Web News

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Web news refers to news content that is published and disseminated through online platforms, utilizing the internet as a medium for distribution. It encompasses various formats, including articles, videos, and interactive content, and is characterized by its immediacy, accessibility, and the ability to engage audiences through multimedia and social sharing.

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Key research themes

1. How can personalization methods predict and adapt to changing user interests for effective web news recommendation?

This research area investigates approaches for building personalized news recommendation systems that capture the dynamic nature of users’ news interests. As users consume news online, their preferences evolve rapidly owing to ongoing news events and individual changes, posing challenges for systems to accurately predict current interests and recommend relevant articles. Studies in this theme seek to model user behavior from click logs, incorporate collective news trends, and combine complementary recommendation techniques to enhance user engagement with web news.

Personalized News Recommendation Based on Click Behavior

by yvette tin

2017

Key finding: By analyzing anonymized Google News click logs, this paper revealed that while individual user interests vary over time, they generally follow broader news trends. Based on this, the authors developed a Bayesian framework... Read more

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2. What roles do interactivity, immediacy, and temporal dynamics play in shaping the production, presentation, and consumption of web news?

This theme explores how the digital nature of web news influences its distinct characteristics compared to traditional print news. Key features include interactivity—enabling user participation and modification of content; immediacy—allowing faster publication cycles without strict deadlines; and the complex temporalities associated with continuous news updates and accumulation of information online. Research investigates methodological challenges in analyzing these characteristics at the news story level, how time is textually constituted on news websites over years, and the consequences for journalistic practices and reader engagement.

FREEZING THE FLOW OF ONLINE NEWS

by Federico Gatti

2019

Key finding: The study identified methods to 'freeze' the continually updating flow of online news, enabling systematic content analyses focused on interactivity and immediacy at the news story level. Empirical comparison of online news... Read more

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The shifting temporalities of online news: The Guardian's website from 1996 to 2015

by Niels Brügger

2021

Key finding: This longitudinal analysis used archived versions of The Guardian’s website to reveal how news temporalities evolved alongside digital affordances. It demonstrated that online news constituted time through overlapping textual... Read more

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Journalism on the Web

by Michael Karlsson and

2016, Journalism Studies

Key finding: The paper conceptualized four core traits—interactivity, immediacy, hypertextuality, and multimodality—as defining features of web journalism. It highlighted the expansive research on interactivity, emphasizing how increased... Read more

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3. How do algorithms and code influence the curation, diversity, and distribution of web news, and what implications does this have for users and the news ecosystem?

This area investigates the embedded journalistic decisions and influence of algorithmic systems that curate web news content, impacting diversity, attention allocation, and ultimately public discourse. It includes audits of dominant algorithmic news curation engines, analyses of the code within news recommendation apps, and studies on the role of search engines in news distribution. These insights reveal the complex ways algorithms shape what news users see, how news is categorized and prioritized, and the potential biases or editorial decisions encoded within technological infrastructures.

Search as News Curator

by Daniel Trielli

2023, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Key finding: Through an audit of Google Search’s Top Stories box, this study found a strong concentration of news sources—just twenty sources contributed over half the impressions—indicating limited source diversity in algorithmic news... Read more

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Coding the News

by Matthew Weber and

2017, Digital Journalism

Key finding: Analyzing 59 open source mobile news apps, this work uncovered how embedded code functions as technological actants that filter and prioritize news, implementing journalistic decisions algorithmically. It demonstrated that... Read more

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Web News Extraction via Path Ratios

by Gongqing Wu

2013

Key finding: The authors introduced a novel web content extraction method using Text to Tag Path Ratio (TPR) and Extended TPR features based on DOM tree structures, enabling accurate differentiation of news content from non-news elements... Read more

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4. How do users integrate online and offline news sources in their consumption patterns, especially regarding news function and media combinations?

This research theme addresses the relationship between online and offline news consumption, analyzing whether users supplement or substitute traditional media with online sources. It explores user motivations and functions (e.g., broad overview, fast updates, background information) across different outlets, as well as patterns in combining multiple media types to compose personal news diets. Findings help to understand the complex media repertoires that shape modern news consumption and inform strategies for delivering news across platforms.

