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This paper aims to shed light on the impact of personalized news media on the shared issue agenda that provides democracies with a set of topics that structure the public debate. The advent of personalized news media that use smart algorithms to tailor the news offer to the user challenges the established way of setting the agenda of such a common core of issues.

This paper tests the effects of personalized news use on perceived importance of these issues in the common core. In particular, the authors study whether personalized news use leads to a concentration at the top of the issue agenda or to a more diverse issue agenda with a long tail of topics.

Based on a cross-sectional survey of a representative population sample (n = 1,556), we find that personalized news use does not lead to a small common core in which few topics are discussed extensively, yet there is a relationship between personalized news use and a preference for less discussed topics. This is a result of a specific user profile of personalized news users: younger, more educated news users are more interested in topics at the fringes of the common core and also make more use of personalized news offers.

The results are discussed in the light of media diversity and recent advances in public sphere research.

This paper contributes to the ongoing debate about algorithmic news dissemination. While, currently, much attention is reserved for the role of platforms as information gatekeepers in relationship to the news media, maybe their ability to enable or hinder the audience in discovering and distributing news content is part of what really characterizes their influence on the market place of ideas.

The authors examine how financial analysts respond to online investor sentiment when updating recommendations for specific stocks in South Africa. The aim is to establish whether online sentiment contains significant information that can influence analyst recommendations. The authors follow up the above by examining when online investor sentiment is most associated with analyst recommendation changes.

For online investor sentiment proxies, the authors make use of the social media sentiment and news media sentiment scores provided by Bloomberg Inc. The sample size includes all companies listed on the Johannesburg Stock Exchange All Share Index. The study uses traditional ordinary least squares to examine the relation at the mean and quantile regression to identify the scope of the relationship across the distribution of the dependent variable.

The authors find evidence that pre-event news sentiment significantly influences analyst recommendation changes while no significant relationship is found with the Twitter sentiment. Further analysis shows that news sentiment is more influential when the recommendation changes are moderate (in the middle of the conditional distribution of the recommendation changes).

The study is the one of the first to examine the association between online sentiment and analyst recommendation changes in an emerging market using high frequency data. The authors also make a direct comparison between social media sentiment and news media sentiment, some of the most used contemporary investor sentiment proxies.

Predicting the stock market's prices has always been an interesting topic since its closely related to making money. Recently, the advances in natural language processing (NLP) have opened new perspectives for solving this task. The purpose of this paper is to show a state-of-the-art natural language approach to using language in predicting the stock market.

In this paper, the conventional statistical models for time-series prediction are implemented as a benchmark. Then, for methodological comparison, various state-of-the-art natural language models ranging from the baseline convolutional and recurrent neural network models to the most advanced transformer-based models are developed, implemented and tested.

Experimental results show that there is a correlation between the textual information in the news headlines and stock price prediction. The model based on the GRU (gated recurrent unit) cell with one linear layer, which takes pairs of the historical prices and the sentiment score calculated using transformer-based models, achieved the best result.

This study provides an insight into how to use NLP to improve stock price prediction and shows that there is a correlation between news headlines and stock price prediction.

This article explores the synergy between journalism studies and computer science in the context of observing online news. By establishing web applications of online media observatories as research tools, researchers can employ various analytical approaches to gain valuable insights into online news discourse and production.

Drawing eight months of data (01.08.2022–30.04.2023) from the Labservatory’s web application, i.e. over 250,000 news items, the article demonstrates how some of this web application’s main functionalities may be useful in implementing (1) news flow analysis, (2) news topic distribution analysis and (3) media discourse analysis.

The capabilities provided by this web application, (1) to simultaneously analyse the daily news production of ten media outlets with varying features, (2) to rapidly collect a large volume of news items, (3) to identify the news categories as classified by the media themselves, (4) to present the results of the search in relevance order and (5) to automatically generate a search report, highlight the significance of this interdisciplinary collaboration for implementing comprehensive analyses of online news.

The article concludes by emphasising the importance of continuing this joint effort, as it opens new avenues for further research and provides a deeper grasp of the intricate relationship between journalism, technology and society in the digital era. The Labservatory also contributes to society since it may be used by the broader public for immediate access to more pluralistic information and thus for promoting both news media literacy and news media accountability.

