Utilizing Complex Networks for Event Detection in Heterogeneous High-Volume News Streams

Moutidis, Iraklis; Williams, Hywel T. P.

doi:10.1007/978-3-030-36687-2_55

Cited by 9 publications

(14 citation statements)

References 18 publications

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“…However, this framework ignored the high dimensionality of feature space generated from merging a large volume of news articles published by different sources. Moutidis and Williams (2020) identified events through finding peaks within entity knowledge graph and summarizing of events was done by applied community detection method on KeyGraph that linking noun-phrases and entities. This study was implemented on small size of manually annotated dataset.…”

Section: Graph-based Clustering Methodsmentioning

confidence: 99%

A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

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Event Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: Data collection, pre-processing, feature selection, event detection, performance evaluation and result representation. Among these phases, event detection phase has a vital rule in the performance of the ED model. Consequently, numerous supervised, unsupervised, semi-supervised detection methods have been introduced for this phase. However, unsupervised methods have been extensively utilized as ED process is considered as unsupervised task. Hence, such methods need to be categorized on such a way so it can help researchers to understand and identified the limitations lay in these methods. In this survey, ED models for text data from various Social Network sites (SNs) are analyzed based on domain type, detection methods, type of detection task. In addition, main categories for unsupervised detection methods are explicitly mentioned with revising their related works. Moreover, the major open challenges faced by researchers for building ED models are explained and discussed in detail. The main objective of this survey paper is to provide a complete view of the recent developments in ED field. Hence, help scholars to identify the limitations of existing ED models for text data and help them to recognize the interesting future works directions.

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Section: Graph-based Clustering Methodsmentioning

confidence: 99%

A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

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“…In this work, we expand the network event detection (NED) previously introduced in Moutidis and Williams (2019). The main goal of the NED system is to detect emerging news 'events' from a stream of documents (articles, tweets).…”

Section: Sentiment Analysis For Twitter Datamentioning

confidence: 99%

“…This permits a summary of the detected event to be created by applying community detection to the entity-phrase graph, where a community of named entities and noun phrases corresponds to a description of a news event. A more detailed description of the NED system can be found in Moutidis and Williams (2019). 1…”

Section: Sentiment Analysis For Twitter Datamentioning

confidence: 99%

“…In prior work Moutidis and Williams (2019), we introduced a method labelled the network event detection (NED) system ( Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the application of community detection techniques to the expanded knowledge graph generates a set of ranked n-grams (named entities and noun phrases) that provides a summary description of the event. In previously published work Moutidis and Williams (2019), the NED approach is described fully and evaluated on several manually curated data sets from online news and social media.…”

Section: Introductionmentioning

confidence: 99%

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Good and bad events: combining network-based event detection with sentiment analysis

Moutidis

Williams

2020

Soc. Netw. Anal. Min.

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The huge volume and velocity of media content published on the Web presents a substantial challenge to human analysts. In prior work, we developed a system (network event detection, NED) to assist analysts by detecting events within high-volume news streams in real time. NED can process a heterogeneous stream of news articles or social media user posts, combining text mining and network analysis to detect breaking news stories and generate an easy-to-understand event summary. In this paper, we expand the NED event detection and summarisation approach in two ways. First, we introduce a new approach to named entity disambiguation for tweets, which contain minimal information due to brevity. Second, we apply sentiment analysis techniques to documents associated with a detected event to characterise the event as either broadly 'positive' or 'negative' based on media portrayal. Our expansion focuses on Twitter streams since Twitter has become an important news dissemination platform and is often the site where emerging events are first seen. To test the extended methodology, we apply it here to three data sets related to political elections in the UK and the USA. The addition of sentiment analysis to the NED event detection methodology improves the insight gained by the user by allowing quick evaluation of the perceived impact of an event. This approach may have potential applications in domains where public sentiment is relevant to decision-making around events, such as financial markets and politics.

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