News Discovery Research Articles

News discovery marketing strategy SMEs with entrepreneurial concept

ABSTRACT This study aims to determine the factors that influence marketing strategy and marketing performance in SMEs of souvenir food. Entrepreneurial Orientation (EO), Marketing Orientation (MO), and Unique Resources (UR) are used as independent variables to drive Marketing Strategy (MS) and Marketing Performance (MP). Using a purposive technique, data was collected from 248 SMEs souvenir food, consisting of owners and managers. The research method employed is descriptive correlational, using Structural Equation Modeling (SEM) with the SmartPLS 4 program to analyze the data. The findings of the study reveal that EO, MO, and UR play a significant role in enhancing marketing strategies and improving the effectiveness and direction of marketing performance due to their interrelatedness.

Do Algorithms Do It Better? Analysing Occupational Ideology in the Age of Computational Journalism

ABSTRACT Journalism is increasingly discussed through the lens of a quantitative and material turn associated with data and computational journalism, implying that important aspects of news production are mediated by technological actants. The implications of these developments may be understood as part of journalism’s reconfiguration, calling for continuous monitoring and reconsideration of what journalism is, how it is done, and why. Using the framework of occupational ideology and drawing on qualitative research, this study offers empirical knowledge on the changing constituents of occupational ideology in reference to evaluating a Computational News Discovery tool. The findings add new insights to an emerging trend suggesting that journalists compromise their civic duties in the name of traffic-building and algorithmic judgment. While the results speak to the idiosyncrasy of the case study, they add nuance to the increasing marketisation and technologisation of news production by acknowledging their intensity and the subsequent formalisation of “good performance” over professional judgement. We also find that for newcomers, performance and productivity are becoming “organic elements” of their occupational ideology, while older journalists succumb to organisational pressures. Overall, assigned possibilities of computational tools, although quite detached from the reality of the technology, tend to influence fundamental values and norms of journalism.

Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism

Science and technology journalists today face challenges in finding newsworthy leads due to increased workloads, reduced resources, and expanding scientific publishing ecosystems. Given this context, we explore computational methods to aid these journalists' news discovery in terms of their agency and time-efficiency. We prototyped three computational information subsidies into an interactive tool that we used as a probe to better understand how such a tool may offer utility or more broadly shape the practices of professional science journalists. Our findings highlight central considerations around science journalists' user agency, contexts of use, and professional responsibility that such tools can influence and could account for in design. Based on this, we suggest design opportunities for enhancing and extending user agency over the longer-term; incorporating contextual, personal and collaborative notions of newsworthiness; and leveraging flexible interfaces and generative models. Overall, our findings contribute a richer view of the sociotechnical system around computational news discovery tools, and suggest ways to improve such tools to better support the practices of science journalists.

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.

News platformization in Portugal: analysis of the dependence of the news media on social media and search engines

The research aims to understand the news platformization process based on the identification and analysis of the dependence that Portuguese news media have on social media and search engines in the process of distributed discovery of news. The method replicated the dependency analysis model proposed by Nielsen & Fletcher (2002) and Tambini & Labo (2016), selecting the 30 Portuguese journalistic media with the highest average volume of web traffic between July, August, and September 2022. Thus, the different types of access were identified with the SimilarWeb platform, used in the literature as a reference source in webometrics. The data showed that 19 news media have more than 50% of indirect traffic dependent on the platforms. As for the total volume, we found that, on average, 56.26% of news access originates from social media or search engines; 36.05% from direct access; 7.05% from reference links; and 0.31% from email, sponsored search, and advertising. Considering mobile devices, the average dependency increases to 57.35% and, on desktop devices, it drops to 50.05%. We found that there is variation according to the type of media and its content: those specializing in football have greater direct access, and those specializing in lifestyle and celebrities have greater indirect access. Digital native media also rely more on platforms, as do media coming from Radio and TV. We conclude that platformization is a relevant phenomenon in the news ecosystem in Portugal, with significant integration of journalistic media content in these digital infrastructures. Thus, media will have to find ways to counterbalance the power of platforms, which, at the same time, improve connection with the user and increase audience reach, control data, interaction patterns, as well as online advertising, operating in a logic of increased dependency.

