Towards an On-Line Analysis of Tweets Processing

Bringay, Sandra; Béchet, Nicolas; Bouillot, Flavien; Poncelet, Pascal; Roche, Mathieu; Teisseire, Maguelonne

doi:10.1007/978-3-642-23091-2_15

Cited by 30 publications

(31 citation statements)

References 10 publications

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“…Nevertheless, we identified few studies that have focused on the use of multidimensional tweets (Content and metadata). Among these works, the one of Bringay et al (2011) defined a multidimensional star model for analyzing a large number of tweets. However, the proposed model was dedicated to a particular trend.…”

Section: Related Workmentioning

confidence: 99%

Modeling and OLAPing social media: the case of Twitter

Kraiem

Feki

Khrouf

et al. 2015

Soc. Netw. Anal. Min.

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In the recent year, social networks have revolutionized the ways of interacting and exchanging information on the Internet. Millions of users interact frequently and share variety of digital content with each other. They express their feelings and opinions on every topic of interest. These opinions carry import value for personal, academic, and commercial applications, but the volume and the speed at which these are produced make it a challenging task for researchers and the underlying technologies to provide useful insights into such data. We attempt to extend the established online analytical processing (OLAP) technology to allow multidimensional analysis of social media data. In this paper, we pursue a goal of providing a generic multidimensional model dedicated to the OLAP of social media and specially Twitter. The proposed model reflects on some specifics such as recursive references between tweets, Empty dimension, and different types of hierarchies. It is implemented using NetBeans IDE platform. We present also some experimental results. We expect our proposed approach to be applicable for analyzing the data of other social networks as well.

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Section: Related Workmentioning

confidence: 99%

Modeling and OLAPing social media: the case of Twitter

Kraiem

Feki

Khrouf

et al. 2015

Soc. Netw. Anal. Min.

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“…Among these works, the one of [4] defined a multidimensional star model for analyzing a large number of tweets. However the proposed model was dedicated to a particular trend.…”

Section: Related Workmentioning

confidence: 99%

OLAP4Tweets: Multidimensional Modeling of Tweets

Kraiem

Feki

Khrouf

et al. 2015

Communications in Computer and Information Science

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“…For instance, the characteristics of the words in tweets are not necessarily the same in a State and in a City. In [7], in a very different context, we proposed a new measure called T F -IDF adaptative . This measure has been defined in order not to focus on the number of documents but rather to the number of documents for a specific class and take into account the level in the hierarchy.…”

Section: Some Proposed Measuresmentioning

confidence: 99%

“…Since its introduction in 2006, the Twitter website 6 has become so popular that it is currently ranked as the 10 th most visited site over the world 7 . In January 2012, Twitter has been visited 2.5 billion times and in October 2011, more than 250 million tweets are posted every day with a user base of about 300 million people.…”

Section: Introductionmentioning

confidence: 99%

“…In [7], we propose to address these multidimensional characteristics by focusing on datawarehouses [8] since they provide very efficient tool for the storage and analysis of multidimensional and historized data. Our main goal was to using the facilities provided by these tool to manipulate a set of indicators (measures) according to the different dimensions obtained from tweets and for which we can be provide some hierarchies.…”

Section: Introductionmentioning

confidence: 99%

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How to Extract Relevant Knowledge from Tweets?

Bouillot

Hai

Béchet

et al. 2013

Communications in Computer and Information Science

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Abstract. Tweets exchanged over the Internet are an important source of information even if their characteristics make them difficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we investigate two different problems. The first one is related to the extraction of representative terms from a set of tweets. More precisely we address the following question: are traditional information retrieval measures appropriate when dealing with tweets?. The second problem is related to the evolution of tweets over time for a set of users. With the development of data mining approaches, lots of very efficient methods have been defined to extract patterns hidden in the huge amount of data available. More recently new spatio-temporal data mining approaches have specifically been defined for dealing with the huge amount of moving object data that can be obtained from the improvement in positioning technology. Due to particularity of tweets, the second question we investigate is the following: are spatio-temporal mining algorithms appropriate for better understanding the behavior of communities over time? These two problems are illustrated through real applications concerning both health and political tweets.

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Towards an On-Line Analysis of Tweets Processing

Cited by 30 publications

References 10 publications

Modeling and OLAPing social media: the case of Twitter

Modeling and OLAPing social media: the case of Twitter

OLAP4Tweets: Multidimensional Modeling of Tweets

How to Extract Relevant Knowledge from Tweets?

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