This study aims to acknowledge that most Indonesian small and medium enterprises (SMEs) experience slow growth. It highlighted that this sluggishness is because of some falsification of Indonesia’s ecological psychology. It focuses on investigating the situated cognition that probably supports this falsification, such as affordance, a community of practice, embodiment and the legitimacy of peripheral participation situated cognition and social intelligence theories.

This study obtained data from published newspapers between October 2016 and February 2019. The authors used the Waikato Environment for Knowledge Analysis and the J48 C.45 algorithm. The authors analyzed the data using the emergence of news probability for both the Government of Indonesia (GoI) and Indonesian society and the situated cognition concerning the improvement of the SMEs. The authors inferred ecological psychology from these published newspapers in Indonesia that the engaged actions were still suppressed, in comparison with being and doing.

This study contributes to the innovation and leadership policies of the SMEs’ managerial systems and the GoI. After this study identified the backward-looking practices, which the GoI and the people of Indonesia held, this study recommended some policies to help create a forward-looking orientation. The second one is also a policy for the GoI, which needs to reduce the discrepancy between the signified and the signifier, as recommended by the structuralist theory. The last one is suggested by the social learning theory; policies are needed that relate to developing the SMEs’ beliefs, attitudes and behavior. It means that the GoI should prepare the required social contexts, which are in motoric production and reinforcement. Explicitly, the authors argue that the GoI facilitates SMEs by emphasizing the internal learning process.

The authors present some possibilities for the limitations of this research. The authors took into account that this study assumes the SMEs are all the same, without industrial clustering. It considers that the need for social learning and social cognition by the unclustered industries is equal. Second, the authors acknowledge that Indonesia is an emerging country, and its economic structure has three levels of contributors; the companies listed on the Indonesian Stock Exchange, then the SMEs and the lowest level is the underground economy. Third, the authors did not distinguish the levels of success for the empowerment programs that are conducted by either the GoI or the local governments. This study recognizes that the authors did not measure success levels. It means that the authors only focused on the knowledge content.

From these pieces of evidence, this study constructed its strategies. The authors offer three kinds of policies. The first is the submission of special allocation funds from which the GoI and local governments develop their budgets for the SMEs’ social learning and social cognition. The second is the development of social learning and social cognition’s curricula for both the SMEs’ owners and executive officers. The third is the need for a national knowledge repository for all the Indonesian SMEs. This repository is used for the dissemination of knowledge.

This study raises argumental novelties with some of the critical reasoning. First, the authors argue that the sluggishness of the Indonesian SMEs is because of some fallacies in their social cognition. This social cognition is derived from the cultural knowledge that the GoI and people of Indonesia disclosed in the newspapers. This study shows the falsifications from the three main perspectives of the structuration, structuralist and social learning theories. Second, this study can elaborate on the causal factor for the sluggishness of Indonesia’s SMEs, which can be explained by philosophical science, especially its fallacies (Hundleby, 2010; Magnus and Callender, 2004). The authors expand the causal factors for each gap in every theory, which determined the SMEs’ sluggishness through the identification of inconsistencies in each dimension of their structuration, structuralism and social learning. This study focused on the fallacy of philosophical science that explains the misconceptions about the SMEs’ improvement because of faulty reasoning, which causes the wrong moves to be made in the future (Dorr, 2017; Pielke, 1999).

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies.

This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI.

By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI.

Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

Purpose – This study explored how the pandemic shaped or shifted legacy news reporting about crime, focusing on Twitter posts as visual elements of the crossmedia landscape.

Methodology/Approach – Drawing a purposive sample of tweets about crime and the pandemic posted from March 2020 to December 2021 by major TV news outlets, the qualitative media analysis (QMA) scrutinized how tweets constructed narratives about crime. The analysis considered images, text, and their juxtaposition within tweets and over time.

Findings – This study found that news organizations partnered the pandemic and crime in the American discourse of fear. Tweets acted as crime news snapshots, which magnified a sense of instability and uncertainty. Tweets constructed a collective malaise that could contribute to users’ sense of ontological insecurity.

Originality/Value – The spectacle of crime churned through news organizations’ tweets, dissociating crime from the complex social context of the pandemic. Attention to the liquidity of images and information in the crossmedia landscape revealed fluctuating social meanings and disorientation.