News Search Research Articles

Journalist's Strategy Tras.Id in Search of News in Bengkulu Province

Every day journalists are required to be able to find and make news. With these demands, journalists are encouraged to be able to fulfill them in various ways in accordance with the rules and regulations. This paper shows the strategies of Tras.id journalists in finding news in the Bengkulu Region. Purposive sampling, an informant interview technique, was used in this qualitative research. Planning, production, and editing are the three main focuses of this research. This study uses data collection methods with observation, interviews, and documentation to collect information from six people, namely the public, editor-in-chief, editors, and journalists. Starting with daily editorial meetings and the availability of supporting equipment such as cars, cell phones, agendas/notebooks, and press cards, this research resulted in a news search strategy. The study's recommendations focus on two areas: the lack of case studies or specific topics on the news published by Tras.id media and the need to initiate some major investigations into the escalation of political issues. The next advice that needs to be given is to pay close attention to each sentence on the news page before it is published.

Editorial Management Sindonews.com in Determining News Worth Publishing

The media are currently faced with demands to increase selectivity and creativity in the presentation of journalistic products, especially in choosing news that is worthy of airing in order to meet the desires, interests and demands of the audience in the midst of increasingly fierce media competition. This research aims to examine editorial management at SINDOnews.com in determining the right news, including the process of planning, organizing, implementing, and supervising as well as factors that support and hinder it. This research uses a qualitative approach to understand the phenomenon holistically through data collection in the form of written words and language obtained from interviews with main and supporting informants and documentation. The results show that news search planning is carried out through editorial meetings, organizing through coordination in WhatsApp groups, implementation starts from news material distribution, writing, editing in verification groups, and supervision through quality and quantity evaluations. Supporting factors are the completeness of equipment and qualified human resources, while the inhibiting factor is the difficulty in obtaining confirmation information from sources. The implications of this research show that editorial management runs in accordance with POAC theory and existing journalistic theory, with supporting factors such as adequate equipment and human resources and challenges in the form of obstacles in obtaining confirmation from the main source, which sometimes requires alternative sources.

Examining exposure diversity on Google News in Australia

This article presents results from a sub-project established under thebanner of the ADM+S Australian Search Experience ‘citizen science’research project. The aim was to characterise news recommendationspresented to Australians through Google News. The project investigatedthe search results for 20 keywords related to Australian politics and globalissues, including names of politicians, political parties, global sportingevents and various COVID-related terms. Keywords were organised intotwo keyword sets: ‘Australian Politics’ and ‘Global Issues’. Theresearchers found that Google News search results were not personalisedwith respect to demographics or geography. A small subset of domainsdominated the first page of results. While several major Australian newsdomains were regularly observed for the ‘Australian Politics’ keyword set,the ‘Global Issues’ category featured more news domains from the UnitedStates and the United Kingdom. These findings help to better understandhow search engines present news content to ordinary internet users.

Neo-institutionalism perspective on local governance in Lombok: Reinstitutionalisation of Tuan Guru in the Indonesian decentralization era

This article delves into an examination and analysis of leadership models within local government institutions in Indonesia, employing the conceptual framework of new institutionalism. We contend that informal local institutions within communities not only influence the behavior and identity of leaders as actors but, within the context of decentralization, have also undergone a process of reinstitutionalization regarding roles and functions, employing distinct patterns of appropriation. Employing an interpretive approach, this article focuses on phenomena within the management of local governance in the West Nusa Tenggara province. Data were collected through in-depth interviews, literature studies on local history, and online news searches. Through a case study of local governance in West Nusa Tenggara province, particularly Lombok, the article reveals that the Tuan Guru, an informal local institution in Lombok society, has experienced reinstitutionalization through vertical and horizontal appropriation. The conclusion drawn is that decentralization has created opportunities for informal institutions to re-establish their roles within formal governance through appropriation patterns.

