News Topics Research Articles

TV media sentiment, mutual fund flows and portfolio choice: They do not put their money where their sentiment is

The central role of the media for people’s sentiment and for capital markets has been analysed by a broad range of literature, referenced from several strands of academic research. Applying a vector autoregression method to a unique set of TV news, consumer sentiment and excess flows of mutual funds, I have found evidence that daily TV news is reflected in consumer sentiment and that this reflection varies according to specific news topics. However, I have not found any evidence that this has an impact on viewers’ portfolio allocation decisions. Mutual fund investors neither seem to put their money where their newly gained insights from the TV news are, nor where their sentiment is. Anchoring also appears to play a role, at least for US investors. These findings are robust in relation to different measures of fund flows and as an alternative indicator for sentiment in the news media.

Comparing Journalistic Role Performance Across Thematic Beats: A 37-Country Study

Studies suggest that, at the routine level, news beats function as unique “micro-cultures.” Exploring this “particularist” approach in news content, we compare how the interventionist, watchdog, loyal, service, infotainment, and civic roles materialize across 11 thematic news beats and analyze the moderating effect of platforms, ownership, and levels of political freedom on journalistic role performance in hard and soft news. Based on the second wave of the Journalistic Role Performance (JRP) project, this article reports the findings of a content analysis of 148,474 news items from 37 countries. Our results reveal the transversality of interventionism, the strong associations of some topics and roles, and the limited reach of news beat particularism in the face of moderating variables.

The effects of algorithmic content selection on user engagement with news on Twitter

In this article, we investigate how Twitter’s switch from a reverse-chronological timeline to algorithmic content selection in March 2016 influenced user engagement with tweets published by German newspapers. To mitigate concerns about omitted variables, we use the Facebook postings of these newspapers as a counterfactual. We find that the number of likes increased by 20% and the number of retweets by 15% within a span of 30 days after the switch. Importantly, our results indicate a rich-get-richer effect, implying that initially more popular outlets and news topics benefited the most. User engagement also increased more for sensationalist content than quality news stories.

Print media coverage of child maltreatment in Bangladesh: A content analysis

Newspapers can be a vital source of information in Bangladesh for investigating child maltreatment (CM), since the epidemiological data on CM is underestimated. Yet, there is little information on how CM-related content is covered in newspapers. To investigate the distribution and news framing of different types of CM cases, 2,957 news reports were collected retrospectively from six national (four Bengali and two English-language) daily newspapers published from January to December 2017. Berelson’s quantitative content analysis technique (only manifest content) was used to analyze the stories. From the descriptive statistics, this study showed that sexual (30%) and physical abuse (29%) were highly reported in Bangladeshi print media. Although 72% of newspapers focused on episodic events, only 27% of CM stories were reported using thematic frame. Individual-level causal (72%) and solution frame (53%) were more prominent, whereas societal-level solution frame (18%) were poorly reported focusing on the larger context of the society. We recommend that authorities take necessary steps to encourage journalists to cover more thematic news with societal-level solution frames to help prevent CM.

Sexual Harassment in News Gender Violence in Tempo.co Media and Newsroom Narratives (Sara Mills Critical Discourse Analysis)

This study aims to describe the position of subjects-objects and writer-reader positions on news topics of gender violence against women in the media Tempo.co December 2021. This research takes a qualitative approach with the critical discourse analysis method of Sara Mills' model. The object of this study is the construction of language that gives rise to the position of women in news texts. The results of this study are the position of subject-object and writer-reader in the news of information media Tempo.co and Narasi News room

Beyond fear and anger: A global analysis of emotional response to Covid-19 news on Twitter

