When an event occurs in the real world, numerous news reports describing this event start to appear on different news sites within a few minutes of the event occurrence. This may result in a huge amount of information for users, and automated processes may be required to help manage this information. In this paper, we describe a clustering system that can cluster news reports from disparate sources into event-centric clusters—i.e., clusters of news reports describing the same event. A user can identify any RSS feed as a source of news he/she would like to receive and our clustering system can cluster reports received from the separate RSS feeds as they arrive without knowing the number of clusters in advance. Our clustering system was designed to function well in an online incremental environment. In evaluating our system, we found that our system is very good in performing fine-grained clustering, but performs rather poorly when performing coarser-grained clustering.

News agencies work around the clock to report critical news such as earthquakes. We investigate the relationship between online news articles and seismic events that happen around the world in real time. We utilize computer text mining tools to automatically harvest, identify, cluster and extract information from earthquake-related reports, and carry out cross-validation on the mined information. Earthquake parameters retrieved from the United States Geological Survey (USGS) Application Programming Interface (API) are organized into earthquake events, with each event consisting of daily earthquake readings taking place in a particular geographical location. The results are then visualized on a user-friendly dashboard. 268,182 news reports published by 23 news agencies from different parts of the world and 14,717 earthquakes of magnitude ranging from 4 to 8.2 listed in the bulletin were processed during a 1-year study between 2018 and 2019. 1.25% of the analyzed articles had the word “quake” and 0.4% were clustered and then mapped to an earthquake event. The use of multilingual news sources from 16 countries (6 languages) gives the advantage of reducing potential news bias originating from English-written reports only. The mapping of articles with an earthquake catalog helps verify earthquake reports and determine relationships. We find that the distribution of the reported seismicity is from earthquakes that occur on or very close to land. We propose a general relationship between the number of news agencies, the earthquake magnitude and the anticipated number of published articles. News reports tend to mention higher earthquake magnitudes than those in the USGS earthquake catalog, and the reports on earthquakes can last from a few days to a couple of weeks following the earthquake.

Topic Detection and Tracking (TDT) on Twitter emulates human identifying developments in events from a stream of tweets, but while event participants are important for humans to understand what happens during events, machines have no knowledge of them. Our evaluation on football matches and basketball games shows that identifying event participants from tweets is a difficult problem exacerbated by Twitter’s noise and bias. As a result, traditional Named Entity Recognition (NER) approaches struggle to identify participants from the pre-event Twitter stream. To overcome these challenges, we describe Automatic Participant Detection (APD) to detect an event’s participants before the event starts and improve the machine understanding of events. We propose a six-step framework to identify participants and present our implementation, which combines information from Twitter’s pre-event stream and Wikipedia. In spite of the difficulties associated with Twitter and NER in the challenging context of events, our approach manages to restrict noise and consistently detects the majority of the participants. By empowering machines with some of the knowledge that humans have about events, APD lays the foundation not just for improved TDT systems, but also for a future where machines can model and mine events for themselves.