Semantic Expansion of Tweet Contents for Enhanced Event Detection in Twitter

Özdikiş, Özer; Senkul, Pinar; Oğuztüzün, Halit

doi:10.1109/asonam.2012.14

Cited by 80 publications

(55 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Sayyadi et al [11] also model the community to discover and detect events on the live Labs SocialStream platform, extracting keywords, noun phrases and named entities. Ozdikis et al [12] also detect events using hashtags by clustering them and finding semantic similarities between hashtags, the latter being more of a lexicographic method. Ratkiewitcz et al [13] focus specifically on the detection of a single type of topic, namely political abuse.…”

Section: Related Workmentioning

confidence: 99%

Mining Newsworthy Topics from Social Media

Martin

Corney

Göker

2015

Advances in Social Media Analysis

View full text Add to dashboard Cite

Abstract. Newsworthy stories are increasingly being shared through social networking platforms such as Twitter and Reddit, and journalists now use them to rapidly discover stories and eye-witness accounts. We present a technique that detects "bursts" of phrases on Twitter that is designed for a real-time topic-detection system. We describe a time-dependent variant of the classic tf-idf approach and group together bursty phrases that often appear in the same messages in order to identify emerging topics. We demonstrate our methods by analysing tweets corresponding to events drawn from the worlds of politics and sport. We created a user-centred "ground truth" to evaluate our methods, based on mainstream media accounts of the events. This helps ensure our methods remain practical. We compare several clustering and topic ranking methods to discover the characteristics of news-related collections, and show that different strategies are needed to detect emerging topics within them. We show that our methods successfully detect a range of different topics for each event and can retrieve messages (for example, tweets) that represent each topic for the user.

show abstract

Section: Related Workmentioning

confidence: 99%

Mining Newsworthy Topics from Social Media

Martin

Corney

Göker

2015

Advances in Social Media Analysis

View full text Add to dashboard Cite

show abstract

“…A common way of analysing a set of tweets is to take their hashtags and partition them in a set of non-overlapping classes, so that all the hashtags in the same class (cluster) are supposed to be heavily related, whereas those belonging to different classes should be associated to different topics [15,17,22]. In this study we have compared two different ways of clustering the set of hashtags of the tweet corpus:…”

Section: Fig 1 Distribution Of Hashtags Per Tweetmentioning

confidence: 99%

“…Wang et al [17] define a sentiment analysis method based on different mechanisms of sentiment propagation in graphs that basically take into account the co-occurrence of hashtags. Ozdikis et al [22] cluster hashtags by considering the cooccurrence between the hashtags and the words that appear in the tweets. Pöschko [15] also considers the co-occurrence between hashtags to group together related tweets for visualization purposes.…”

Section: Related Workmentioning

confidence: 99%

“…visualization of clusters of similar tweets [14,15], recommendation of hashtags [16], sentiment analysis [17,18], detection of events or common topics [19,20], clustering of tweets [21,22], etc.). However, in most cases it can be observed that the treatment of the tweet components (mainly the words contained in the body of the tweet and its hashtags) is purely syntactic.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Study on the Influence of Semantics on the Analysis of Micro-blog Tags in the Medical Domain

Vicient

Moreno

2013

Availability, Reliability, and Security in Information Systems and HCI

View full text Add to dashboard Cite

“…The approach cannot detect previously unknown events. Phuvipadawat and Murata (2010), Ozdikis et al (2012) and Cordeiro (2012), scale their systems by only considering tweets containing hashtags. Although efficient, this method don't consider 90% of the tweets (Petrovic, 2013), which limits their scope.…”

Section: Related Workmentioning

confidence: 99%

Twitter-scale New Event Detection via K-term Hashing

Wurzer

Lavrenko

Osborne

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

First Story Detection is hard because the most accurate systems become progressively slower with each document processed.We present a novel approach to FSD, which operates in constant time/space and scales to very high volume streams. We show that when computing novelty over a large dataset of tweets, our method performs 192 times faster than a state-of-the-art baseline without sacrificing accuracy. Our method is capable of performing FSD on the full Twitter stream on a single core of modest hardware.

show abstract

Semantic Expansion of Tweet Contents for Enhanced Event Detection in Twitter

Cited by 80 publications

References 8 publications

Mining Newsworthy Topics from Social Media

Mining Newsworthy Topics from Social Media

A Study on the Influence of Semantics on the Analysis of Micro-blog Tags in the Medical Domain

Twitter-scale New Event Detection via K-term Hashing

Contact Info

Product

Resources

About