The use of online social networks has made significant progress in recent years as the use
of the Internet has become widespread worldwide as the technological infrastructure and the …

Social media platforms provide active communication channels during mass convergence
and emergency events such as disasters caused by natural hazards. As a result, first …

This paper employs deep learning in detecting the traffic accident from social media data.
First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan …

Centrality is one of the most studied concepts in social network analysis. There is a huge
literature regarding centrality measures, as ways to identify the most relevant users in a …

Social media is an invaluable source of time-critical information during a crisis. However,
emergency response and humanitarian relief organizations that would like to use this …

The importance of timely, accurate and effective use of available information is essential to
the proper management of emergency situations. In recent years, emerging technologies …

Social networks have been recently employed as a source of information for event detection,
with particular reference to road traffic congestion and car accidents. In this paper, we …

Enabling situation awareness in a given environment thus relies on being able to identify an
appropriate set of perception elements, coupled with higher-level comprehension patterns …

Twitter® 1 is a massive social networking site tuned towards fast communication. More than
140 million active users publish over 400 million 140-character “Tweets” every day. 2 …

Event detection from tweets is an important task to understand the current events/topics
attracting a large number of common users. However, the unique characteristics of tweets …