Exploring Generation Z's Perspective on Twitter as a News Discovery Platform: Insights from @CNNIndonesia Followers

This qualitative descriptive research aims to explore how Generation Z perceives the use of Twitter, particularly the @CNNIndonesia news account, as a platform for news discovery. Employing purposive sampling, in-depth interviews, and observations, the study reveals that followers retain knowledge obtained from @CNNIndonesia, emphasizing the critical role of perception indicators in influencing their attitudes. The informants express a preference for @CNNIndonesia, citing its reliability and alignment with their information needs. The results underscore the significance of perception in shaping user behavior on Twitter as a news source. Highlights : Digital Information Consumption: Explore how Generation Z consumes and retains news information through the Twitter platform, specifically focusing on the @CNNIndonesia account. Perception Indicators: Investigate the key indicators shaping the perceptions of Generation Z followers towards @CNNIndonesia as a reliable and preferred news source. User Behavior Influence: Highlight the significance of perception in influencing the behavior of Generation Z users on Twitter as they seek and engage with news content. Keywords: Generation Z, Twitter, News Discovery, @CNNIndonesia, Perceptions

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.

Comparison of political agenda spreading agents and spreading agenda in social media before and after fake news discovery : Focusing on the spread of suspicion of ‘Cheongdam-dong drinking party’ on YouTube

Comparison of political agenda spreading agents and spreading agenda in social media before and after fake news discovery : Focusing on the spread of suspicion of ‘Cheongdam-dong drinking party’ on YouTube

Aquila optimized feedback artificial tree for detection of fake news and impact identification

In recent days, social media is termed a major source for several people residing over the world because of less cost, simpler accessibility, and quick dissemination. However, it comes with dubious trustworthiness and is of high risk in exposing fake news. Hence, the automated discovery of fake news is an essential task. An innovative model is provided to identify fake news considering social media. Here, the BERT model is utilized to perform tokenization in order to produce tokens. Multiple features linked with the data are analyzed for detecting the behavior using the deep model. The features, like Term Frequency-Inverse Document Frequency (TF-IDF), SentiWordNet scores, and sentence level features are obtained to automatically learn the features. Automatic discovery of fake news is done with Aquila Feedback Artificial tree-based Deep Residual Network (AFAT-based DRN). The optimum weight tuning of DRN is executed with AFAT and the AFAT is the fusion of Aquila optimizer (AO) and Feedback artificial tree (FAT). The impact detection of fake news is done with AFAT-based DRN, which helps to detect how many of them shared the fake news. The AFAT-based DRN offered high competence with utmost sensitivity of 92.3%, testing accuracy of 91.6%, and specificity of 91.9%.

Fake News Detection using Machine Learning

The approach of the Internet and the quick reception of online entertainment stages (like Facebook and Twitter) prepared for data scattering that has never been seen in mankind's set of experiences previously. With the ongoing use of online entertainment stages, shoppers are making and sharing more data than at any other time in recent memory, some of which are deluding with no importance to the real world. Mechanized characterization of a text article as deception or disinformation is difficult. Indeed, even a specialist in a specific space needs to investigate various viewpoints before deciding on the honesty of an article. In this work, we propose to utilize an AI-gathering approach for robotized order of news stories. Our review investigates different printed properties that can be utilized to separate phony items from genuine ones. By utilizing those properties, we train a mix of various AI calculations utilizing different troupe strategies and assess their exhibition on 4 genuine world datasets. Exploratory assessment affirms the unrivaled presentation of our proposed outfit student approach in contrast with individual students. Counterfeit news discovery is bit by bit happening to principal significance to our society to keep away from the alleged reality dizziness and safeguard specifically the less instructed people. Different AI procedures have been proposed to resolve this issue. This article presents a thorough exhibition assessment of eight AI calculations for counterfeit news identification/characterization

Characterizing multi-domain false news and underlying user effects on Chinese Weibo

False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world. While there are studies of false news on specific domains (like politics or health care), little work is found comparing false news across domains. In this article, we investigate false news across nine domains on Weibo, the largest Twitter-like social media platform in China, from 2009 to 2019. The newly collected data comprise 44,728 posts in the nine domains, published by 40,215 users, and reposted over 3.4 million times. Based on the distributions and spreads of the multi-domain dataset, we observe that false news in domains that are close to daily life like health and medicine generated more posts but diffused less effectively than those in other domains like politics, and that political false news had the most effective capacity for diffusion. The widely diffused false news posts on Weibo were associated strongly with certain types of users — by gender, age, etc. Further, these posts provoked strong emotions in the reposts and diffused further with the active engagement of false-news starters. Our findings have the potential to help design false news detection systems in suspicious news discovery, veracity prediction, and display and explanation. The comparison of the findings on Weibo with those of existing work demonstrates nuanced patterns, suggesting the need for more research on data from diverse platforms, countries, or languages to tackle the global issue of false news. The code and new anonymized dataset are available at https://github.com/ICTMCG/Characterizing-Weibo-Multi-Domain-False-News.

