TwitterMonitor: trend detection over the twitter stream

By Michael Mathioudakis, Nick Koudas. 2010

954 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Processing Social Media Messages in Mass Emergency (2014, methodology): “A similar system, TwitterMonitor [Mathioudakis and Koudas 2010], also collects tweets from the Twitter stream and detects trends (e.g. emerging topics such as breaking news, or crises) in real-time.”
Evaluating Event Credibility on Twitter (2012, methodology): “They extracted events using Twitter Monitor [10] from tweet feeds for Apr-Sep 2010.”
When is it biased?: assessing the representativeness of twitter's streaming API (2014, methodology): “It has been used to collect data for topic modeling [10, 16], network analysis [19], and statistical analysis of content [12], among others.”
Twevent: segment-based event detection from tweets (2012, background): “Michael and Nick [11] presented a trend detection system over twitter stream.”
Sensing Trending Topics in Twitter (2013, background): “, Twitter), but with additional emphasis on the enrichment of the obtained topics with non-bursty but relevant terms, URLs and locations [28].”
#Earthquake: Twitter as a Distributed Sensor System (2013, background): “In order to fully realize this potential we need to take advantage of systems that perform real-time trend detection over the Twitter stream (e.g. Mathioudakis and Koudas 2010).”
Predicting the Political Alignment of Twitter Users (2011, background): “Similarly, burst-detection mechanisms could be employed to detect a rise in prominence of a specific candidate or issue, allowing campaigns to shape marketing and messaging efforts in response to emerging topics and trends [11], [41], [42].”
Extracting information nuggets from disaster- Related messages in social media (2013, methodology): “Most event-detection methods, however, are based on keyword (Mathioudakis and Koudas, 2010).”
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, neutral): “…of news stories, and the reactions of the users to events (Benhardus & Kalita, 2013; Bifet & Frank, 2010; Hsieh, Moghbel, Fang, & Cho, 2013; Mathioudakis & Koudas, 2010; Nav-eed, Gottron, Kunegis, & Alhadi, 2011; Petrovic, Osborne, & Lav-renko, 2010; Phelan, McCarthy, & Smyth, 2009;…”
Real-time Rumor Debunking on Twitter (2015, methodology): “To collect additional true stories, we sent Twitter’s free data stream 2 , i.e. 1% of ﬁrehose, to our event detection system which essentially is a clustering algorithm that groups tweets of the same stories or events 3 , very close to the outcomes of Twitter Monitor [4].”
Sampling Content from Online Social Networks (2015, methodology): “%) provided by Twitter have been used, for instance, to detect current trending topics [Mathioudakis and Koudas 2010], to summarize the tweet stream to provide user-speciﬁc and topic-speciﬁc temporal analytics [Yang et al. 2012a], to map the content to various topical dimensions [Ramage et al.…”
Learning similarity functions for topic detection in online reputation monitoring (2014, methodology): “Co-occurrence bursts and temporal signals have been adopted for detecting topics in both the blogosphere [30, 16, 37] and the Twittersphere [25, 8, 37].”