Investigating people’s news diets: How online users use offline news

by Klaus Schoenbach

2022, Communications

Key finding: Based on a large-scale survey in the Netherlands, the study found that users often employ offline and online news media for similar information functions such as obtaining a broad news overview or fast updates. Rather than... Read more

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5. How can educational settings leverage web news through support systems to facilitate News-In-Education (NIE) for elementary school learners?

This research area focuses on developing web news recommendation and browsing systems tailored to the needs of elementary school teachers and students within NIE programs. It includes methods to identify suitable web news articles based on readability, local relevance, and supportive features, as well as interface designs—such as map-based browsing—to enhance engagement and comprehension. These studies address pedagogical challenges such as article selection, language difficulty, and teacher workload, aiming to utilize web news effectively as teaching materials.

Examination of a Method for Determining Web News Suitable as Teaching Materials of NIE toward News Recommendation for Elementary School Teachers

by SDIWC Organization

2019, The Fifth International Conference on Electronics and Software Science (ICESS2019)

Key finding: By analyzing features from NIE worksheets and web news articles, this study developed a Support Vector Machine-based method incorporating Word2Vec representations and difficulty measures of words to accurately classify web... Read more

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Examination of a Method for Determining Web News Suitable for NIE using Features of NIE Worksheets

by SDIWC Organization

2019, The Fourth International Conference on Electronics and Software Science (ICESS2018)

Key finding: This paper analyzed 181 articles from NIE worksheets, focusing on content categories and readability. It identified characteristic features of suitable articles and proposed an SVM-based classification method to select web... Read more

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A Support System to Browse Web News using Google Maps for Elementary School Children in Japan

by SDIWC Organization

2015

Key finding: The authors developed a system that aggregates web news and displays them geographically on Google Maps, enabling elementary school students to interactively browse local news by area. The system addresses issues like news... Read more

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All papers in Web News

Web News Extraction via Path Ratios

by Gongqing Wu

In addition to the news content, most web news pages also contain navigation panels, advertisements, related news links etc. These non-news items not only exist outside the news region, but are also present in the news content region.... more

websites’ layouts or structures. It is difficult for traditional models and techniques to distinguish non-news items from content segments. Let’s take a news article from the New York Times as an example, which appeared on October 23, 2012, as shown in Figure 1. This is a typical web news page. The content in the solid frame is web news, and the content in the dotted boxes is noise. Obviously, the banners, related links and advertisements occupy over one half of the page. Besides, we notice that the news in the solid frame also contains two non-news areas. In this paper, we propose a highly effective content extraction algorithm for distinguishing news content from non-news content effectively. Our news extraction technique is based on the potential relevance between web content layouts and their tag paths on the parsing trees, which is summarized as follows: on a typical web news page, news segments commonly have similar tag paths and contain long text and more punctuations, whereas the noise items usually have similar tag paths and contain brief text and less punctuations.

Figure 2. The DOM tree of Example 1 For convenience of description, this paper adopts the labeled ordered tree model to formalize an HTML page. According to the extraction demands, we extend the labeled ordered tree model, and meanwhile, ensure all operations could be operated on the DOM tree conveniently. If no confusion in the context, we treat the extended labeled ordered tree and the DOM tree as the same concept. Definition 1 (Extended Labeled Ordered Tree): L = {l, h, 15, ...} is a set of finite alphabetic elements, and each /; is a tag of the HTML language. The extended labeled ordered tree on L is defined as a 7-tuple expression T = (V, E, vo, <, L, U(*), c(*)). In this expression, V is a set of nodes, E is a set of edges, ve V, < is the sibling relationship set of nodes, and G = (V, E, vo, <) is an ordered tree with vo as the root. Mapping /: V — L is a label function, Vve V, I(v) is the label of node v. Mapping c: V > String is a content function, Vve V, c(v) represents the content of node v.

Figure 5. TPR of each node from the web news in Figure 1

Figure 6. ETPR of each node from the web news in Figure 1 A. CONTENT EXTRACTION Figure 6 shows the ETPR-histogram ordering by preorder traversal of the text nodes for the page in Figure 1. It is similar to Figure 5, the tag paths having a common ETPR value gather together, which indicates that content nodes gather together and noise nodes also gather together. Compared to Figure 5, ETPR is better suited for identifying web news because of the more obvious difference between sections. The difference between the two calculation methods is discussed in detail in Section 5.