The Relationship Between News Coverage of COVID-19 Misinformation and Online Search Behavior

ABSTRACT The spread of health misinformation poses a threat to public health as it can influence individuals’ health beliefs and, potentially, behaviors, and their support for public health policies. The mainstream news media have the potential to inadvertently increase the salience of misinformation through their reporting. This study explores the agenda-setting effects of mainstream news media coverage on public salience of public health misinformation during the COVID-19 pandemic. We analyzed the association between news media coverage and public salience – as measured by Google Trends relative search volume – of three different pieces of COVID-19 misinformation: (1) hydroxychloroquine is a safe and effective treatment for COVID-19; (2) the COVID-19 vaccines cause infertility; and (3) ivermectin is a safe and effective treatment for COVID-19. We plotted news coverage of each piece of misinformation against Google Trends relative search volume over time. Autoregressive integrated moving average (ARIMA) models were used to account for autocorrelation and analyze the relationship between the two time series. News media coverage volume was a significant predictor of same-day relative search volume for hydroxychloroquine and ivermectin. The relationship between news media coverage and searches for COVID-19 vaccines and infertility was not significant. These findings indicate mainstream news media coverage may contribute to the public salience of misinformation. Mainstream media outlets should contextualize their reporting on misinformation with verification from scientific consensus.

Naming Buildings in Universities Based on the Names of National Figures to Ignite the Fighting Spirit of the Academic Community

The naming of the building at Universitas “X” as one of the leading universities in Indonesia is not fully known. This study investigates the reasons for naming places using the names of national figures, the language unit structure of place names, and the names of places that have been given special names. This qualitative research data were collected using a documentation study, a search for news on the internet with reliable sources of information, interviews with informants who have information about the building names at Universitas “X”, and direct observations. The data were analyzed qualitatively according to those presented by Miles dan Huberman: data reduction, display, and conclusion drawing. The research results in naming buildings based on the names of national figures intended so that the academic community can emulate national figures’ spirit, achievements, and fighting power. The phrase structure of building names based on the rules for English language toponymy is a combination of common nouns and proper nouns, such as Gedung Prof. Mr. H. Mohammad Yamin, Gedung Prof. Dr. Ace Partadiredja, and Gedung K.H. A. Wahid Hasyim. As a follow-up to this research, it can be investigated, among others, how to name streets or name rooms on campus with this Islamic spirit. Keywords: toponymy, place name, building name, universitas “X”

Tree failure - A natural phenomenon with forensic implications.

Tree failure, or summer branch drop, refers to situations where large branches or whole trees may unexpectedly drop on unsuspecting victims. On occasion, this involves vehicles impacted by falling overhanging trees or branches. To study this phenomenon further review of the Forensic Science SA, Adelaide, Australia, database and an internet news search were undertaken over a 20 year period from March 2004 to February 2024 for all cases where unexpected accidental deaths in South Australia had been caused by falling branches or trees. There were six cases (age range 12-63 years; average 39 years; M:F 1:2). Three deaths occurred when branches dropped onto vehicles being driven along roads, 2 occurred when entire trees dropped onto the victims (one in windy weather) and 1 was due to a branch drop. Three tree species were identified as a grey box gum tree (Eucalyptus microcarpa), a lemon-scented gum tree (Corymbia citriodora) and a Cottonwood tree (Populus deltoides). The remainder were variants of eucalyptus (gum) trees. Deaths due to tree failures are uncommon and may involve a variety of different tree species with these events not necessarily occurring during high winds or storms. The major findings at autopsy are blunt force injuries.

Fake News Detection System Using Machine Learning and Deep Learning

The rise of fake news in today's age of mass media poses a serious threat to the credibility of the media and public discourse [1]. This project focuses on developing effective and robust solutions to detect fake news by integrating multiple machine learning algorithms. This project uses a variety of techniques, including natural language processing (NLP), clustering, classification algorithms and deep learning, to identify the content of patterns that indicate error messages. The project first explores NLP algorithms to extract content from news media content. These features allow the system to understand and classify the language used, helping to identify language patterns associated with fake news. Additionally, emotional analysis plays an important role in visualizing the tone and content of the text, further increasing the accuracy of the findings. The integration method is used to combine various machine learning models. By combining the performance of various algorithms, the system aims to overcome individual limitations and provide a more comprehensive and effective evaluation of media content. This approach helps increase the power of fake news detection and adapts to different fraud strategies. The project also includes deep learning algorithms, especially neural networks, to capture relationships in data. The use of deep learning ensures that the system can recognize nuances in the language and clarifies the complex information of the error message. Continuous updating and reworking of machine learning models is an important part of the process, allowing the system to constantly adapt to fraud strategies [2]. The aim of fake news detection system is to create a well-informed, weak search for fake news that can help create greater awareness and public approval. Key Words: Fake news detection, Machine learning techniques, Advanced algorithms, Classification algorithms, Deep learning, Feature extraction, Model training, Natural language processing

