Graph-based Event Extraction from Twitter

Edouard, Amosse; Cabrio, Elena; Tonelli, Sara; Thành, Nhân Lê

doi:10.26615/978-954-452-049-6_031

Cited by 22 publications

(10 citation statements)

References 22 publications

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“…Thus, our model SEDTWik outperforms Twevent in all the three metrics. Edouard et al (2017) and TwitterNews+ (Hasan et al, 2016) also evaluated their models on the same Events2012 dataset (McMinn et al, 2013) but on a different period of tweets and were able to get precision values of 75.0% and 78.0% only. Since we have to manually annotate the results, we did not re-evaluate the results of our model on these tweets but we believe our model would outperform both these models in terms of precision.…”

Section: Event Detection Resultsmentioning

confidence: 99%

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Morabia¹,

Neti²,

Malapati³

et al. 2019

Proceedings of the 2019 Conference of the North

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Event Detection has been one of the research areas in Text Mining that has attracted attention during this decade due to the widespread availability of social media data specifically twitter data. Twitter has become a major source for information about real-world events because of the use of hashtags and the small word limit of Twitter that ensures concise presentation of events. Previous works on event detection from tweets are either applicable to detect localized events or breaking news only or miss out on many important events. This paper presents the problems associated with event detection from tweets and a tweet-segmentation based system for event detection called SEDTWik, an extension to a previous work, that is able to detect newsworthy events occurring at different locations of the world from a wide range of categories. The main idea is to split each tweet and hash-tag into segments, extract bursty segments, cluster them, and summarize them. We evaluated our results on the well-known Events2012 corpus and achieved state-of-the-art results.

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Section: Event Detection Resultsmentioning

confidence: 99%

Untitled

Morabia¹,

Neti²,

Malapati³

et al. 2019

Proceedings of the 2019 Conference of the North

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“…The document clusters were generated based on the maximum common subgraphs between each document graph. In a similar attempt, Edouard et al (2017) proposed an event clustering model which leveraged named entities (NE) based directed GoW structure. The GoW was improved by using surrounding context of the graph nodes, NE, to enrich node level information.…”

Section: Related Workmentioning

confidence: 99%

Tracing Topic Transitions with Temporal Graph Clusters

Jing

Zhang

et al. 2021

FLAIRS

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Twitter serves as a data source for many Natural Language Processing (NLP) tasks. It can be challenging to identify topics on Twitter due to continuous updating data stream. In this paper, we present an unsupervised graph based framework to identify the evolution of sub-topics within two weeks of real-world Twitter data. We first employ a Markov Clustering Algorithm (MCL) with a node removal method to identify optimal graph clusters from temporal Graph-of-Words (GoW). Subsequently, we model the clustering transitions between the temporal graphs to identify the topic evolution. Finally, the transition flows generated from both computational approach and human annotations are compared to ensure the validity of our framework.

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“…Once we can compute similarities, the advantage is that a wide variety of clustering algorithms can be leveraged [21]. For example, community detection algorithms have been used in similar settings [13,31]. One of the most popular algorithms of this type is the Louvain method [10], which relies on modularity-based graph partitioning.…”

Section: Entitymentioning

confidence: 99%

Real-time Event Detection on Social Data Streams

Fedoryszak¹,

Frederick

Rajaram

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. The information that is generated on social platforms like Twitter can produce rich data streams for immediate insights into ongoing matters and the conversations around them. To tackle the problem of event detection, we model events as a list of clusters of trending entities over time. We describe a real-time system for discovering events that is modular in design and novel in scale and speed: it applies clustering on a large stream with millions of entities per minute and produces a dynamically updated set of events. In order to assess clustering methodologies, we build an evaluation dataset derived from a snapshot of the full Twitter Firehose and propose novel metrics for measuring clustering quality. Through experiments and system profiling, we highlight key results from the offline and online pipelines. Finally, we visualize a high profile event on Twitter to show the importance of modeling the evolution of events, especially those detected from social data streams.

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Graph-based Event Extraction from Twitter

Cited by 22 publications

References 22 publications

Untitled

Untitled

Tracing Topic Transitions with Temporal Graph Clusters

Real-time Event Detection on Social Data Streams

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