Compared to Figure 6, H' shown in Figure 7 is better suited for distinguishing content segments from noise segments. H' has a lower variance because outlying peaks and valleys are smoothed. Similarly, the important short texts, such as internal-links, that may occupy a single low-ETPR node among many high-ETPR nodes, are smoothed to a higher value than the threshold. Therefore smoothing increases the cohesiveness within segments and also the differences between segments. 4.3 Extraction Algorithm CEPR

Figure 9. Precision, Recall and F-score tradeoff for NY Post as the threshold coefficient (4) increases from 0 to 2 After one webpage is parsed into a tree, we can calculate a TPR or ETPR histogram easily. Content nodes are often with high values and noise nodes have low values. But how to make a good use of high values from low values? In other words, how to distinguish content nodes from noise nodes? In this paper, we use a threshold technique. According to the above definitions of precision, recall and F-score, we find that with the increase of threshold 1, there might be a corresponding increase of precision and a sharp decrease of recall. Then how to select an available t to balance the recall and precision to get the highest F-score is considered to be a key issue. Common threshold selection methods include the middle value, the average value and the standard deviation. However, from ETPR histograms, we can figure out that the number of lower values in ETPR is far more than the higher ones. The middle value might be affected by low values, and the average value might be affected by high values, so we choose the standard deviation o to set the threshold. We set t=Jo, where 4 is a threshold parameter. Figure 9 shows the tradeoff of precision, recall and F-score along with 2 when extracting the source of the NY Post corpora. We can see that with the J increasing, the recall reduces and the precision increases. When A=0 all text nodes are

The results shown in Figure 10 demonstrate that the smoothed extractions outperform unsmoothed extraction. During the smoothing process, with the increase of a, the average P increases but average R decreases, and the highest average F (92.60%) is obtained when a is set as 1.

Table 1. The top 5 most popular news websites and their estimated unique monthly visitors Besides news content, a typical web news page contains title banners, advertisements, related links, copyrights and disclaimer notices. These additional non-news items, which are also known as noise, are responsible for roughly 40-50% of content on news websites [1]. Therefore, many web applications need to clean up news web pages. For instance, with highly-cleaned news inputted, the quality of web news summaries will be improved [2]; pocket- sized devices with small screens like mobile phones or PADs will achieve a better effect of user experience. In addition, the noise costs more data storage space and more computing time in web information retrieval, web content management and analysis, and reduces the quality of service at the same time.

CEPR is implemented in Java, and uses JTidy and HTMLParser components for cleaning and parsing web pages. Incorrect tags, wrong HTML coding and other errors that exist on web pages often make HTMLParser fail to parse. We use JTidy to fix these errors and translate them into standard XHTML documents. After parsing a webpage into a DOM tree, we calculate the text to tag path ratios by ignoring empty text nodes. In the extraction process. we remove the HTML tags, redundant blanks and empty lines from the content. Combining threshold setting with Gaussian smoothing weighted by the tag path edit distance together, we give our content extraction algorithm with the TPR/ETPR basis in Figure 8.

Table 2. Precision, Recall, F and Score for each algorithm on each source. W inners are in bold.

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A Support System to Browse Web News using Google Maps for Elementary School Children in Japan

by SDIWC Organization

Education that takes advantage of the newspaper as teaching materials called NIE (Newspaper In Education) is attracting attention in many elementary schools around the world. In general, teachers often take up the features and the events... more

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Web News

Key research themes

1. How can personalization methods predict and adapt to changing user interests for effective web news recommendation?

2. What roles do interactivity, immediacy, and temporal dynamics play in shaping the production, presentation, and consumption of web news?

3. How do algorithms and code influence the curation, diversity, and distribution of web news, and what implications does this have for users and the news ecosystem?

4. How do users integrate online and offline news sources in their consumption patterns, especially regarding news function and media combinations?

5. How can educational settings leverage web news through support systems to facilitate News-In-Education (NIE) for elementary school learners?

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