Event Detection via Tracking the Change in Community Structure and Communication Trends

Abstract

Event detection is a popular research problem aiming to detect events from various data sources, such as news texts, social media postings or social interaction patterns. In this work, event detection is studied on social interaction and communication data via tracking changes in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">community structure and communication trends</i> . With this aim, various community structure and communication trend based event detection methods are proposed. Additionally, a new strategy called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">community size range based change tracking</i> is presented such that the proposed algorithms can focus on communities with different size ranges, and considerable time efficiency can be obtained. The event detection performance of the proposed methods is analyzed using a set of real world and benchmark data sets in comparison to previous solutions in the literature. The experiments show that the proposed methods have higher event detection accuracy than the baseline methods. Additionally, their scalability is presented through analysis by using high volume of communication data. Among the proposed methods, CN-NEW, which is a community structure based method, performs the best on the overall. The proposed communication trend based methods perform better mostly on communication data sets (such as CDR), whereas community structure based methods tend to perform better on social media-based data sets.