Cities have been a thriving place for citizens over the centuries due to
their complex infrastructure. The emergence of the Cyber-Physical-Social
Systems (CPSS) and context-aware technologies boost a growing interest in
analysing, extracting and eventually understanding city events which
subsequently can be utilised to leverage the citizen observations of their
cities. In this paper, we investigate the feasibility of using Twitter textual
streams for extracting city events. We propose a hierarchical multi-view deep
learning approach to contextualise citizen observations of various city systems
and services. Our goal has been to build a flexible architecture that can learn
representations useful for tasks, thus avoiding excessive task-specific feature
engineering. We apply our approach on a real-world dataset consisting of event
reports and tweets of over four months from San Francisco Bay Area dataset and
additional datasets collected from London. The results of our evaluations show
that our proposed solution outperforms the existing models and can be used for
extracting city related events with an averaged accuracy of 81% over all
classes. To further evaluate the impact of our Twitter event extraction model,
we have used two sources of authorised reports through collecting road traffic
disruptions data from Transport for London API, and parsing the Time Out London
website for sociocultural events. The analysis showed that 49.5% of the Twitter
traffic comments are reported approximately five hours prior to the authorities
official records. Moreover, we discovered that amongst the scheduled
sociocultural event topics; tweets reporting transportation, cultural and
social events are 31.75% more likely to influence the distribution of the
Twitter comments than sport, weather and crime topics
