Event detection on social media using transaction based stream processing engine

Download
2019
Çınar, Hüseyin Alper
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 method which is a version of the hierarchical clustering algorithm. And the last one is the hybrid event detection method of keyword-based and clustering-based algorithms. To specify the problem as streaming transaction process, all algorithms are implemented on top of S-Store. S-Store is a streaming OLTP engine having distributed, scalable and guaranteed ordered delivery features. All of the event detection methods are run and evaluated their performance with a real data set obtained from Twitter.

Suggestions

Event Detection via Tracking the Change in Community Structure and Communication Trends
Aktunc, Riza; Karagöz, Pınar; Toroslu, Ismail Hakki (2022-01-01)
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 trac...
Event detection via tracking the change in community structure, communication trends, and graph embeddings
Aktunç, Rıza; Karagöz, Pınar; Toroslu, İsmail Hakkı; Department of Computer Engineering (2022-11-4)
Event detection is a popular research problem aiming to detect events from various data sources, such as climate records, traffic data, 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, communication trends, and graph embeddings. With this aim, various community structure, communication trend, and graph embedding based event detection methods are proposed. Additi...
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...
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...
Citation Formats
H. A. Çınar, “Event detection on social media using transaction based stream processing engine,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering., Middle East Technical University, 2019.