In recent years, several technological advancements have changed the lives of millions throughout the globe. These include broadband Internet wireless access, advanced mobile platforms and smartphones including accurate global positioning system capabilities, and the introduction of social networks. The fusion of these technological advances led to the massive adoption of mobile platform-operated social networking applications and unleashed new real-time and on-site social information. The ability to generate content anywhere and anytime leads to a detectable projection of real-life events on geo-social networks (GSN). For example, in preparation for a rally, the geo-social activity may precede the actual event, allowing predictive capabilities. Alternatively, in a natural event such as a wildfire, early content generated in the proximity of the event may allow early identification of the event and the assessment of its physical boundaries. In this article, we propose to use the massive and rapidly accumulating information communicated within GSN to identify and track major events and present a proof of concept. We discuss means and methods to retrieve relevant information from the networks, through a set of adequate spatial, temporal, and textual filters. Our preliminary empirical results corroborate our assumptions and show that major events may have detectable “abnormal” impact on GSN activities, which allows prompt identification and real-time tracking. Our approach is expected to pave the way to the development of real-time systems and algorithms for early identification and geographical tracking of major events.

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The volume of information generated by social and cellular networks has significantly increased in recent years.Automated collection of these data and its rapid analyses allow for better and faster detection of major (in terms of National impact) 'real life' events. This study uses data obtained from social networks such as Twitter and Google+. It proposes a mechanism for detecting major events and a system to alert on their manifestation. The article describes the considerations and needed algorithms required to develop and establish such a system.The methodology presented here is based on linking major events that occurred in Israel during the years 2011-2014, with information extracted from social networks. Results indicate that alerts were received shortly after the event occurred for most of major events. Such are large fires, earthquakes and terror attacks. However, attempts to achieve alerts for 'local' secondary events failed. This as their impact on the social network is low.

The understanding of information communicated over social networks enables quick tracking of real events as they occur. In other cases, where the “crowd” factor is on high note, it is possible to identify events and to evaluate their magnitude, even before they occur. A full assessment of the content generated by social network users is very complex. This, due to the gigantic volume of data communicated over the net at any given time. Using few, well defined, keywords for the detection of relevant data reduces, considerably, the processing effort and expedites the identification of events, such as wildfire, floods or terror attacks. The preliminary results here has shown that by using keywords, specially tailored for different types of major events, one may detect ‘abnormal' surges of social network activities. Also, presented are threshold values, in terms of magnitude and frequency designed for early detection of these events. This approach is the basis for the development of algorithms for early identification real time systems and for geographical tracking of major events.