidential election, through her association with her husband’s flailing presidency, and as a high-profile political woman involved in social media blunders. Using critical discourse analysis, specifically social actor analysis, this paper examines the discursive strategies engaged by the mainstream Indonesian online news media to malign Ani Yudhoyono, and draw a wedge between her and the Indonesian public, which in turn undermined her husband’s presidency. The analysis highlights the role of online news media discourse in shaping power relations and ideological groupings, as well as the role of first lady as a target of political contestation.

Ketika Fakta Bukanlah Kebenaran: Peran Media Daring dalam Amplifikasi Misinformasi Kesehatan di Era Pandemi Covid-19

During the Covid-19 epidemic, people must not only escape getting infected with the virus, but also avoid being targeted by hoaxes. In cyberspace, there are several topics of misinformation ranging from conspiracy theories to claims of finding anti-corona medications. Various studies have discovered that during a pandemic, social media is the mechanism through which health misinformation begins and spreads. However, there is a lack of data on misinformation disseminated by other media, particularly verified media. This study intends to bridge a research gap by investigating more into how claims of traditional medicine healing Covid-19 are publicized by Indonesian online news outlets. This study did a content analysis of 14 media articles from various online media about anti-corona herbal remedies affiliated to the 'Covid-19 Herbal Research Team for the Nation.' The study's findings suggest that in reporting on anti-corona medications, the majority of the media have abandoned the discipline of verification in the news production process. The media has amplified the lies of the 'Covid-19 Herbal Research Team for the Nation' through misleading headlines and reports that only feature sole claims.

When Journalism Produces Horror Story: The Case of the INSENTIF Podcast by Tirto.id

TThis research aims to investigate the production of horror contents in the INSENTIF podcast channel by Tirto.id, an Indonesian online news media. When journalistic media are involved in the production of ghost stories and mystery, it raises concerns about journalistic basic principles of providing verified information. To understand this phenomenon, we conducted a qualitative case study and interviewed three key informants from the INSENTIF production team. Using Giddens’ structuration theory as an analysis framework, we identify that the INSENTIF production crews still perceive their routine as part of journalistic works. In writing the script, they strive to rely on credible sources and actualise the discipline of verification although they admit that they insert some fictional horrific scenarios at the beginning of each episode as well. Thus, they mention a disclaimer, explaining that their journalism practice, specifically on horror episodes, is not the same as the regular Tirto.id’s standard journalism. This research argues that the agents, in this case, seem to follow and reproduce the existing structure, rather than resist it.

Monitoring Indonesian online news for COVID-19 event detection using deep learning

Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners.

BERT Implementation on News Sentiment Analysis and Analysis Benefits on Branding

The rapid development of information makes data processing easy and fast, especially in the business world, so many business brands have used the internet as a marketing medium for their operations. Now the business does not only depend on its operations; now, the opinion of the public media, especially on the news, has become an essential spotlight in today's business, especially against negative opinions that indirectly impact the image and product branding of the business, we need the proper means to help identifying and analyzing this kind of news. This study aims to identify and analyze sentiment with negative and positive indications on news titles from one of the sources of an Indonesian online news portal using the Bidirectional Representations from Transformers (BERT) sentiment analysis method, with the measurement of the confusion matrix metrics to measure and identify which headlines contains negative and positive indications. The sentiment analysis system offers identification and categorization with ease and immediately provide good results on identifying news. The results of this study, the sentiment model achieves an accuracy rate of 93% in identifying negative and positive news and F1-Score on negative identification rate of 92% and positive identification rate of 93%. The sentiment analysis system was built as effort to help analyzing against positive news indications or awful news as analysis benefits carried out to identifying alarming news indications towards branding.

Excluding the margins: Indonesian media’s framing of women and people with disability in the COVID-19 pandemic reporting

There are only a few pieces of research that show how online news covers the COVID-19 pandemic and its effect on different sections of society, including marginalized groups such as women and people with disabilities (PwD). During the COVID-19 pandemic, when marginalized people struggle for their livelihoods, inclusive journalism plays an important role. This research investigates the practice of inclusive journalism through the framing of women and PwD in some Indonesian online news media from March to June 2020. This research employed the content analysis method to analyse and study several news portals covering COVID-19 issues in local Yogyakarta and the national context. Framing data of the two marginalized groups showed the Indonesian news portals have portrayed women and PwD during the early phase of the pandemic. The dominant frames applied include government policies and solidarity for both women and PwD in national news, and solidarity and resilience for women and PwD in local news. It was found that all of the news on marginalized groups had a positive tone. However, because of limited access to marginalized groups and their supporters, government sources became dominant actors in determining news frames. Therefore, inclusive journalism has not been fully achieved.

Predictions of Indonesia Economic Phenomena Based on Online News Using Random Forest

Economic growth in the first quarter of 2021 based on YoY (Year on Year) is around -0.74%. This figure caused the Indonesian economy to recession after contracting four times since the second quarter of 2020. With positive and negative growth in the value of GDP for each category based on the business sector each quarter, can do future economic growth modelling. The prediction results can be used as an early warning for the government on factors that can maximize and factors that must improve. This study aims to predict the state of economic growth in the next quarter using Random Forest classification. Random Forest combines tree classification and bagging by resampling the data, which reduces the variance of the final model, which is for low variance overfitting. The data used in this study was scrapped from January 2021 to March 2021 on 5 Indonesian online news portals, namely Kompas, Antara, Okezone, Detik, and Bisnis. The independent variable is online news based on GDP category. The dependent variable results from data labelling on each news, up or down, carried out by the Directorate of Balance Sheet of BPS. Based on the calculations with cross-validation of 10, the modelling results obtained 96.51% accuracy, 97% precision, and 97% recall. The random forest method is good for predicting economic growth in the next quarter, namely the second quarter of 2021. Incorrectly predicted only three categories of GDP were: the construction category, the transportation and warehousing category, and the company service category