Search (550 results, page 1 of 28)

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Date

19. 1.2019 17:42:22

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Abstract

Are our memories of the world well described by the international news coverage in our country? If so, sources central to international news may also be central to international recall patterns; in particular, they may reflect an American-centric focus, given the previously proposed central U.S. position in the news marketplace. We asked people of four different nationalities (China, Israel, Switzerland, and the United States) to list all the countries they could name. We also constructed a network representation of the world for each nation based on the co-occurrence pattern of countries in the news. To compare news and memories, we developed a computational model that predicts the recall order of countries based on the news networks. Consistent with previous reports, the U.S. news was central to the news networks overall. However, although national recall patterns reflected their corresponding national news sources, the Chinese news was substantially better than other national news sources at predicting both individual and aggregate memories across nations. Our results suggest that news and memories are related but may also reflect biases in the way information is transferred to long-term memory, potentially biased against the transient coverage of more "free" presses. We discuss possible explanations for this "Chinese news effect" in relation to prominent cognitive and communications theories.

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Abstract

Purpose - The purpose of this paper is to analyze the disciplinary orientation of scientific publications that were mentioned on different social media platforms, focussing on their differences and similarities with citation counts. Design/methodology/approach - Social media metrics and readership counts, associated with 500,216 publications and their citation data from the Web of Science database, were collected from Altmetric.com and Mendeley. Results are presented through descriptive statistical analyses together with science maps generated with VOSviewer. Findings - The results confirm Mendeley as the most prevalent social media source with similar characteristics to citations in their distribution across fields and their density in average values per publication. The humanities, natural sciences, and engineering disciplines have a much lower presence of social media metrics. Twitter has a stronger focus on general medicine and social sciences. Other sources (blog, Facebook, Google+, and news media mentions) are more prominent in regards to multidisciplinary journals. Originality/value - This paper reinforces the relevance of Mendeley as a social media source for analytical purposes from a disciplinary perspective, being particularly relevant for the social sciences (together with Twitter). Key implications for the use of social media metrics on the evaluation of research performance (e.g. the concentration of some social media metrics, such as blogs, news items, etc., around multidisciplinary journals) are identified.

Date

20. 1.2015 18:30:22

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Abstract

Prominent news sites on the web provide hundreds of news articles daily. The abundance of news content competing to attract online attention, coupled with the manual effort involved in article selection, necessitates the timely prediction of future popularity of these news articles. The future popularity of a news article can be estimated using signals indicating the article's penetration in social media (e.g., number of tweets) in addition to traditional web analytics (e.g., number of page views). In practice, it is important to make such estimations as early as possible, preferably before the article is made available on the news site (i.e., at cold start). In this paper we perform a study on cold-start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News, a major news provider. We characterize the popularity of news articles through a set of online metrics and try to predict their values across time using machine learning techniques on a large collection of features obtained from various sources. Our findings indicate that predicting news popularity at cold start is a difficult task, contrary to the findings of a prior work on the same topic. Most articles' popularity may not be accurately anticipated solely on the basis of content features, without having the early-stage popularity values.

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Source

http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja

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Abstract

The delivery of personalized news content depends on the ability to predict user interests. We evaluated different methods for acquiring user profiles based on declared and actual interest in various news topics and items. In an experiment, 36 students rated their interest in six news topics and in specific news items and read on 6 days standard, nonpersonalized editions and personalized (basic or adaptive) news editions. We measured subjective satisfaction with the editions and expressed preferences, along with objective measures, to infer actual interest in items. Users' declared interest in news topics did not strongly predict their actual interest in specific news items. Satisfaction with all news editions was high, but participants preferred the personalized editions. User interest was weakly correlated with reading duration, article length, and reading order. Different measures predicted interest in different news topics. Explicit measures predicted interest in relatively clearly defined topics such as sports, but were less appropriate for broader topics such as science and technology. Our results indicate that explicit and implicit methods should be combined to generate user profiles. We suggest that a personalized newspaper should contain both general information and personalized items, selected based on specific combinations of measures for each of the different news topics. Based on the findings, we present a general model to decide on the personalization of news content to generate personalized editions for readers.

