The sheer amount of newsworthy information published by users in social media platforms makes it necessary to have efficient and effective methods to filter and organize content. In this scenario, off-the-shelf methods fail to process large amounts of data, which is usually approached by adding more computational resources. Simple data aggregations can help to cope with space and time constraints, while at the same time improve the effectiveness of certain applications, such as topic detection or summarization. We propose a lightweight representation of newsworthy social media data. The proposed representation leverages microblog features, such as redundancy and re-sharing capabilities, by using surrogate texts from shared URLs and word embeddings. Our representation allows us to achieve comparable clustering results to those obtained by using the complete data, while reducing running time and required memory. This is useful when dealing with noisy and raw user-generated social media data.

Online Social Networks (OSN) have changed the way information is produced and consumed. Organizing and retrieving unstructured data extracted from these platforms is not an easy task. Galean is a visual and interactive tool that aims to help journalists and historians, among others, analyze news events discussed on Twitter. In this tool, news events are visually represented by the very countries from where the news originated, the date when they happened and their impact in the OSN. Galean considers countries as entities, as opposed to mere geographical locations as most of the tools in the state of the art. As a consequence, it allows users to explore and retrieve news not only by their geographical and temporal context, but also by the relationship among countries. With this tool users can search for behavioral patterns of news events and observe how countries are associated in specific events. We expect our work to become a public tool that helps conduct historical analyses of social media news coverage over time.

Nowadays, social media services are being used extensively as news sources and for spreading information on real-world events. Several studies have focused on detecting those events and locating them geographically. However, in order to study real-world events, for example, finding relationships between locations or detecting high impact events based on their coverage, we need more suitable models to represent events. In this work we propose a simple model to represent real-world news events using two sources of information: the locations that are mentioned in the event (where the event occurs), and the locations of users that discuss or comment on it. We then characterize a country based on the amount of events in which that country is mentioned and also participates on the event. We show some applications of the model: we find clusters of news events based on the level of participation of countries, identifying global and impactful events in certain areas. Also, we show groups of similar countries, finding promising insights about their relationships. This model can be useful at finding unsuspected relations among countries based on the news coverage and country participation, identifying different levels of news coverage in the world, and finding bias in international news sources.