The media has been used to disseminate public information amid the Covid-19 pandemic. However, Covid-19 news has triggered emotional responses in people that have impacted their mental well-being and led to news avoidance. To understand the emotional response to Covid-19 news, we studied user comments on news published on Twitter by 37 media outlets in 11 countries from January 2020 to December 2022. We employed a deep-learning-based model to identify the basic human emotions defined by Ekman in comments related to Covid-19 news. Additionally, we implemented Latent Dirichlet Allocation (LDA) to identify the news topics. Our analysis found that while nearly half of the user comments showed no significant emotions, negative emotions were more common. Anger was the most prevalent emotion, particularly in the media and comments regarding political responses and governmental actions in the United States. On the other hand, joy was mainly linked to media outlets from the Philippines and news about vaccination. Over time, anger consistently remained the most prevalent emotion, with fear being most prevalent at the start of the pandemic but decreasing over time, occasionally spiking with news on Covid-19 variants, cases, and deaths. Emotions also varied across media outlets, with Fox News being associated with the highest level of disgust, the second-highest level of anger, and the lowest level of fear. Sadness was highest at Citizen TV, SABC, and Nation Africa, all three African media outlets. Additionally, fear was most evident in the comments on news from The Times of India.

Hybrid Human and Machine Learning Algorithms to Forecast the European Stock Market

This paper explores the power of news sentiment to predict financial returns, particularly the returns of a set of European stocks. Building on past decision support work going back to the Delphi method, this paper describes a text analysis expert weighting algorithm that aggregates the responses of both humans and algorithms by dynamically selecting the best answer according to previous performance. The proposed system is tested through an experiment in which ensembles of experts, crowds, and machines analyzed Thomson Reuters news stories and predicted the returns of the relevant stocks mentioned right after the stories appeared. In most cases, the expert weighting algorithm was better than or as good as the best algorithm or human. The algorithm’s capacity to dynamically select the best answers from humans and machines results in an evolving collective intelligence: the final decision is an aggregation of the best automated individual answers, some of which come from machines and some from humans. Additionally, this paper shows that the groups of humans, algorithms, and expert weighting algorithms have associated with them, particularly, news topics that these groups are good at making predictions from.

Local Television News on Instagram: Exploring the Effects of News Values and Post Features on Audience Engagement

ABSTRACT This study investigates the effects of Instagram news values and post features on audience engagement by sampling 2,100 news posts distributed by 12 U.S. local television stations’ Instagram accounts. The content analysis results indicate that various news topics, message vividness and interactivity, post time, captions, and length significantly impact Instagram engagement rate by followers. The research further discusses managerial, practical implications for local broadcasters and digital publishers to optimize their Instagram content and enhance audience engagement.

Research on Efficient Work Innovation Effect Evaluation Based on Real-Time Online Public Opinion Data Classification and Detection Algorithm

Various heterogeneous network resources are collected through web crawlers, and after preprocessing such as data cleaning, they are written into the data center. And implement data mining technologies such as classification, clustering, association rules and anomaly detection. If an enterprise wants to maintain a good development ability and maintain a correct development trend, it must pay attention to and do a good job in ideological and political work, and take ideological and political work as the guiding light of its own operation and development. Improved the stability and accuracy of clustering results. The news topics are clustered to form topics, and the topic tracking technology is used to track specific news topics to achieve personalized services.

Intelligent Sports Prediction Analysis System Based on Edge Computing of Particle Swarm Optimization Algorithm

In recent years, the level of competition in my country's sports field has developed rapidly, and the public's attention to sports competitions has also increased. The forecast and analysis of sports events have therefore gathered the attention of a broad audience. The sports forecast news reports issued by various sports news media have become popular entertainment news topics by many readers. The research purpose of this article is to explore the research and design of intelligent sports prediction analysis systems based on particle swarm optimization algorithm. Under the background that artificial intelligence has been widely used in various industries, this article combines the predictive performance of the edge computing of the particle swarm optimization algorithm in artificial intelligence and the traditional sports event predictive analysis method to design an intelligent sports predictive analysis system. After understanding the methods and research status of sports events prediction analysis through literature research, this article conducts demand analysis and feasibility analysis on the intelligent sports prediction analysis system based on the main factors that need to be considered in sports prediction analysis and the current application of particle swarm optimization algorithms. According to the demand analysis, the functional modules of the intelligent sports predictive analysis system are designed, and a systematic test experiment is carried out on the predictive performance of the intelligent sports predictive analysis system for sports events. Experiments show that the prediction accuracy of the intelligent sports prediction analysis system for sports events is higher than that of traditional sports events prediction methods, which can reach about 89.6%, and it can better cater to the interests of readers who are concerned about sports events.

