IDENTIFICATION OF PRACTICAL TRAFFIC VIA DIGITAL MEDIA TWITTER STREAM AND SCRUTINY

In the recent times, social networks have been extensively used as a data source for the event detection. Social networks permit people to generate an identity and allow them share it to construct a community. The resultant social network is a basis for managing of social relationships, discovering users with related interests, and locates content and knowledge entered by several users. We provide an actual monitoring scheme for traffic event recognition from the analysis of Twitter stream. The system was designed from ground as event-driven infrastructure, built on service oriented architecture and obtains tweets from Twitter based on various search criteria such as processes tweets, by application of text mining methods; and performs Tweet classification. The objective is to allocate the suitable class label to every tweet, as associated to traffic event or else not. The traffic detection system was in use for monitoring of numerous areas, allowing for recognition of traffic events more or less in real time, often prior to online web sites

Tracing the German Centennial Flood in the Stream of Tweets: First Lessons Learned

Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results con rm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions