Mining Social Media Text: Extracting Knowledge from Facebook

Social media websites allow users to communicate with each other through several tools like chats, discussion forums, comments etc. This results in learning and sharing of important information among the users. The nature of information on such social networking websites can be straight forward cate...

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Main Authors:	Salloum, Said A., Al-Emran, Mostafa, Shaalan, Khaled
Format:	Article
Language:	English
Published:	University Of Bahrain 2017
Subjects:	T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/17104/ http://umpir.ump.edu.my/id/eprint/17104/ http://umpir.ump.edu.my/id/eprint/17104/ http://umpir.ump.edu.my/id/eprint/17104/1/fskkp-2017-emran-Mining%20social%20media%20text%20extracting%20%20knowledge%20from%20facebook.pdf

id	ump-17104
recordtype	eprints
spelling	ump-171042017-03-29T05:47:59Z http://umpir.ump.edu.my/id/eprint/17104/ Mining Social Media Text: Extracting Knowledge from Facebook Salloum, Said A. Al-Emran, Mostafa Shaalan, Khaled T Technology (General) Social media websites allow users to communicate with each other through several tools like chats, discussion forums, comments etc. This results in learning and sharing of important information among the users. The nature of information on such social networking websites can be straight forward categorized as unstructured and fuzzy. In regular day-to-day discussions, spellings, grammar and sentence structure are usually neglected. This may prompt various sorts of ambiguities, for example, lexical, syntactic, and semantic, which makes it difficult to analyse and extract data patterns from such datasets. This study aims at analyzing textual data from Facebook and attempts to find interesting knowledge from such data and represent it in different forms. 33815 posts from 16 news channels pages over Facebook were extracted and analyzed. Different text mining techniques were applied on the collected data. Findings indicated that Fox news is the most news channel that share posts on Facebook, followed by CNN and ABC News respectively. Results revealed that the most frequent linked words are focused on the USA elections. Moreover, results revealed that most of the people are highly interested in sharing the news of Mohammed Ali Clay through all the news channels. Other implications and future perspectives are presented within the study. University Of Bahrain 2017-03 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17104/1/fskkp-2017-emran-Mining%20social%20media%20text%20extracting%20%20knowledge%20from%20facebook.pdf Salloum, Said A. and Al-Emran, Mostafa and Shaalan, Khaled (2017) Mining Social Media Text: Extracting Knowledge from Facebook. International Journal of Computing and Digital Systems, 6 (2). pp. 73-81. ISSN 2210-142X http://journals.uob.edu.bh/IJCDS/contents/volume-1082/articles/article-2675 DOI: 10.12785/ijcds/060203
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Salloum, Said A. Al-Emran, Mostafa Shaalan, Khaled Mining Social Media Text: Extracting Knowledge from Facebook
description	Social media websites allow users to communicate with each other through several tools like chats, discussion forums, comments etc. This results in learning and sharing of important information among the users. The nature of information on such social networking websites can be straight forward categorized as unstructured and fuzzy. In regular day-to-day discussions, spellings, grammar and sentence structure are usually neglected. This may prompt various sorts of ambiguities, for example, lexical, syntactic, and semantic, which makes it difficult to analyse and extract data patterns from such datasets. This study aims at analyzing textual data from Facebook and attempts to find interesting knowledge from such data and represent it in different forms. 33815 posts from 16 news channels pages over Facebook were extracted and analyzed. Different text mining techniques were applied on the collected data. Findings indicated that Fox news is the most news channel that share posts on Facebook, followed by CNN and ABC News respectively. Results revealed that the most frequent linked words are focused on the USA elections. Moreover, results revealed that most of the people are highly interested in sharing the news of Mohammed Ali Clay through all the news channels. Other implications and future perspectives are presented within the study.
format	Article
author	Salloum, Said A. Al-Emran, Mostafa Shaalan, Khaled
author_facet	Salloum, Said A. Al-Emran, Mostafa Shaalan, Khaled
author_sort	Salloum, Said A.
title	Mining Social Media Text: Extracting Knowledge from Facebook
title_short	Mining Social Media Text: Extracting Knowledge from Facebook
title_full	Mining Social Media Text: Extracting Knowledge from Facebook
title_fullStr	Mining Social Media Text: Extracting Knowledge from Facebook
title_full_unstemmed	Mining Social Media Text: Extracting Knowledge from Facebook
title_sort	mining social media text: extracting knowledge from facebook
publisher	University Of Bahrain
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/17104/ http://umpir.ump.edu.my/id/eprint/17104/ http://umpir.ump.edu.my/id/eprint/17104/ http://umpir.ump.edu.my/id/eprint/17104/1/fskkp-2017-emran-Mining%20social%20media%20text%20extracting%20%20knowledge%20from%20facebook.pdf
first_indexed	2023-09-18T22:23:22Z
last_indexed	2023-09-18T22:23:22Z
_version_	1777415804604645376

Mining Social Media Text: Extracting Knowledge from Facebook

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