Analisis Topic Modelling Persepsi Pengguna Internet Menggunakan Metode Latent Dirichlet Allocation

Technological advances have undoubtedly had a major impact on information media. One impact of technological progress is the existence of news media as a source of publik information. There is also regional information, both domestic and foreign, and of course there are various discussions. News data from online news portals can be used as a source of information as a source of research and analysis. Of course, newa portals cover all types of news on various topics. Indentifying frequently discussed topics on news portal will denfinitely take a lot of time. Therefore, this research focuses on applying a topic modelling system to implement a news topic decision system using the Latent Dirichlet Allocation (LDA) method. This research successfully applies the Latent Dirichlet Allocation (LDA) method in determining news topics, of which there are three topic categories that are often discussed on the online news portal detik.com. topic 1 contains natural disaster event, topik 2 contins political figures and issues, topik 3 conttains news about the world cup.

Negative ageism and compassionate ageism in news coverage of older people under COVID-19: how did the pandemic progression and public health responses associate with different news themes?

AbstractPrevious studies have found negative ageing narratives in the media during the COVID-19 pandemic. However, few have focused on compassionate ageism and how the news responded to the progression of the COVID-19 pandemic. We investigated (a) media themes of negative and compassionate ageism and (b) their relationships with COVID-19 parameters and the public health response. The sample included 1,197 articles relevant to COVID-19 and older people in Hong Kong published between January and December 2020. We used thematic analysis to identify themes from the news articles and structural equation modelling to explore these themes' relationship with the number of older people infected, effective reproduction number, number of COVID-19 deaths and public health response parallel in time. Pandemic-related variables were lagged for a day – the time needed to be reflected in the news. Two negative ageism themes portrayed older people as vulnerable to COVID-19 but counterproductive in combating the pandemic. Two compassionate ageism themes depicted older people as a homogenous group of passive assistance recipients. The themeblaming older peoplewas associated with the number of confirmed infections (β = 0.418,p= 0.002) butvulnerability of older peoplewas not associated with pandemic-related variables. The themehelping older peoplewas negatively associated with the percentage of older people in confirmed infections (β = −0.155,p= 0.019). The themeresources availablewas negatively associated with confirmed infections (β = −0.342,p< 0.001) but positively associated with the Containment and Health Index (β = 0.217,p= 0.005). Findings suggested that negative and compassionate ageism were translated into narratives about older people in the media as the pandemic evolved but did not address the actual risk they faced. Media professionals should be aware of the potential negative and compassionate ageism prompted by the news agenda and promote adequate health behaviours and responses.

Self-Injury in the News: A Content Analysis

Non-suicidal self-injury (NSSI) has garnered increasing academic and media attention in society. While more awareness of NSSI is welcomed, inappropriate reporting of NSSI in media could heighten the potential for stigmatization and misunderstanding of NSSI and people who engage in it. Further, certain kinds of content (e.g., graphic imagery) may be harmful to people who self-injure (e.g., provoking urges to self-injure). These concerns notwithstanding, little research has focused on how NSSI has been portrayed in news media. Such knowledge would therefore represent a first step toward illuminating the nature of media depictions of NSSI and highlight potential areas to circumvent any concerns. Using content analysis, we explored how NSSI was portrayed in 568 online news articles about NSSI, published between 2007 and 2018, from top news sources in Australia, Canada, New Zealand, the United Kingdom, and the United States. Codes were developed based on prior research investigating online NSSI content, and the available existing and proposed media guidelines for the reporting of NSSI at the time of the study. While the overall tone of the examined articles was often neutral, areas of concern included: most articles detailing specific NSSI methods, the frequent inclusion of negative imagery, an absence of clear communication about what NSSI is and why people self-injure, the use of sensationalist and stigmatizing language, and a lack of helpful resources. These preliminary findings suggest the utility of a set of newly developed media guidelines on the reporting of NSSI as one component in an effort to address the stigmatization and misunderstanding of NSSI and individuals who self-injure.