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Abstract

Previous studies of user-generated vocabularies (e.g., Golder & Huberman, 2006; Munk & Mork, 2007b; Yoon, 2009) have proposed that a primary source of tag agreement across users is due to wide-spread use of tags at the basic level of abstraction. However, an investigation of levels of abstraction in user-generated tagging vocabularies did not support this notion. This study analyzed approximately 8000 tags generated by 40 subjects. Analysis of 7617 tags assigned to 36 online resources representing four content categories (TOOL, FRUIT, CLOTHING, VEHICLE) and three resource genres (news article, blog, ecommerce) did not find statistically significant preferences in the assignment of tags at the superordinate, subordinate or basic levels of abstraction. Within the framework of Heidegger's (1953/1996) notion of handiness , observed variations in the preferred level of abstraction are both natural and phenomenological in that perception and understanding -- and thus the meaning of "things" -- arise out of the individual's contextualized experiences of engaging with objects. Operationalization of superordinate, subordinate and basic levels of abstraction using Heidegger's notion of handiness may be able to account for differences in the everyday experiences and activities of taggers, thereby leading to a better understanding of user-generated tagging vocabularies.

Date

5. 9.2014 16:22:27

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Abstract

"Dewey Defeats Truman." "Hillary Clinton Adopts Alien Baby." Fake news may have reached new notoriety since the 2016 US election, but it has been around a long time. Whether it was an error in judgment in a rush to publish election results in November, 1948, or a tabloid cover designed to incite an eye roll and a chuckle in June, 1993, fake news has permeated and influenced culture since the inception of the printed press. But now, when almost every press conference at the White House contains a declaration of the evils of "fake news", evaluating information integrity and quality is more important than ever. In All That?s Not Fit to Print, Amy Affelt offers tools and techniques for spotting fake news and discusses best practices for finding high quality sources, information, and data. Including an analysis of the relationship between fake news and social media, and potential remedies for viral fake news, Affelt explores the future of the press and the skills that librarians will need, not only to navigate these murky waters, but also to lead information consumers in to that future. For any librarian or information professional, or anyone who has ever felt overwhelmed by the struggle of determining the true from the false, this book is a fundamental guide to facing the tides of fake news.

Content

1. Fake News: False Content in a Familiar Format; 2. How We Got Here; 3. When Sharing Is Not Caring: Fake News and Social Media; 4. How to Spot Fake News; 5. Fake News in the Field: Library Schools and Libraries; Ottawa Public Library; Vancouver Public Library; Surrey Public Library; Mississauga Public Library; Oshawa Public Library Librarian; 6. The Future of Fake News: The View from HereThe Eyes Have It; Put Your Money Where the Mouth Is; Hot Blooded? Check It and See; Go Slow-Mo; Remember the Old Standbys; Conclusion.

LCSH

Fake news

Subject

Fake news

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Abstract

Online content providers, such as news portals and social media platforms, constantly seek new ways to attract large shares of online attention by keeping their users engaged. A common challenge is to identify which aspects of online interaction influence user engagement the most. In this article, through an analysis of a news article collection obtained from Yahoo News US, we demonstrate that news articles exhibit considerable variation in terms of the sentimentality and polarity of their content, depending on factors such as news provider and genre. Moreover, through a laboratory study, we observe the effect of sentimentality and polarity of news and comments on a set of subjective and objective measures of engagement. In particular, we show that attention, affect, and gaze differ across news of varying interestingness. As part of our study, we also explore methods that exploit the sentiments expressed in user comments to reorder the lists of comments displayed in news pages. Our results indicate that user engagement can be anticipated predicted if we account for the sentimentality and polarity of the content as well as other factors that drive attention and inspire human curiosity.

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Abstract

Although the Internet has become a major source for accessing news, there is little research regarding users' experience with news sites. We conducted an experiment to test a comprehensive model of user experience with news sites that was developed previously by means of an online survey. Level of adoption (novel or adopted site) was controlled with a between-subjects manipulation. We collected participants' answers to psychometric scales at 2 times: after presentation of 5 screenshots of a news site and directly after 10 minutes of hands-on experience with the site. The model was extended with the prediction of users' satisfaction with news sites as a high-level design goal. A psychometric measure of trust in news providers was developed and added to the model to better predict people's intention to use particular news sites. The model presented in this article represents a theoretically founded, empirically tested basis for evaluating news websites, and it holds theoretical relevance to user-experience research in general. Finally, the findings and the model are applied to provide practical guidance in design prioritization.

