The Topy system automates real-time story tracking by utilizing crowdsourced tagging on social media platforms. Topy employs a state-of-the-art Twitter hashtag recommender to continuously annotate news articles with hashtags, a rich meta-data source that allows connecting articles under drastically different timelines than typical keyword based story tracking systems. Employing social tags for ...

After the beginning of the extension of current Web towards the semantics, the annotation starts to take a significant role, since it takes part to give the semantic aspect to the different types of documents. With the proliferation of news articles from thousands of different sources now available on the Web, summarization of such information is becoming increasingly important. We will define ...

Nowadays there are thousands of news sites available on-line. Traditional methods to access this huge news repository are overwhelmed. In this paper we present NowOnWeb, a news retrieval system that crawls the articles from the internet publishers and provides news searching and browsing.

The main purpose of topic detection and tracking (TDT) is to detect, group, and organize newspaper articles reporting on the same event. Since an event is a reported occurrence at a specific time and place and the unavoidable consequences, TDT can benefit from an explicit use of time and place information. In this work, we focused on place information, using time information as in the previous ...

Traditional phrase matching approaches, which can discover documents containing exactly the same phrases, fail to detect documents including phrases that are semantically relevant, but not exact matches. We propose a correlation-based phrase matching (CPM) model that can detect RSS news articles which contain not only phrases that are exactly the same but also semantically relevant, which dicta...

Given the wide spread of web based tools and social news media services which are facilitating grassroots journalism, there is a growing interest in selecting credible news content among a huge number of articles. Currently, most of social news services rely on reader votes to select articles for their front pages. However, the fundamental problem is that users’ votes often stand for popularity...

We study the news reading behaviour 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 storyfocused reading on user engagement and how news sites can support this phenomenon. We found that most us...

News portals are a popular destination for web users who read news online. News providers are therefore greatly interested in attaining higher visitor rates and promoting greater engagement with their content. One aspect of user engagement deals with keeping users on site longer, by allowing them to navigate through enhanced click-through experiences. Therefore, news portals have invested in wa...

This thesis tries to answer the question how to predict the reaction of the stock market to news articles using the latest suitable developments in Natural Language Processing. This is done using text classification where a new article is matched to a category of articles which have a certain influence on the stock price. The thesis first discusses why analysis of news articles is a feasible ap...

News recommendation has been a must-have service for most mobile device users to know what has happened in the world. In this paper, we focus on recommending latest news articles to new users, which consists of the new user coldstart challenge and the new item (i.e., news article) coldstart challenge, and is thus termed as dual cold-start recommendation (DCSR). As a response, we propose a solut...