Social Bots' Role in the COVID-19 Pandemic Discussion on Twitter.

Social bots have already infiltrated social media platforms, such as Twitter, Facebook, and so on. Exploring the role of social bots in discussions of the COVID-19 pandemic, as well as comparing the behavioral differences between social bots and humans, is an important foundation for studying public health opinion dissemination. We collected data on Twitter and used Botometer to classify users into social bots and humans. Machine learning methods were used to analyze the characteristics of topic semantics, sentiment attributes, dissemination intentions, and interaction patterns of humans and social bots. The results show that 22% of these accounts were social bots, while 78% were humans, and there are significant differences in the behavioral characteristics between them. Social bots are more concerned with the topics of public health news than humans are with individual health and daily lives. More than 85% of bots' tweets are liked, and they have a large number of followers and friends, which means they have influence on internet users' perceptions about disease transmission and public health. In addition, social bots, located mainly in Europe and America countries, create an "authoritative" image by posting a lot of news, which in turn gains more attention and has a significant effect on humans. The findings contribute to understanding the behavioral patterns of new technologies such as social bots and their role in the dissemination of public health information.

Identifying the role of media discourse in tourism demand forecasting

ABSTRACT High-frequency and timely tourism demand forecasting of fine-grained attractions is an effective tool that helps tourism stakeholders make decisions and formulate strategies. However, there are limitations due to the external sensitivity of tourism demand. This research integrated news coverage with other psychosocial variables to comprehensively explore the impact of social unrest on tourism demand. Topic modelling was applied to identify tourism news topics and potential meanings. Sentiment classification was used to convert news into structured data. Sentiment indices and quantities constituted a composite news term. The empirical results showed that the comprehensive inclusion of external elements, especially news coverage, can significantly improve the prediction performance. K-Nearest Neighbour with the news term yielded the best results.

Which news topics drive economic prosperity in China?

Precise and real-time measurements of economic prosperity are vital to a country's economic system. This study aims to identify news topics that promoted economic prosperity in China from 2011-2021. By extracting economic topics from news text data, we construct a news coincidence index with comprehensive information and strong timeliness and reveal the trend of topic contribution. The Latent Dirichlet Allocation (LDA) topic model is applied to extract economic topics from the news. We use a mixed-frequency dynamic factor model to track rapid economic development without using high-frequency weekly and daily data. We identify the six most influential topics and investigate their evolution, which may serve as a reference for economic construction and regulation.

A computer-assisted textual analysis of 10,191 rape news headlines shared on social media

Although rape is prevalent in Bangladesh, we have limited knowledge about how the media reports rape news. This study analyzed 10,191 headlines of rape news shared on ten Bangladeshi media's Facebook pages between 2013 and 2021. The aim was to investigate the media's rape news-sharing behavior by exploring the frequent topics of rape news. Following a computer-assisted textual analysis of the Bangla news headlines, this study found that media outlets share rape news on social media that is more likely to contain issues related to rape victims and judicial processes of rape crime. The news headlines provide information about rape victims, indicating their age groups and relations (e.g., teenager, young lady, girl, woman, wife), along with information about judicial processes (e.g., case, arrest, complaint, remand). The analysis further explored other prevalent topics in rape news that the media emphasizes, such as the elements used in rape (e.g., the promise of love and marriage, private video), the act of raping (e.g., tying, forcing, blackmailing, confining), and the places, states, and time of rape (e.g., alone, home, field). This study contributes to the theoretical understanding of news-sharing behavior, gender-based violence, and media framing of rape issues. It also offers methodological guidance to semi-automated social media text analysis for the Bangla language.

