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

Aktunc, Riza
Karagöz, Pınar
Toroslu, Ismail Hakki
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 community structure and communication trends. With this aim, various community structure and communication trend based event detection methods are proposed. Additionally, a new strategy called community size range based change tracking 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.


Event Detection by Change Tracking on Community Structure of Temporal Networks
Aktunc, Riza; Toroslu, İsmail Hakkı; Karagöz, Pınar (2018-08-31)
Event detection is a popular research problem, aiming to detect events from online data sources with least possible delay. Most of the previous work focus on analyzing textual content such as social media postings to detect happenings. In this work, we consider event detection as a change detection problem in network structure, and propose a method that detects change in community structure extracted from communication network. We study three versions of the method based on different change models. Experime...
Event Detection on Communities: Tracking the Change in Community Structure within Temporal Communication Networks
Aktunç, Rıza; Toroslu, İsmail Hakkı; Karagöz, Pınar (Springer, Cham, 2020-01-01)
In this work, we focus on social interactions in communities in order to detect events. There are several previous efforts for the event detection problem based on analyzing the change in the network structure in terms of the overall network features. However, in this work, event detection is considered as a problem of change detection in community structures. Particularly, communities extracted from communication network are focused on, and various versions of the community change detection methods are dev...
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Çınar, Hüseyin Alper; Karagöz, Pınar; Department of Computer Engineering (2019)
The aim of this study is detecting events on social media by improving current solutions in terms of accuracy and time performance. An event is something that occurs in a short duration of time in a certain place. In this thesis, the problem is modelled as a streaming transaction process. Three different event detection method is adapted to our solution. First one is the keyword-based event detection method that looks for bursty keywords in a period. The second one is the clustering-based event detection me...
Word Embedding Based Event Detection on Social Media
Ertugrul, Ali Mert; Velioglu, Burak; Karagöz, Pınar (2017-06-23)
Event detection from social media messages is conventionally based on clustering the message contents. The most basic approach is representing messages in terms of term vectors that are constructed through traditional natural language processing (NLP) methods and then assigning weights to terms generally based on frequency. In this study, we use neural feature extraction approach and explore the performance of event detection under the use of word embeddings. Using a corpus of a set of tweets, message terms...
Online event detection from streaming data
Şahin, Özlem Ceren; Karagöz, Pınar; Department of Computer Engineering (2018)
The purpose of this study is detecting events from social media in an online fashion where event is a happening that takes place at a certain time and place that attracts attention within a short period of time. By doing so, it is aimed to provide a system both accurate and efficient at the same time. The problem studied in this thesis is modeled as a stream processing problem and three alternative methods are proposed. The first event detection method is keyword-based and works with bursty keywords inside ...
Citation Formats
R. Aktunc, P. Karagöz, and I. H. Toroslu, “Event Detection via Tracking the Change in Community Structure and Communication Trends,” IEEE ACCESS, vol. 10, pp. 109712–109728, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/101115.