A Geofencing-based Recent Trends Identification from Twitter Data

For facilitating users from information overloading by finding recent trends in twitter, several techniques are proposed. However, most of these techniques need to process extensive data. Therefore, in this paper, a geofencing-based recent trends identification technique is proposed, which acquires...

Full description

Saved in:
Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2020-02, Vol.769 (1), p.12008
Main Authors: Saef Ullah Miah, M., Sadid Tahsin, M., Azad, Saiful, Rabby, Gollam, Sirajul Islam, M., Uddin, Shihab, Masuduzzaman, M.
Format: Article
Language:English
Subjects:
Data acquisition
Geofences
Trends
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c3128-621d49cb7e36b4fe50ebfee370281b3d22f3a1442a836a2ba70ee12ee445a18b3
container_end_page
container_issue 1
container_start_page 12008
container_title IOP conference series. Materials Science and Engineering
container_volume 769
creator Saef Ullah Miah, M.
Sadid Tahsin, M.
Azad, Saiful
Rabby, Gollam
Sirajul Islam, M.
Uddin, Shihab
Masuduzzaman, M.
description For facilitating users from information overloading by finding recent trends in twitter, several techniques are proposed. However, most of these techniques need to process extensive data. Therefore, in this paper, a geofencing-based recent trends identification technique is proposed, which acquires data based on a geofence. Afterwards, they are cleaned and the weight of these tweet data is calculated. For that, the frequency of tweet texts and hashtags are taken into account along with a boosting factor. Thereafter, they are ranked to recommend recent trends to the user. This proposed technique is applied in developing a system using Java and python. It is compared with other relevant systems, where it demonstrates that the performance of the proposed system is comparable. Over and above, since the proposed system integrates geofencing feature, it is more preferable over other systems.
doi_str_mv 10.1088/1757-899X/769/1/012008
format article
fullrecord <record><control><sourceid>proquest_iop_j</sourceid><recordid>TN_cdi_proquest_journals_2562154830</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2562154830</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3128-621d49cb7e36b4fe50ebfee370281b3d22f3a1442a836a2ba70ee12ee445a18b3</originalsourceid><addsrcrecordid>eNqFkF1LwzAUhoMoOKd_QQreeFObrzbp5ZjbHEwEneBdSNoTyXBNTTrEf29HZSIIXp0c8j7vgQehS4JvCJYyIyIXqSzLl0wUZUYyTCjG8giNDh_Hh7ckp-gsxg3GheAcj9BkkizAW2gq17ymRkeok0eooOmSdYCmjsmy7hdnXaU755vEBr9N1h-u6yAkt7rT5-jE6rcIF99zjJ7ns_X0Ll09LJbTySqtGKEyLSipeVkZAaww3EKOwVgAJjCVxLCaUss04ZxqyQpNjRYYgFAAznNNpGFjdDX0tsG_7yB2auN3oelPKpr37TmXDPepYkhVwccYwKo2uK0On4pgtdel9ibU3orqdSmiBl09SAfQ-fan-V_o-g_o_mn2K6ba2rIvjBB4xQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2562154830</pqid></control><display><type>article</type><title>A Geofencing-based Recent Trends Identification from Twitter Data</title><source>Publicly Available Content Database</source><source>Free Full-Text Journals in Chemistry</source><creator>Saef Ullah Miah, M. ; Sadid Tahsin, M. ; Azad, Saiful ; Rabby, Gollam ; Sirajul Islam, M. ; Uddin, Shihab ; Masuduzzaman, M.</creator><creatorcontrib>Saef Ullah Miah, M. ; Sadid Tahsin, M. ; Azad, Saiful ; Rabby, Gollam ; Sirajul Islam, M. ; Uddin, Shihab ; Masuduzzaman, M.</creatorcontrib><description>For facilitating users from information overloading by finding recent trends in twitter, several techniques are proposed. However, most of these techniques need to process extensive data. Therefore, in this paper, a geofencing-based recent trends identification technique is proposed, which acquires data based on a geofence. Afterwards, they are cleaned and the weight of these tweet data is calculated. For that, the frequency of tweet texts and hashtags are taken into account along with a boosting factor. Thereafter, they are ranked to recommend recent trends to the user. This proposed technique is applied in developing a system using Java and python. It is compared with other relevant systems, where it demonstrates that the performance of the proposed system is comparable. Over and above, since the proposed system integrates geofencing feature, it is more preferable over other systems.