Fake News Detection using Machine Learning: A Comprehensive Analysis

Fake news today is an important fact or the life of social media and in the political world. False news discovery is an important study that should be done for its discovery but there are some challenges as well. Some challenges may be due to the small number of similar resources available collection of data and publications. We suggest in this paper, the discovery of false information using machine learning techniques. We compare three different stages of machine learning strategies. Not only that, but we will be working with three different models namely Logistic Regression, Decision Tree Classifier, and Random Forest Classification. According to the discovery of our project, we have gained your various accuracy each way in sequence. Our project can greatly benefit from finding out if the given information is true or fake.

Towards Understanding and Supporting Journalistic Practices Using Semi-Automated News Discovery Tools

Journalists are routinely challenged with monitoring vast information environments in order to identify what is newsworthy and of interest to report to a wider audience. In a process referred to as computational news discovery, alerts and leads based on data-driven algorithmic analysis can orient journalists' attention to events, documents, or anomalous patterns in data that are more likely to be newsworthy. In this paper we prototype one such news discovery tool, Algorithm Tips, which we designed to help journalists find newsworthy leads about algorithmic decision-making systems used across all levels of U.S. government. The tool incorporates algorithmic, crowdsourced, and expert evaluations into an integrated interface designed to support users in making editorial decisions about which news leads to pursue. We then present an evaluation of our prototype based on an extended deployment with eight professional journalists. Our findings offer insights into journalistic practices that are enabled and transformed by such news discovery tools, and suggest opportunities for improving computational news discovery tool designs to better support those practices.

Sources that Trigger the News: Multiplexity of Social Ties in News Discovery

ABSTRACT The study explores how the content of the social ties between journalists and their sources, and the multiplexity of the ties in particular, is reflected in the news discovery practices in the political beat. Some of the news ideas come from publicly available channels while others derive from journalists’ unique networks of social contacts (often to some degree informal). The sources connected to journalists via single vs. multiplex social ties achieve a discursive power (Jungerr et al. 2019) in different ways. The study is based on reconstructed news discovery situations (n = 162) from two different political journalism environments: Lithuania and Sweden, and combines quantitative and qualitative questions. First, it measures the prevalence of the multiplex social ties between journalists and the sources. Second, it analyzes how the multiplexity of these ties is reflected in the process of the news discovery and, third, the outcome—the news content. The results show that the prevalence of multiplex social ties, and the social network behind them, is reflected in what news journalists can access and select as newsworthy. Process-wise, multiplex social ties work as a shortcut but require a balance between maintaining the access to the network and distance to the source.

Credibility Detection on Twitter News Using Machine Learning Approach

Social media presence is a crucial portion of our life. It is considered one of the most important sources of information than traditional sources. Twitter has become one of the prevalent social sites for exchanging viewpoints and feelings. This work proposes a supervised machine learning system for discovering false news. One of the credibility detection problems is finding new features that are most predictive to better performance classifiers. Both features depending on new content, and features based on the user are used. The features' importance is examined, and their impact on the performance. The reasons for choosing the final feature set using the k-best method are explained. Seven supervised machine learning classifiers are used. They are Naïve Bayes (NB), Support vector machine (SVM), Knearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Maximum entropy (ME), and conditional random forest (CRF). Training and testing models were conducted using the Pheme dataset. The feature's analysis is introduced and compared to the features depending on the content, as the decisive factors in determining the validity. Random forest shows the highest performance while using user-based features only and using a mixture of both types of features; features depending on content and the features based on the user, accuracy (82.2 %) in using user-based features only. We achieved the highest results by using both types of features, utilizing random forest classifier accuracy(83.4%). In contrast, logistic regression was the best as to using features that are based on contents. Performance is measured by different measurements accuracy, precision, recall, and F1_score. We compared our feature set with other studies' features and the impact of our new features. We found that our conclusions exhibit high enhancement concerning discovering and verifying the false news regarding the discovery and verification of false news, comparing it to the current results of how it is developed.