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Abstract

Topic time reflects the temporal feature of topics in Web news pages, which can be used to establish and analyze topic models for many time-sensitive text mining tasks. However, there are two critical challenges in discovering topic time from Web news pages. The first issue is how to normalize different kinds of temporal expressions within a Web news page, e.g., explicit and implicit temporal expressions, into a unified representation framework. The second issue is how to determine the right topic time for topics in Web news. Aiming at solving these two problems, we propose a systematic framework for discovering topic time from Web news. In particular, for the first issue, we propose a new approach that can effectively determine the appropriate referential time for implicit temporal expressions and further present an effective defuzzification algorithm to find the right explanation for a fuzzy temporal expression. For the second issue, we propose a relation model to describe the relationship between news topics and topic time. Based on this model, we design a new algorithm to extract topic time from Web news. We build a prototype system called Topic Time Parser (TTP) and conduct extensive experiments to measure the effectiveness of our proposal. The results suggest that our proposal is effective in both temporal expression normalization and topic time extraction.

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Abstract

We study the news reading behavior of several hundred thousand users on 65 highly visited news sites. We focus on a specific phenomenon: users reading several articles related to a particular news development, which we call story-focused reading. Our goal is to understand the effect of story-focused reading on user engagement and how news sites can support this phenomenon. We found that most users focus on stories that interest them and that even casual news readers engage in story-focused reading. During story-focused reading, users spend more time reading and a larger number of news sites are involved. In addition, readers employ different strategies to find articles related to a story. We also analyze how news sites promote story-focused reading by looking at how they link their articles to related content published by them, or by other sources. The results show that providing links to related content leads to a higher engagement of the users, and that this is the case even for links to external sites. We also show that the performance of links can be affected by their type, their position, and how many of them are present within an article.

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Abstract

In this article, we present a new algorithm for clustering a bilingual collection of comparable news items in groups of specific topics. Our hypothesis is that named entities (NEs) are more informative than other features in the news when clustering fine grained topics. The algorithm does not need as input any information related to the number of clusters, and carries out the clustering only based on information regarding the shared named entities of the news items. This proposal is evaluated using different data sets and outperforms other state-of-the-art algorithms, thereby proving the plausibility of the approach. In addition, because the applicability of our approach depends on the possibility of identifying equivalent named entities among the news, we propose a heuristic system to identify equivalent named entities in the same and different languages, thereby obtaining good performance.

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Abstract

Although news websites are used by a large and increasing number of people, there is a lack of research within human-computer interaction regarding users' experience with this type of interactive technology. In the current research, existing measures of user-experience factors were identified and, using an online survey, answers to psychometric scales to measure website characteristics, need fulfillment, affective reactions, and constructs of technology acceptance and user experience were collected from regular users of news sites. A comprehensive user-experience model was formulated to explain acceptance and quality judgments of news sites. The main contribution of the current study is the application of influential models of user experience and technology acceptance to the domain of online news. By integrating both types of variable in a comprehensive model, the relationships between the types of variable are clarified both theoretically and empirically. Implications of the model for theory, further research, and system design are discussed.

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Abstract

Arabic news articles in electronic collections are difficult to study. Browsing by category is rarely supported. Although helpful machine-learning methods have been applied successfully to similar situations for English news articles, limited research has been completed to yield suitable solutions for Arabic news. In connection with a Qatar National Research Fund (QNRF)-funded project to build digital library community and infrastructure in Qatar, we developed software for browsing a collection of about 237,000 Arabic news articles, which should be applicable to other Arabic news collections. We designed a simple taxonomy for Arabic news stories that is suitable for the needs of Qatar and other nations, is compatible with the subject codes of the International Press Telecommunications Council, and was enhanced with the aid of a librarian expert as well as five Arabic-speaking volunteers. We developed tailored stemming (i.e., a new Arabic light stemmer called P-Stemmer) and automatic classification methods (the best being binary Support Vector Machines classifiers) to work with the taxonomy. Using evaluation techniques commonly used in the information retrieval community, including 10-fold cross-validation and the Wilcoxon signed-rank test, we showed that our approach to stemming and classification is superior to state-of-the-art techniques.

