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Article
Publication date: 11 November 2020

Muzammil Khan, Sarwar Shah Khan, Arshad Ahmad and Arif Ur Rahman

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

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Article
Publication date: 1 August 2001

Raija Lehtokangas and Kalervo Järvelin

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…

Abstract

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.

Details

Journal of Documentation, vol. 57 no. 4
Type: Research Article
ISSN: 0022-0418

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Article
Publication date: 1 May 2006

Shiyan Ou, Christopher S.G. Khoo and Dion H. Goh

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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

Originality/value

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

Details

Aslib Proceedings, vol. 58 no. 3
Type: Research Article
ISSN: 0001-253X

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Article
Publication date: 1 June 2015

Anca C. Micu and Iryna Pentina

The purpose of this paper is to examine the applicability of the economics of information-driven product categorization – search vs experience products – when…

Abstract

Purpose

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.

Design/methodology/approach

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

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.

Research limitations/implications

Findings limited to the four product categories and student participants.

Practical implications

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.

Originality/value

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.

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Article
Publication date: 11 September 2019

Duen-Ren Liu, Yu-Shan Liao and Jun-Yi Lu

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Industrial Management & Data Systems, vol. 119 no. 8
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 3 April 2009

Maria Soledad Pera and Yiu‐Kai Ng

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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

Research limitations/implications

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

Originality/value

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.

Details

International Journal of Web Information Systems, vol. 5 no. 1
Type: Research Article
ISSN: 1744-0084

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Article
Publication date: 1 December 1997

Da‐Wei Chang, Ing‐Chou Chen, Hao‐Ren Ke and Ruei‐Chuan Chang

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…

Abstract

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.

Details

Internet Research, vol. 7 no. 4
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 18 May 2021

Yafei Zhang and Chuqing Dong

This study aims to explore multifaceted corporate social responsibility (CSR) covered in popular English newspapers in the UK, USA, mainland China and Hong Kong from 2000…

Abstract

Purpose

This study aims to explore multifaceted corporate social responsibility (CSR) covered in popular English newspapers in the UK, USA, mainland China and Hong Kong from 2000 to 2016 via a computer-assisted analytical approach. This study moves the understanding of CSR away from corporate self-reporting to the mass media and raises interesting questions about the role of the news media in presenting CSR as a multifaceted, socially constructed concept.

Design/methodology/approach

Data were retrieved from CSR-related news articles from 2000 to 2016 that were archived in the LexisNexis database. Guided by the theoretical framework of agenda setting, a computer-assisted content analysis (Latent Dirichlet Allocation) was used to analyze 4,487 CSR-related articles from both business and non-business news sources. Analysis of variance was used to compare salient CSR topics in each country/region.

Findings

This study identifies newspapers as an alternate to corporations’ attempts to distribute CSR information and construct CSR meaning. The findings revealed that the news communicates a variety of CSR issues that are aligned or beyond what CSR was defined in corporate CSR reporting, as suggested in previous studies. In addition, CSR news coverages differ between the business and nonbusiness news sources. Furthermore, the media tone of CSR coverage significantly differed across the regions and between the business and nonbusiness newspapers.

Social implications

Emerging topics in CSR news coverage, such as business education, could help companies identify untapped CSR realms in the market.

Originality/value

This study contributes to CSR communication research by adding a non-corporate perspective regarding what CSR means and should be focused on. The news media presents CSR using a heterogeneous approach as they not only provide surface reports on corporations’ CSR activities but also offer in-depth discussions.

Details

Journal of Global Responsibility, vol. 12 no. 2
Type: Research Article
ISSN: 2041-2568

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Article
Publication date: 27 February 2020

Shan Lei and Yafei Zhang

This study aims to understand how media content and media sentiment in corporate social responsibility (CSR) news coverage affect investment performance, as reflected in…

Abstract

Purpose

This study aims to understand how media content and media sentiment in corporate social responsibility (CSR) news coverage affect investment performance, as reflected in the S&P 500 Environmental and Socially Responsible Index from 2010 to 2016.

Design/methodology/approach

Computer-assisted content analysis and sentiment analysis are employed to analyze 818 CSR-related newspaper articles from mainstream newspapers. Autoregressive model is used to comprehend socially responsible investment (SRI) performance.

Findings

This study reveals the impact of media content and media sentiment of CSR-related news articles on SRI. The authors’ findings indicate that such topics as recognition of a company's CSR contributions in CSR-related news articles are positively associated with SRI performance, whereas topics such as tax avoidance and environmental protection show a negative relationship with SRI performance. In addition, this study contributes to the authors’ understanding of framing bias in investment by confirming a significant positive association between an uncertain or constraining media sentiment and SRI performance, as well as a negative relationship between a litigious sentiment and SRI performance.

Originality/value

There has been limited attention to examining the effect of media coverage of CSR on the financial market. Since SRI is one of the most useful financial indices for SRIs, it is meaningful to explore the relationship between media coverage of CSR and SRI. To fill the research gap, this study specifically examines how media coverage of CSR-related issues is associated with SRI performance.

Details

International Journal of Bank Marketing, vol. 38 no. 4
Type: Research Article
ISSN: 0265-2323

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Article
Publication date: 4 September 2017

Jia-Lang Seng and Hsiao-Fang Yang

The purpose of this study is to develop the dictionary with grammar and multiword structure has to be used in conjunction with sentiment analysis to investigate the…

Abstract

Purpose

The purpose of this study is to develop the dictionary with grammar and multiword structure has to be used in conjunction with sentiment analysis to investigate the relationship between financial news and stock market volatility.

Design/methodology/approach

An algorithm has been developed for calculating the sentiment orientation and score of data with added information, and the results of calculation have been integrated to construct an empirical model for calculating stock market volatility.

Findings

The experimental results reveal a statistically significant relationship between financial news and stock market volatility. Moreover, positive (negative) news is found to be positively (negatively) correlated with positive stock returns, and the score of added information of the news is positively correlated with stock returns. Model verification and stock market volatility predictions are verified over four time periods (monthly, quarterly, semiannually and annually). The results show that the prediction accuracy of the models approaches 66% and stock market volatility with a particular trend-predicting effect in specific periods by using moving window evaluation.

Research limitations/implications

Only one news source is used and the research period is only two years; thus, future studies should incorporate several data sources and use a longer period to conduct a more in-depth analysis.

Practical implications

Understanding trends in stock market volatility can decrease risk and increase profit from investment. Therefore, individuals or businesses can feasibly engage in investment activities for profit by understanding volatility trends in capital markets.

Originality/value

The ability to exploit textual information could potentially increase the quality of the data. Few scholars have applied sentiment analysis in investigating interdisciplinary topics that cover information management technology, accounting and finance. Furthermore, few studies have provided support for structured and unstructured data. In this paper, the efficiency of providing the algorithm, the model and the trend in stock market volatility has been demonstrated.

Details

Kybernetes, vol. 46 no. 8
Type: Research Article
ISSN: 0368-492X

Keywords

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