Automating the search and selection of news in municipal registries: Experiences from a Swedish participatory action research project

This article describes a Swedish case study with a focus on how to enhance journalists’ professional autonomy, local news coverage and local democracy with the help of an automated search tool. The aim was to involve journalists in the development project using the theory of participatory action research and tailor a search tool to their specific needs to search for news stories in documents from municipalities. We conclude that user-centred design is a viable methodology when developing a new tool, but not necessarily when it comes to implementing the tool in journalists’ everyday practice, as the implementation is dependent on other factors connected to management and organizational structures. It became clear that the journalists were optimistic about the automated search tool as long as it made their work more efficient in terms of time and money and offered better possibilities for independent news discovery. We also noticed that some of the journalists’ professional knowledge about a certain geographical area or municipality became subordinate to the knowledge of how to best use available search tools.

Drowning in a Sea of News: The Role of Doomscrolling, News Overload, and News Failure in the Relationship Between News Search on Social Media and News Avoidance in Natural Disaster Situations

ABSTRACT Türkiye is a region prone to earthquakes, and its fault structures generate destructive earthquakes on a regular basis. In this sense, a second devastating earthquake occurred in February 2023, and a great deal of information and news about it was distributed, particularly on social media. In cases of natural disasters such as earthquakes, this study examines the effect of intense and negative news flow on news avoidance as a consequence of people’s search for information about the event on social media. In this regard, a survey was conducted with the participation of 398 social media users in Istanbul, the most populous and earthquake-prone city in Türkiye, during the months of March and April. As a result of the study, it has been determined that there is a direct effect between information seeking on social media and news avoidance, whereas there is an indirect mediation effect between doomscrolling, news overload, and news fatigue. In addition, as the age and educational level of the participants increase, while being affected by doomscrolling, news overload, news fatigue, and news avoidance variables decrease, there is no significant difference in the category of information seeking on social media.

Unpacking the effects of scams in marketplace lending: investor confidence and attention

ABSTRACT Both transformational and problematic platforms, corresponding to moderate and severe scam conditions, are considered abnormal in Chinese marketplace lending. This paper examines the impact of scams in marketplace lending on investor confidence and attention. We find that as the abnormal platform ratio increases, lenders are more likely to lose confidence and withdraw investments. They also become more concerned about the risks associated with marketplace lending and the entire industry. These effects are primarily driven by severe scam conditions. Additionally, the effects of severe scam conditions on investment withdrawals and industry-related news searches are contemporaneous, while there is a lag in the effects on searching for risk-related news. Furthermore, we also find that regulatory policies enhance individual sensitivity to severe scams, and severe scams result in greater capital outflows in first-tier regions and more significant levels of panic in other regions.

Distributed Discovery of News and Perceived Misinformation Exposure: A Cross-Continent Application of the Resilience to Online Disinformation Framework

Perceived misinformation exposure (PME) among citizens is a global phenomenon and a normative concern because it can lead to reduced trust and faith in democratic institutions, actors, and processes. Using secondary data from multiple sources, this study analyzed individuals’ online news habits across forty-six countries in six continents (North America, South America, Europe, Asia, Africa, and Australia: N = 91,061) and country-level factors from the resilience to online disinformation framework that shape the relationship between distributed discovery of news and PME. Multilevel analyses showed that increased incidental news exposure and searching for news online at the individual level and news sharing on social media at the country level increased PME while aggregate media trust reduced PME. Cross-level interactions also indicated that higher levels of public service media in a country attenuated the relationship between online news search and PME, exhibiting what we call soft resilience. This study demonstrates the theoretical utility of the resilience to disinformation framework and certain country-level factors that can affect the individual-level dynamics of news consumption and PME.