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Abstract

The worldwide span of the microblogging service Twitter provides an opportunity to make international comparisons of trending topics of interest, such as news stories. Previous international comparisons of news interests have tended to use surveys and may bypass topics not well covered in the mainstream media. This study uses 9 months of English-language Tweets from the United Kingdom, United States, India, South Africa, New Zealand, and Australia. Based upon the top 50 trending keywords in each country from the 0.5 billion Tweets collected, festivals or religious events are the most common, followed by media events, politics, human interest, and sports. U.S. trending topics have the most interest in the other countries and Indian trending topics the least. Conversely, India is the most interested in other countries' trending topics and the United States the least. This gives evidence of an international hierarchy of perceived importance or relevance with some issues, such as the international interest in U.S. Thanksgiving celebrations, apparently not being directly driven by the media. This hierarchy echoes, and may be caused by, similar news coverage trends. Although the current imbalanced international news coverage does not seem to be out of step with public news interests, the political implication is that the Twitter-using public reflects, and hence seems to implicitly accept, international imbalances in news media agenda setting rather than combating them. This is an issue for those believing that these imbalances make the media too powerful.

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Abstract

News portals are a popular destination for web users. News providers are therefore interested in attaining higher visitor rates and promoting greater engagement with their content. One aspect of engagement deals with keeping users on site longer by allowing them to have enhanced click-through experiences. News portals have invested in ways to embed links within news stories but so far these links have been curated by news editors. Given the manual effort involved, the use of such links is limited to a small scale. In this article, we evaluate a system-based approach that detects newsworthy events in a news article and locates other articles related to these events. Our system does not rely on resources like Wikipedia to identify events, and it was designed to be domain independent. A rigorous evaluation, using Amazon's Mechanical Turk, was performed to assess the system-embedded links against the manually-curated ones. Our findings reveal that our system's performance is comparable with that of professional editors, and that users find the automatically generated highlights interesting and the associated articles worthy of reading. Our evaluation also provides quantitative and qualitative insights into the curation of links, from the perspective of users and professional editors.

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Abstract

Although news stories target the general public and are sometimes inaccurate, they can serve as sources of real-world information for researchers. This article investigates the extent to which academics exploit journalism using content and citation analyses of online BBC News stories cited by Scopus articles. A total of 27,234 Scopus-indexed publications have cited at least one BBC News story, with a steady annual increase. Citations from the arts and humanities (2.8% of publications in 2015) and social sciences (1.5%) were more likely than citations from medicine (0.1%) and science (<0.1%). Surprisingly, half of the sampled Scopus-cited science and technology (53%) and medicine and health (47%) stories were based on academic research, rather than otherwise unpublished information, suggesting that researchers have chosen a lower-quality secondary source for their citations. Nevertheless, the BBC News stories that were most frequently cited by Scopus, Google Books, and Wikipedia introduced new information from many different topics, including politics, business, economics, statistics, and reports about events. Thus, news stories are mediating real-world knowledge into the academic domain, a potential cause for concern.

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Abstract

Multisource web news portals provide various advantages such as richness in news content and an opportunity to follow developments from different perspectives. However, in such environments, news variety and quantity can have an overwhelming effect. New-event detection and topic-tracking studies address this problem. They examine news streams and organize stories according to their events; however, several tracking stories of an event/topic may contain no new information (i.e., no novelty). We study the novelty detection (ND) problem on the tracking news of a particular topic. For this purpose, we build a Turkish ND test collection called BilNov-2005 and propose the usage of three ND methods: a cosine-similarity (CS)-based method, a language-model (LM)-based method, and a cover-coefficient (CC)-based method. For the LM-based ND method, we show that a simpler smoothing approach, Dirichlet smoothing, can have similar performance to a more complex smoothing approach, Shrinkage smoothing. We introduce a baseline that shows the performance of a system with random novelty decisions. In addition, a category-based threshold learning method is used for the first time in ND literature. The experimental results show that the LM-based ND method significantly outperforms the CS- and CC-based methods, and category-based threshold learning achieves promising results when compared to general threshold learning.