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This article investigates how consistent different newspapers are in their choice of words when writing about the same news events. News articles on the same news events were taken from three Finnish newspapers and compared in regard to their central concepts and words representing the concepts in the news texts. Consistency figures were calculated for each set of three articles (the total number of sets was sixty). Inconsistency in words and concepts was found between news articles from different newspapers. The mean value of consistency calculated on the basis of words was 65 per cent; this however depended on the article length. For short news wires consistency was 83 per cent while for long articles it was only 47 per cent. At the concept level, consistency was considerably higher, ranging from 92 per cent to 97 per cent between short and long articles. The articles also represented three categories of topic (event, process and opinion). Statistically significant differences in consistency were found in regard to length but not in regard to the categories of topic. We argue that the expression inconsistency is a clear sign of a retrieval problem and that query expansion based on semantic relationships can significantly improve retrieval performance on free‐text sources.

The World Wide Web has become an essential platform for a news publication, and it has become one of the primary sources of information dissemination in the past few years. Electronic media, i.e., television channels, magazines and newspapers, have started publishing news online. This online information is prompt to be disappeared because of short life-span and imperative to be archived for the long-term and future generations. This paper presents a content-based similarity measure based on the headings of the news articles for linking digital news stories published in various newspapers during the preservation process that helps to ensure future accessibility.

To evaluate the accuracy and assess the effectiveness and worth of the proposed measure for linking news articles in Digital News Story Archive (DNSA), we adopted both, system-centric and user-centric (human judgment) evaluation over different datasets of news articles.

The proposed similarity measure is evaluated using different sizes of datasets, and the results are compared by both user-centric technique, i.e., expert judgment and system-centric techniques, i.e., cosine similarity measure, extended Jaccard measure and common ratio measure for stories (CRMS). The comparison helps to get a broader impact and can be helpful for generalization of the measure for different categories of news articles. Multiple experiments have conducted the findings of which showed that the measure presented viable results for national and international news, while best results for linking sports news articles during preservation based on headings.

The DNSA preserves a huge number of news articles from multiple news sources and to link with a vast collection, which encourages to introduce an efficient linking mechanism with few terms to manipulate. The CRMS is modified to deal with the headings of news articles as a part of the digital news stories preservation framework and comprehensively analysed.

The purpose of this research is to develop a method for automatic construction of multi‐document summaries of sets of news articles that might be retrieved by a web search engine in response to a user query.

Based on the cross‐document discourse analysis, an event‐based framework is proposed for integrating and organizing information extracted from different news articles. It has a hierarchical structure in which the summarized information is presented at the top level and more detailed information given at the lower levels. A tree‐view interface was implemented for displaying a multi‐document summary based on the framework. A preliminary user evaluation was performed by comparing the framework‐based summaries against the sentence‐based summaries.

In a small evaluation, all the human subjects preferred the framework‐based summaries to the sentence‐based summaries. It indicates that the event‐based framework is an effective way to summarize a set of news articles reporting an event or a series of relevant events.

Limited to event‐based news articles only, not applicable to news critiques and other kinds of news articles. A summarization system based on the event‐based framework is being implemented.

Multi‐document summarization of news articles can adopt the proposed event‐based framework.

An event‐based framework for summarizing sets of news articles was developed and evaluated using a tree‐view interface for displaying such summaries.

The purpose of this paper is to examine the applicability of the economics of information-driven product categorization – search vs experience products – when investigating online brand advertising and news synergies.

Randomized controlled post-test experiment with over 400 participants in three treatment groups involving exposures to paid advertising (banner ad-plus-banner ad) and publicity (news article-plus-banner ad and banner ad-plus-news article) for four products. Questionnaire upon web site exit tested differences in brand attitudes among treatment groups and product categories.

Findings indicate that including news about the brand in the online brand communication mix – either before or after ads – generates higher brand attitude scores for experience products. For search products sequence matters and brand attitudes are more positive when consumers are exposed to news articles first followed by advertisements.

Findings limited to the four product categories and student participants.

When promoting search goods online, brand managers should include publicity only before display advertising efforts. For experience goods, publicity generates higher brand attitude scores when included either before or while running display advertising.

First study examining online publicity and advertising synergies from an economics of information theory perspective separating search from experience goods when promoting new/unknown brands online. In the online environment, the line between journalistic/news and promotional/advertising text-based content has become increasingly blurred. Compared to paid online advertising, using third-party attributed communications sources like publicity increases message credibility. Adding product-related news and blog articles to banner advertisements may benefit from synergistic effects and have consumers process the brand message more extensively. The order of exposure to the different brand messages matters when promoting search as opposed to experience products online.

Tens of thousands of news articles are posted online each day, covering topics from politics to science to current events. To better cope with this overwhelming volume of information, RSS (news) feeds are used to categorize newly posted articles. Nonetheless, most RSS users must filter through many articles within the same or different RSS feeds to locate articles pertaining to their particular interests. Due to the large number of news articles in individual RSS feeds, there is a need for further organizing articles to aid users in locating non‐redundant, informative, and related articles of interest quickly. This paper aims to address these issues.