Development of Website BEM FEBTD Universitas Nahdlatul Ulama Surabaya

Currently BEM FEBTD UNUSA does not yet have a web-based information system, and only relies on delivering information and documentation through social media, namely Instagram. The drawback is that the management cannot manage the schedule so that information on the progress of each work program is obtained. This causes limited information that can be known by the academic community about work programs from year to year. Therefore, media is needed that can manage the work program of BEM FEBTD UNUSA. The methodological stages used are problem identification, website needs analysis, website design, website creation, website testing, and website testing analysis. The results of this study are the formation of the BEM FEBTD UNUSA website, and the administrators can manage work program schedules well, provide activity information, organizational profiles, facilitate various news search purposes, important information announcements, and activity documentation galleries. Testing is done by Black Box Testing and Usability Testing. The success percentage of Black Box Testing with 117 Test Cases is 100%. Meanwhile, the test results from Usability Testing show that according to the admin and supervisor, they strongly agree if the website is easy to use, fast, memorable, no errors are found, and satisfied when used. Meanwhile, according to administrators and visitors, they agree that if the website is easy to use, fast, memorable, no errors are found, and satisfied when used.

Impact of Google searches and social media on digital assets’ volatility

Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.

The Emotion Magnitude Effect: Navigating Market Dynamics Amidst Supply Chain Events

During the volatile market period of 2019–2021, characterized by geopolitical shifts, economic sanctions, pandemics, natural disasters, and wars, the global market presented a complex landscape for financial decision making and motivated this study. This study makes two groundbreaking and novel contributions. First, we augment Plunket’s emotional research and leverage the emotional classification algorithm in Fin-Emotion to introduce a novel quantitative metric, “emotion magnitude”, that captures the emotional undercurrents of the market. When integrated with traditional time series analysis using Temporal Convolutional Networks applied to stock market futures, this metric offers a more holistic understanding of market dynamics. In our experiments, incorporating it as a feature led to significantly better performance on both the training and validation sets (9.26%, 52.11%) compared to traditional market-based risk measures, in predicting futures market trends based on the commodities and supply chains analyzed. Second, we deploy a multidimensional data science framework that synthesizes disparate data streams and analyses. This includes stock metrics of sector-leading companies, the time horizon of significant market events identified based on company stock data, and the extraction of further knowledge concepts identified through “emotion magnitude” analysis. Our approach stitches together countries, commodities, and supply chains identified in the targeted news search and identifies the domestic companies impacted based on the time horizon of these emotional supply chain events. This methodology culminates in a unified knowledge graph that not only highlights the relationships between supply chain disruptions, affected corporations, and commodities but also quantifies the broader systemic implications of such market events that are revealed. Collectively, these innovations form a robust analytical tool for financial risk strategy, empowering stakeholders to navigate an ever-evolving financial global ecosystem with enhanced insights. This graph encapsulates multi-dimensional forces and enables stakeholders to anticipate and understand the broader causal implications of related supply chain and market events (such as economic sanctions’ impact on the energy, technology, and telecommunication sectors).

The 2021 Nigerian Twitter ban: A text-analytics and survey insight into public reactions and outcomes in the early weeks of the ban

The suspension of the micro-blogging platform, Twitter, by the federal government of Nigeria on 5 June 2021 for ‘consistent non-removal of inciting and divisive contents’, undoubtedly generated mixed feelings among social media users in and outside the country. Hinging on Ball-Rokeach & DeFleur’s Media Systems Dependency theory, this study adopts a combination of text mining, sentiment analysis and survey approaches to make evident social media users’ reactions to the Twitter ban in 2021. Of the 3885 social posts that were sampled across three platforms (Facebook, Twitter and Nairaland) between 5 June and 16 July 2021, 73% decry while just 27% extol the Twitter ban action. The Nvivo word frequency results showed that the top five keywords used across the sampled posts were: ‘end’, ‘#twitterban’, ‘unlawful’, ‘suspension’ and ‘Nigeria’ ( n = 11,925; 24%), suggesting that majority of users were unhappy with the shutdown. The complementary opinion sampling conducted on students of Nile University of Nigeria revealed that the majority of the 319 sampled respondents (264; 83%) responded to VPNs to access and use Twitter despite the ban. Among the daily Twitter habits of the respondents most affected by the shutdown were Trending Topics Search (22%), Social Engagement (20%), Daily News Search (20%), Product Search (13%), Product Marketing (10%), Customer Relations (9%), Jobs Search (4%) and others (2%). The study recommends the consideration of better sanctioning means for erring social media companies that will not stifle the exchange of ideas online in this democratic age.

