Social Network - An Autonomous System Designed for Radio Recommendation

Dziczkowski, Grzegorz; Bougueroua, Lamine; Węgrzyn-Wolska, Katarzyna

doi:10.1109/cason.2009.21

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Applying data-mining techniques to the social network data has so much potential. For example, applying natural language processing techniques or text-mining to the Twitter data can be used to analyze or detect social opinions (Yassine and Hajj, 2010;Dziczkowski et al, 2009). Therefore, a tool that supports collecting and mining the social data is needed for researchers to be able to use this untapped resource.…”

Section: Discussionmentioning

confidence: 99%

Twitter data collecting tool with rule‐based filtering and analysis module

Byun

Lee

Kim

et al. 2013

International Journal of Web Information Systems

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Purpose -It is difficult to build our own social data set because data in social media is generally too vast and noisy. The aim of this study is to specify design and implementation details of the Twitter data collecting tool with a rule-based filtering module. Additionally, the paper aims to see how people communicate with each other through social networks in a case study with rule-based analysis. Design/methodology/approach -The authors developed a java-based data gathering tool with a rule-based filtering module for collecting data from Twitter. This paper introduces the design specifications and explain the implementation details of the Twitter Data Collecting Tool with detailed Unified Modeling Language (UML) diagrams. The Model View Controller (MVC) framework is applied in this system to support various types of user interfaces. Findings -The Twitter Data Collecting Tool is able to gather a huge amount of data from Twitter and filter the data with modest rules for complex logic. This case study shows that a historical event creates buzz on Twitter and people's interests on the event are reflected in their Twitter activity. Research limitations/implications -Applying data-mining techniques to the social network data has so much potential. A possible improvement to the Twitter Data Collecting Tool would be an adaptation of a built-in data-mining module. Originality/value -This paper focuses on designing a system handling massive amounts of Twitter Data. This is the first approach to embed a rule engine for filtering and analyzing social data. This paper will be valuable to those who may want to build their own Twitter dataset, apply customized filtering options to get rid of unnecessary, noisy data, and analyze social data to discover new knowledge.

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Section: Discussionmentioning

confidence: 99%

Twitter data collecting tool with rule‐based filtering and analysis module

Byun

Lee

Kim

et al. 2013

International Journal of Web Information Systems

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“…For example, applying natural language processing techniques or text mining to the Twitter data can be used to analyze or detect social opinions [5,6]. Future studies could consider limitations to enhance a better understanding of Twitter and Twitter usage.…”

Section: Discussionmentioning

confidence: 99%

The Influence of Social Media: Twitter Usage Pattern during the 2014 Super Bowl Game

Shin¹,

Byun

Lee

2015

IJMUE

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“…Whitelist accounts were once available on Twitter. Kwak et al crawled the entire Twitter site successfully, including 41.7 million user profiles and 106 million tweets by Twitter API [14]. However, whitelist accounts are no longer available now.…”

Section: Related Workmentioning

confidence: 99%

CUVIM: Extracting Fresh Information from Social Network

Guo

Wang

et al. 2013

Lecture Notes in Computer Science

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Online Social Network (OSN) is one of the most hottest services in the past years. It preserves the life of users and provides great potential for journalists, sociologists and business analysts. Crawling data from social network is a basic step for social network information analysis and processing. As the network becomes huge and information on the network updates faster than web pages, crawling is more difficult because of the limitations of band-width, politeness etiquette and computation power. To extract fresh information from social network efficiently and effectively, this paper presents a novel crawling method and discusses parallelization architecture of social network. To discover the feature of social network, we gather data from real social network, analyze them and build a model to describe the discipline of users' behavior. With the modeled behavior, we propose methods to predict users' behavior. According to the prediction, we schedule our crawler more reasonably and extract more fresh information with parallelization technologies. Experimental results demonstrate that our strategies could obtain information from OSN efficiently and effectively.

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Social Network - An Autonomous System Designed for Radio Recommendation

Cited by 9 publications

References 15 publications

Twitter data collecting tool with rule‐based filtering and analysis module

Twitter data collecting tool with rule‐based filtering and analysis module

The Influence of Social Media: Twitter Usage Pattern during the 2014 Super Bowl Game

CUVIM: Extracting Fresh Information from Social Network

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