Comparing news and non-news sites in Web3 domain

This study provides a pioneering analysis of the features and topics of news websites in the Web 3.0 era through a comparison with non-news sites. We first classify over 4,600 Web3 sites into news and non-news types to investigate the feature characteristics of decentralized websites as well as semantic and subject categories. The most novel finding is that Web3 news sites have more features than non-news sites in terms of design systems and functions, interactivity, information quality, and hyperlinks. Furthermore, news web3 domains have more terms in semantic networks associated with information provision (e.g., "source"), whereas non-news domains have more terms associated with finance (e.g., "token"). The integration of the Decentralized Autonomous Organization, a fundamental component of the Web3 ecosystem, began in the news industry and is at an early stage in terms of functionality and structure. The study’s results are discussed in the context of future Web3 domain development.

Navigating Misinformation: Tackling The Fake News Wave During COVID-19 In Jaipur

With the emergence of social media, there has been a notable change in how news is both created and consumed. Global social media use in 2020 reached around 2.95 billion individuals. The widespread dissemination of the COVID-19 coronavirus led to a surge of activity on social media. Most mediums were used to disseminate pertinent news, recommendations, and precautionary measures. According to the World Health Organization (WHO), unregulated dissemination of misinformation and conspiracy theories is proliferating at a faster pace than the transmission of the COVID-19 pandemic. Consequently, there has been an excessive amount of information circulating, causing psychological stress, unreliable medical advice, and economic upheaval. The primary objective of this study is to examine the portrayal of false information and assess the consequences it has had on the inhabitants of Jaipur city. The objective of this research is to identify various kinds of false information and examine the underlying motives of individuals who disseminate misleading material. This will be achieved by a comprehensive examination of content on four prominent social media platforms. This research analyzed 130 of the most widely circulated false news items on social media in Jaipur. Data is gathered from the city of Jaipur via the utilization of the four predominant social media platforms: Facebook, YouTube, Twitter, and WhatsApp. There are four distinct criteria that may be used to differentiate false news topics: COVID-19 therapy, medical facilities, therapeutic options, and currently accessible interventions. Hospitals' management of COVID-19 is widely misunderstood, including 34.62 percent of all misinformation. Text, images, audio, and videos are the four categories that categorize the many types of material. The proportion of text categorized as a certain kind contributes to the elevated ratio. The objectives of false news may be categorized into three main areas: generating sensationalism, endorsing certain medications, and disseminating propaganda targeting the government as well as hospitals and medical practitioners. While the occurrence of false news is unevenly distributed and constantly changing, there is a noticeable decline in the daily frequency of such episodes, approaching a conclusion.

Research of Public News Headlines’ Bias in the Context of New Media: Using “Weibo” Hot Search Headlines as a Case Study

In recent years, the criteria for creating popular search headlines on Weibo have made it a socially controversial source of information. Using content analysis as the primary research method, this study seeks to systematically dissect the presentation of news topics within Weibo’s popular search headlines. Through a careful examination of news content, trends, and patterns, it aims to reveal the existence and dynamics of biases in news selection and presentation. Furthermore, this paper investigates the dissemination of public news and its complexity while exploring both internal and external factors contributing to deviations in the publication of public news headlines. It also strives to enrich our understanding of the significance of this platform and its potential impact on Chinese society. Insights gained from this research contribute to our understanding of the evolving patterns of news dissemination within the Chinese internet ecosystem. It underscores the importance of critically evaluating the role of digital platforms in shaping public discourse and information consumption, with a focus on bias issues and their broader societal implications.