The paper presents a novel approach which uses the word‐correlation factors in a fuzzy set information retrieval model to: filter out redundant news articles from RSS feeds; shed less‐informative articles from the non‐redundant ones; and cluster the remaining informative articles according to the fuzzy equivalence classes on the news articles.

The clustering approach requires little overhead or computational costs, and experimental results have shown that it outperforms other existing, well‐known clustering approaches.

The clustering approach as proposed in this paper applies only to RSS news articles; however, it can be extended to other application domains.

The developed clustering tool is highly efficient and effective in filtering and classifying RSS news articles and does not employ any labor‐intensive user‐feedback strategy. Therefore, it can be implemented in real‐world RSS feeds to aid users in locating RSS news articles of interest.

The purpose of this study is to analyze the topics of COVID-19 news articles for better obtaining the relationship among and the evolution of news topics, helping to manage the infodemic from a quantified perspective.

To analyze COVID-19 news articles explicitly, this paper proposes a prism architecture. Based on epidemic-related news on China Daily and CNN, this paper identifies the topics of the two news agencies, elucidates the relationship between and amongst these topics, tracks topic changes as the epidemic progresses and presents the results visually and compellingly.

The analysis results show that CNN has a more concentrated distribution of topics than China Daily, with the former focusing on government-related information, and the latter on medical. Besides, the pandemic has had a big impact on CNN and China Daily's reporting preference. The evolution analysis of news topics indicates that the dynamic changes of topics have a strong relationship with the pandemic process.

This paper offers novel perspectives to review the topics of COVID-19 news articles and provide new understandings of news articles during the initial outbreak. The analysis results expand the scope of infodemic-related studies.

The media is an important actor in public procurement, but research on its role is limited. This paper aims to investigate how the media has engaged with public procurement, using UK newspapers as a case example.

The method consisted of searching Nexis database for news articles on public procurement; automatic extraction of article attributes such as length, section, authorship; and manually coding each article for its theme and industry context. This produced quantitative indicators about the extent and focus of press coverage on public procurement.

Press coverage of public procurement increased between 1985 and 2018. The focus of coverage has been on governance failure and socio-economic policy. Governance failure, which includes corruption, cronyism and supplier malpractice, is associated with construction, outsourcing and professional services sectors. Socio-economic policy, which includes supporting small suppliers and favouring domestic industry, is associated with manufacturing, defence and agriculture.

The analysis included UK media only. While the trends observed on the extent and focus of public procurement news coverage likely reflect the situation in other countries, international comparative research is still required.

Government officials should be more proactive in countering the “negativity bias” in news coverage of public procurement by showcasing projects where value-for-money has been achieved, services have been successfully delivered and social value has been realised.

The media accentuates the negatives of public procurement and omits positive developments. The end-result is a selective and, at times, self-serving media narrative that is likely to engender cynicism towards public procurement.

To the best of the authors’ knowledge, this is the first study on media coverage of public procurement. It highlights that while there are similarities between media and academic treatment of public procurement, particularly in relation to its socio-economic side, the media emphasises governance failings and negative developments to a greater extent.

Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms.

A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing.

This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation.

The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

With the speedy growth of information quantity, people need a mechanism to discover automatically the information that interests them. Such a mechanism is called selective dissemination of information (SDI). Describes the design and implementation of an SDI system with the ability of delivering real‐time, personalized news articles. In addition to delivering English news, it delivers Chinese articles also. Focuses on the problems that other researches seldom address. First, discusses how to store and delete news articles efficiently, then describes the user model to let users specify their interests. Finally, presents an efficient method to embed the ability to deliver Chinese as well as English news articles in the system.

To provide human judgment input for computer algorithm development, this study examines the relative importance of source, content, and style cues in predicting the truthfulness ratings of two common types of online health information: news stories and institutional news releases.

This study employed a multi-method approach using (1) a manual content analysis of 400 randomly selected online health news stories and news releases from HealthNewsReview.org and (2) an online experiment comparing truthfulness ratings between news stories and news releases.

Using content analysis, the authors found significant differences in the importance of source, content, and style cues in predicting truthfulness ratings of news stories and news releases: source and style cues predicted truthfulness ratings better than content cues. In the experiment, source credibility was the most important predictor of truthfulness ratings, controlling for individual differences. Experts have higher ratings for news media stories than news releases and lay people have no differences in rating the two news formats.

It is important for health educators to curb consumer trust in misinformation and increase health information literacy. Rather than solely reporting scientific evidence, educators should focus on addressing cues people use to judge the truthfulness of health information.

This is the first study that directly compares human judgments of health news stories and news releases. Using both the breadth of content analysis and experimental causality testing, the authors evaluate the relative importance of source, content, and style cues in predicting truthfulness ratings.