Event Detection by Change Tracking on Community Structure of Temporal Networks

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. Experimental analysis on benchmark data set reveals that change in the community can be used as an indication of an event.


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...
Streaming Event Detection in Microblogs: Balancing Accuracy and Performance
SAHIN, OZLEM CEREN; Karagöz, Pınar; TATBUL, NESIME (2019-06-14)
In this work, we model the problem of online event detection in microblogs as a stateful stream processing problem and offer a novel solution that balances result accuracy and performance. Our new approach builds on two state of the art algorithms. The first algorithm is based on identifying bursty keywords inside blocks of blog messages. The second one involves clustering blog messages based on similarity of their contents. To combine the computational simplicity of the keyword-based algorithm with the sem...
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...
Event detection on social media using transaction based stream processing engine
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...
Event Boundary Detection Using Audio Visual Features and Web casting Texts with Imprecise Time Information
MÜJDAT, Bayar; ALAN, Özgür; SAMET, Akpınar; ORKUNT, Sabuncu; Çiçekli, Fehime Nihan; Alpaslan, Ferda Nur (2010-07-21)
We propose a method to detect events and event boundaries in soccer videos by using web-casting texts and audio-visual features. The events and their inaccurate time information given in web-casting texts need to be aligned with the visual content of the video. We overcome this issue by utilizing textual, visual and audio features. Existing methods assume that the time at which the event occurs is given precisely (in seconds). However, most web-casting texts presented by popular organizations such as uefa.c...
Citation Formats
R. Aktunc, İ. H. Toroslu, and P. Karagöz, “Event Detection by Change Tracking on Community Structure of Temporal Networks,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52701.