Topic Detection in Multichannel Italian Newspapers

Abstract

Nowadays, any person, company or public institution uses and exploits different channels to share private or public information with other people (friends, customers, relatives, etc.) or institutions. This context has changed the journalism, thus, the major newspapers report news not just on its own web site, but also on several social media such as Twitter or YouTube. The use of multiple communication media stimulates the need for integration and analysis of the content published globally and not just at the level of a single medium. An analysis to achieve a comprehensive overview of the information that reaches the end users and how they consume the information is needed. This analysis should identify the main topics in the news flow and reveal the mechanisms of publication of news on different media (e.g. news timeline). Currently, most of the work on this area is still focused on a single medium. So, an analysis across different media (channels) should improve the result of topic detection. This paper shows the application of a graph analytical approach, called Keygraph, to a set of very heterogeneous documents such as the news published on various media. A preliminary evaluation on the news published in a 5 days period was able to identify the main topics within the publications of a single newspaper, and also within the publications of 20 newspapers on several on-line channels.

Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

“…The scope of assigning a news article to a crime category can be addressed following several approaches, such as text classification, community or topic detection [12][13][14][15]. In this work, we model this problem as a text classification task which consists of automatically assigning text documents to one of the predefined categories.…”

Quantifying time-dependent Media Agenda and public opinion by topic modeling

“…For example, topic modeling have been used in discourse analysis [23], analysis of social media discussions [24,25,26], or recognition of entities in news articles [27]. However, much works on topic models are also devoted to the performance of the topic model over a labeled corpus, focusing on the proper detection of the topics [28,29,30], and in general, issues about the temporal profile of topics are embedded in the context of topic tracking [31,32], or in the recognition of emerging topics in real-time [33], mostly applied to social media.…”