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Article
Publication date: 1 May 2006

Srinivas Vadrevu, Fatih Gelgi, Saravanakumar Nagarajan and Hasan Davulcu

The purpose of this research is to automatically separate and extract meta‐data and instance information from various link pages in the web, by utilizing presentation and linkage…

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Abstract

Purpose

The purpose of this research is to automatically separate and extract meta‐data and instance information from various link pages in the web, by utilizing presentation and linkage regularities on the web.

Design/methodology/approach

Research objectives have been achieved through an information extraction system called semantic partitioner that automatically organizes the content in each web page into a hierarchical structure, and an algorithm that interprets and translates these hierarchical structures into logical statements by distinguishing and representing the meta‐data and their individual data instances.

Findings

Experimental results for the university domain with 12 computer science department web sites, comprising 361 individual faculty and course home pages indicate that the performance of the meta‐data and instance extraction averages 85, 88 percent F‐measure, respectively. Our METEOR system achieves this performance without any domain specific engineering requirement.

Originality/value

Important contributions of the METEOR system presented in this paper are: it performs extraction without the assumption that the object instance pages are template‐driven; it is domain independent and does not require any previously engineered domain ontology; and by interpreting the link pages, it can extract both meta‐data, such as concept and attribute names and their relationships, as well as their instances with high accuracy.

Details

Online Information Review, vol. 30 no. 3
Type: Research Article
ISSN: 1468-4527

Keywords

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Article
Publication date: 23 January 2021

Erick Méndez Guzmán, Ziqi Zhang and Wasim Ahmed

The purpose of this work is to study how different stakeholders of a football club engage with interactions online through Twitter. It analyses the football club’s Twitter network…

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Abstract

Purpose

The purpose of this work is to study how different stakeholders of a football club engage with interactions online through Twitter. It analyses the football club’s Twitter network to discover influential actors and the topic of interest in their online communication.

Design/methodology/approach

The authors analysed the social networks derived from over two million tweets collected during football matches played by Manchester United. The authors applied social network analysis to discover influencers and sub-communities and performed content analysis on the most popular tweets of the prominent influencers.

Findings

Sub-communities can be formed around current affairs that are irrelevant to football, perhaps due to opportunistic attempts of using the large networks and massive attention during football matches to disseminate information. Furthermore, the popularity of tweets featuring different topics depends on the types of influencers involved.

Practical implications

The methods can help football clubs develop a deeper understanding of their online social communities. The findings can also inform football clubs on how to optimise their communication strategies by using various influencers.

Originality/value

Compared to previous research, the authors discovered a wide range of influencers and denser networks characterised by a smaller number of large clusters. Interestingly, this study also found that bots appeared to become influential within the network.

Details

Information Discovery and Delivery, vol. 49 no. 1
Type: Research Article
ISSN: 2398-6247

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