A hybrid classification method via keywords screening and attention mechanisms in extreme short text

Short text classification has provoked a vast amount of attention and research in recent decades. However, most existing methods only focus on the short texts that contain dozens of words like Twitter and Microblog, while pay far less attention to the extreme short texts like news headline and search snippets. Meanwhile, contemporary short text classification methods that extend the features via external knowledge sources always introduce lots of useless concepts, which may be detrimental to classification performance. Moreover, unlike traditional short text classification methods, the classification results of extreme short texts are often determined by a few even one or two keywords. To address these problems, we propose a novel hybrid classification method via Keywords Screening and Attention Mechanisms in extreme short text, called KSAM. More specifically, firstly, the attention-based BiLSTM is introduced in our method to enhance the role of keywords. Secondly, we screen the keywords in the extreme short text for obtaining the true class label, and the concepts concerning the keywords are retrieved from external open knowledge sources like DBpedia. Thirdly, the attention mechanisms are introduced to acquire the weight of these retrieved concepts. Finally, conceptual information is utilized to assist the classification of the extreme short text. Extensive experiments have demonstrated the effectiveness of our method compared to other state-of-the-art methods.

Towards improving user awareness of search engine biases: A participatory design approach

AbstractBias in news search engines has been shown to influence users' perceptions of a news topic and contribute to the polarisation of society. As a result, there is a need for news search engines that increase user awareness of biases in the search results. While technical approaches have been developed to mitigate biases in search, very few studies have investigated user preferences in interface designs for potentially raising their awareness of biases in news search engines. In this study, we utilized a participatory design methodology to develop eight prototypes with different features that could potentially be used to raise user awareness of biases in news search engines. We conducted three user studies, involving 132 participants with Computer Science backgrounds, to evaluate these prototypes. Our findings indicate the importance of news search engines that (a) inform users of possible biases in the results (bias visualization approach) and (b) allow users to access alternative search results (results‐reranking approach). Our study provides further insights into the strengths and possible risks of each approach, which are important for future research on designing interfaces for raising user awareness of biases in news search engines.

Novelty in News Search: A Longitudinal Study of the 2020 US Elections

The 2020 US elections news coverage was extensive, with new pieces of information generated rapidly. This evolving scenario presented an opportunity to study the performance of search engines in a context in which they had to quickly process information as it was published. We analyze novelty, a measurement of new items that emerge in the top news search results, to compare the coverage and visibility of different topics. Using virtual agents that simulate human web browsing behavior to collect search engine result pages, we conduct a longitudinal study of news results of five search engines collected in short bursts (every 21 minutes) from two regions (Oregon, US and Frankfurt, Germany), starting on election day and lasting until one day after the announcement of Biden as the winner. We find more new items emerging for election related queries (“joe biden,” “donald trump,” and “us elections”) compared to topical (e.g., “coronavirus”) or stable (e.g., “holocaust”) queries. We demonstrate that our method captures sudden changes in highly covered news topics as well as multiple differences across search engines and regions over time. We highlight novelty imbalances between candidate queries which affect their visibility during electoral periods, and conclude that, when it comes to news, search engines are responsible for such imbalances, either due to their algorithms or the set of news sources that they rely on.

Multilingual News Search—A Comparative User Study of Desktop and Mobile Interfaces

With the global expansion of the Internet and the World Wide Web, users are becoming increasingly diverse, including their language proficiencies. In particular, there is now a significant number of polyglot Web users, i.e., users who are proficient in more than one language. However, even such users with potential access to a broad range of information from multiple languages often continue to suffer from unbalanced and fragmented news information, as traditional news access systems seldom allow users to simultaneously search for and/or compare news in different languages. To overcome language barriers, the majority of research has focused primarily on improving retrieval and translation accuracy, while paying comparably less attention to multilingual user interaction aspects. In particular, relatively little human-centered research has been conducted to better understand and support multilingual user abilities and preferences, and even less so regarding news search and different access modalities (such as desktop and mobile interfaces). The research presented in this article provides the first comparative analyses of polyglot users’ preferences and behaviors with respect to different multilingual news search interfaces on both desktop and mobile platforms. Specifically, through a set of task-based user studies in laboratory experiments, the key contribution of this article is the presentation of the first human-centered studies in multilingual news search result interfaces, aiming to drive the development of human-centered multilingual news access systems for both desktop and mobile platforms. This contribution includes a detailed analysis of different interface design paradigms, as well as a series of implications for design.