A Survey of Data Mining Techniques for Social Media Analysis
Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for info...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nicolas Turenne
2014-06-01
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| Series: | Journal of Data Mining and Digital Humanities |
| Subjects: | |
| Online Access: | https://jdmdh.episciences.org/5/pdf |
| _version_ | 1827323642739949568 |
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| author | Mariam Adedoyin-Olowe Mohamed Medhat Gaber Frederic Stahl |
| author_facet | Mariam Adedoyin-Olowe Mohamed Medhat Gaber Frederic Stahl |
| author_sort | Mariam Adedoyin-Olowe |
| collection | DOAJ |
| description | Social network has gained remarkable attention in the last decade. Accessing
social network sites such as Twitter, Facebook LinkedIn and Google+ through the
internet and the web 2.0 technologies has become more affordable. People are
becoming more interested in and relying on social network for information, news
and opinion of other users on diverse subject matters. The heavy reliance on
social network sites causes them to generate massive data characterised by
three computational issues namely; size, noise and dynamism. These issues often
make social network data very complex to analyse manually, resulting in the
pertinent use of computational means of analysing them. Data mining provides a
wide range of techniques for detecting useful knowledge from massive datasets
like trends, patterns and rules [44]. Data mining techniques are used for
information retrieval, statistical modelling and machine learning. These
techniques employ data pre-processing, data analysis, and data interpretation
processes in the course of data analysis. This survey discusses different data
mining techniques used in mining diverse aspects of the social network over
decades going from the historical techniques to the up-to-date models,
including our novel technique named TRCM. All the techniques covered in this
survey are listed in the Table.1 including the tools employed as well as names
of their authors. |
| first_indexed | 2024-03-11T21:05:35Z |
| format | Article |
| id | doaj.art-4fcc2a51ea0f411aa7ee0a124c784e12 |
| institution | Directory Open Access Journal |
| issn | 2416-5999 |
| language | English |
| last_indexed | 2024-04-25T01:56:16Z |
| publishDate | 2014-06-01 |
| publisher | Nicolas Turenne |
| record_format | Article |
| series | Journal of Data Mining and Digital Humanities |
| spelling | doaj.art-4fcc2a51ea0f411aa7ee0a124c784e122024-03-07T16:29:59ZengNicolas TurenneJournal of Data Mining and Digital Humanities2416-59992014-06-01201410.46298/jdmdh.55A Survey of Data Mining Techniques for Social Media AnalysisMariam Adedoyin-OloweMohamed Medhat GaberFrederic StahlSocial network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.https://jdmdh.episciences.org/5/pdfcomputer science - social and information networkscomputer science - computation and language |
| spellingShingle | Mariam Adedoyin-Olowe Mohamed Medhat Gaber Frederic Stahl A Survey of Data Mining Techniques for Social Media Analysis Journal of Data Mining and Digital Humanities computer science - social and information networks computer science - computation and language |
| title | A Survey of Data Mining Techniques for Social Media Analysis |
| title_full | A Survey of Data Mining Techniques for Social Media Analysis |
| title_fullStr | A Survey of Data Mining Techniques for Social Media Analysis |
| title_full_unstemmed | A Survey of Data Mining Techniques for Social Media Analysis |
| title_short | A Survey of Data Mining Techniques for Social Media Analysis |
| title_sort | survey of data mining techniques for social media analysis |
| topic | computer science - social and information networks computer science - computation and language |
| url | https://jdmdh.episciences.org/5/pdf |
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