Semantic Expansion of Tweet Contents for Enhanced Event Detection in Twitter

Date

2012-08-29

Author

Ozdikis, Ozer
Karagöz, Pınar
Oğuztüzün, Mehmet Halit S.

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

186
views

0
downloads

This paper aims to enhance event detection methods in a micro-blogging platform, namely Twitter. The enhancement technique we propose is based on lexico-semantic expansion of tweet contents while applying document similarity and clustering algorithms. Considering the length limitations and idiosyncratic spelling in Twitter environment, it is possible to take advantage of word similarities and to enrich texts with similar words. The semantic expansion technique we implement is based on syntagmatic and paradigmatic relationships between words, extracted from their co-occurrence statistics. As our technique does not depend on an existing ontology or a lexicon database such as WordNet, it should be applicable for any language. The proposed technique is applied on a tweet set collected for three days from the users in Turkey. The results indicate earlier detection of events and improvements in accuracy.

Subject Keywords

Event detection, Clustering, Micro-blogging, Twitter, Tweets in Turkish, Semantics, Word co-occurrences

URI

https://hdl.handle.net/11511/34327

DOI

https://doi.org/10.1109/asonam.2012.14

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Semantic Expansion of Hashtags for Enhanced Event Detection in Twitter Özdikiş, Özer; Karagöz, Pınar; Oğuztüzün, Mehmet Halit Seyfullah (2012-09-09) In this work, we present an event detection method in Twitter based on clustering of hashtags and introduce an enhancement technique by using the semantic similarities between the hashtags. To this aim, we devised two methods for tweet vector generation and evaluated their effect on clustering and event detection performance in comparison to word-based vector generation methods. By analyzing the contexts of hashtags and their co-occurrence statistics with other words, we identify their paradigmatic relation...
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 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...

Citation Formats

O. Ozdikis, P. Karagöz, and M. H. S. Oğuztüzün, “Semantic Expansion of Tweet Contents for Enhanced Event Detection in Twitter,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34327.