Tick Size Tolls: Can a Trading Slowdown Improve Earnings News Discovery?

ABSTRACT This study examines how an increase in tick size affects algorithmic trading (AT), fundamental information acquisition (FIA), and the price discovery process around earnings announcements (EAs). Leveraging the SEC's randomized Tick Size Pilot experiment, we show that a tick size increase results in a decline in AT and a sharp drop in absolute cumulative abnormal returns and volume around EAs. More importantly, we find increased FIA in the preannouncement period. Specifically, we show: (1) treatment firms' pre-announcement returns better anticipate next quarter's standardized unexpected earnings; (2) these firms experience an increase in EDGAR web traffic prior to EAs; and (3) they exhibit a drop in price synchronicity with index returns. Taken together, our evidence suggests that while an increase in tick size reduces AT and abnormal market reaction after EAs, it also increases FIA activities prior to EAs. JEL Classifications: M40; M41; G12; G14.

Computational News Discovery: Towards Design Considerations for Editorial Orientation Algorithms in Journalism

Computational news discovery (CND) is a particular application area within computational journalism related to the use of algorithms to orient editorial attention to potentially newsworthy events or information prior to publication. Previous work in this area has been concentrated on prototyping CND tools, which can, for instance, send alerts and leads to journalists about social media events, documents of interest, or salient patterns in streams of data. This article describes a qualitative interview study of journalists as they incorporate CND tools into their practices. Findings provide insights into how CND tools interact with the internal attention economy and sociotechnical gatekeeping processes of the newsroom and how future CND tools might better align with necessary journalistic evaluations of newsworthiness and quality, while ensuring configurability, human agency, and flexible applicability to a wide range of use cases. These findings begin to outline a conceptual framework that can help guide the effective design of future CND tools.

Who Takes the Lead? Investigating the Reciprocal Relationship Between Organizational and News Agendas

This study introduces the element of time to investigate the causal relation between organizational and news media agendas. Reciprocal time-series analyses were applied to daily-level aggregated press releases ( n = 17,221) and news articles ( n = 74,067). Results indicate that on the first level of agenda building, organizational and news agendas are intertwined in an intimate relation of reciprocal influence, in which organizations more often take the lead. Conversely, results suggest that on the second level of agenda building, organizational and news agendas influence each other less often. Organizational and newspaper characteristics proved useful to map the contingency of agenda-building effects. The findings suggest that organizational sources are more influential in the news discovery phase compared with the news-gathering phase, and imply that the unidirectional conceptualization of news media as a channel to vent organizational messages is too narrow.

Research on hot news discovery model based on user interest and topic discovery

In order to find a way to process network data and discover hot news based on user’s interest and topic, through research on hot news discovery algorithm, a double-layer text clustering model based on density clustering strategy and Single-pass strategy is proposed. In view of the huge network data characteristics, DBSCAN algorithm is firstly used to cluster the single-crawled network data into small-scale clusters. Then, the Single-pass strategy is used to perform incremental clustering on the micro-classes to create the topic classes. In the hot news part of the network, the media and the user’s attention to the topic is combined to design a model. The heat quantization formula is obtained. Based on the research of related technologies, a network hot topic detection model is designed and implemented by using web crawler, news discovery and hot news discovery technology. By comparing the two-layer model used in the model with the traditional Single-pass strategy, the feasibility of the two-layer model is verified, and the discovery of network hot news is realized.

Anticipated Earnings Announcements and the Customer-Supplier Anomaly

I test whether the anticipation of earnings news stimulates acquisition of customer information and mitigates returns to the customer–supplier anomaly documented by Cohen and Frazzini (“Economic Links and Predictable Returns.” The Journal of Finance 63 (2008): 1977–2011). I find that attention to a firm's publicly disclosed customers increases shortly before the firm announces earnings, and that customer stock returns predict supplier stock returns shortly before, but not after, the supplier's earnings announcement. I further find some evidence that these predictable returns are increasing in the level of customer information acquisition. These results are unique to anticipated disclosure events and suggest that anticipation of supplier earnings announcements resolves investor limited attention to customer information and accelerates price discovery of customer news.