![]() ![]() MATTHAIOS ICONIE FACEBOOK WINDOWSIn this paper, by relying on sliding windows to separate graph streams in chunks, we propose a Hoe-PGPL algorithm to handle the top-k correlated patterns searching from a dynamic perspective. Traditional methods treat graph streams as static records, which is computational infeasible or ineffective because of the complexity of searching correlated patterns in a dynamic graph stream. In this paper, a correlated graph pattern searching scheme has been proposed, that is, provided with a query g as a structured pattern (i.e., a graph), our algorithm is capable of retrieving the top-k graphs that most likely correlated with g. Mining the correlation has attracted widespread attention in the research community because of its advantages in understanding the dependencies between objects. A preliminary version of this work was presented as a 4-pages short paper at ICWSM 2018. ![]() We further improve the results of our algorithm by incorporating news from mainstream media. Our evaluation shows that our method outperforms state-of-the-art approaches for the same problem, in terms of having higher precision, lower number of duplicates, and presenting a keyword-based description that is succinct and informative. We evaluate our approach on a large collection of tweets posted over a period of 19 months, using a crowdsourcing platform. One of the challenges we address in our work is to provide for each event a succinct keyword-based description, containing the most relevant information about it, such as what happened, the location, as well as its timeframe. We present EviDense, a graph-based approach for finding high-impact events (such as disaster events) in social media. This study would help the future researches in the social media data analytics domain for crisis management.ĭespite the significant efforts made by the research community in recent years, automatically acquiring valuable information about high impact-events from social media remains challenging. The contribution of the paper includes the study of research papers from two different aspects - i) Computational Steps for performing a research on event and sub-event detection from social media data, ii) Computational Techniques briefly discussing the methods adopted in recent studies pertaining to event and sub-event detection and summarization. In this paper, we review the existing researches in the field of event and sub-event identification from social media based microblog data for disaster management. Particularly in the field of crisis management, event and sub-event detection can be of great benefit assisting the public safety departments to plan for quick responses. Detecting the events and sub-events from social media posts that require special attention is one of the key research problem in this domain with wide range of applications. ![]() ![]() Social media data analysis is a popular research domain since the last decade. ![]()
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