</description><identifier>ISSN: 1757-8981</identifier><identifier>EISSN: 1757-899X</identifier><identifier>DOI: 10.1088/1757-899X/769/1/012008</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Data acquisition ; Geofences ; Trends</subject><ispartof>IOP conference series. Materials Science and Engineering, 2020-02, Vol.769 (1), p.12008</ispartof><rights>Published under licence by IOP Publishing Ltd</rights><rights>2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3128-621d49cb7e36b4fe50ebfee370281b3d22f3a1442a836a2ba70ee12ee445a18b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2562154830?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590</link.rule.ids></links><search><creatorcontrib>Saef Ullah Miah, M.</creatorcontrib><creatorcontrib>Sadid Tahsin, M.</creatorcontrib><creatorcontrib>Azad, Saiful</creatorcontrib><creatorcontrib>Rabby, Gollam</creatorcontrib><creatorcontrib>Sirajul Islam, M.</creatorcontrib><creatorcontrib>Uddin, Shihab</creatorcontrib><creatorcontrib>Masuduzzaman, M.</creatorcontrib><title>A Geofencing-based Recent Trends Identification from Twitter Data</title><title>IOP conference series. Materials Science and Engineering</title><addtitle>IOP Conf. Ser.: Mater. Sci. Eng</addtitle><description>For facilitating users from information overloading by finding recent trends in twitter, several techniques are proposed. However, most of these techniques need to process extensive data. Therefore, in this paper, a geofencing-based recent trends identification technique is proposed, which acquires data based on a geofence. Afterwards, they are cleaned and the weight of these tweet data is calculated. For that, the frequency of tweet texts and hashtags are taken into account along with a boosting factor. Thereafter, they are ranked to recommend recent trends to the user. This proposed technique is applied in developing a system using Java and python. It is compared with other relevant systems, where it demonstrates that the performance of the proposed system is comparable. Over and above, since the proposed system integrates geofencing feature, it is more preferable over other systems.</description><subject>Data acquisition</subject><subject>Geofences</subject><subject>Trends</subject><issn>1757-8981</issn><issn>1757-899X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqFkF1LwzAUhoMoOKd_QQreeFObrzbp5ZjbHEwEneBdSNoTyXBNTTrEf29HZSIIXp0c8j7vgQehS4JvCJYyIyIXqSzLl0wUZUYyTCjG8giNDh_Hh7ckp-gsxg3GheAcj9BkkizAW2gq17ymRkeok0eooOmSdYCmjsmy7hdnXaU755vEBr9N1h-u6yAkt7rT5-jE6rcIF99zjJ7ns_X0Ll09LJbTySqtGKEyLSipeVkZAaww3EKOwVgAJjCVxLCaUss04ZxqyQpNjRYYgFAAznNNpGFjdDX0tsG_7yB2auN3oelPKpr37TmXDPepYkhVwccYwKo2uK0On4pgtdel9ibU3orqdSmiBl09SAfQ-fan-V_o-g_o_mn2K6ba2rIvjBB4xQ</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Saef Ullah Miah, M.</creator><creator>Sadid Tahsin, M.</creator><creator>Azad, Saiful</creator><creator>Rabby, Gollam</creator><creator>Sirajul Islam, M.</creator><creator>Uddin, Shihab</creator><creator>Masuduzzaman, M.</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>L6V</scope><scope>M7S</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20200201</creationdate><title>A Geofencing-based Recent Trends Identification from Twitter Data</title><author>Saef Ullah Miah, M. ; Sadid Tahsin, M. ; Azad, Saiful ; Rabby, Gollam ; Sirajul Islam, M. ; Uddin, Shihab ; Masuduzzaman, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3128-621d49cb7e36b4fe50ebfee370281b3d22f3a1442a836a2ba70ee12ee445a18b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Data acquisition</topic><topic>Geofences</topic><topic>Trends</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Saef Ullah Miah, M.</creatorcontrib><creatorcontrib>Sadid Tahsin, M.</creatorcontrib><creatorcontrib>Azad, Saiful</creatorcontrib><creatorcontrib>Rabby, Gollam</creatorcontrib><creatorcontrib>Sirajul Islam, M.</creatorcontrib><creatorcontrib>Uddin, Shihab</creatorcontrib><creatorcontrib>Masuduzzaman, M.</creatorcontrib><collection>Open Access: IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>IOP conference series. Materials Science and Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Saef Ullah Miah, M.</au><au>Sadid Tahsin, M.</au><au>Azad, Saiful</au><au>Rabby, Gollam</au><au>Sirajul Islam, M.</au><au>Uddin, Shihab</au><au>Masuduzzaman, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Geofencing-based Recent Trends Identification from Twitter Data</atitle><jtitle>IOP conference series. Materials Science and Engineering</jtitle><addtitle>IOP Conf. Ser.: Mater. Sci. Eng</addtitle><date>2020-02-01</date><risdate>2020</risdate><volume>769</volume><issue>1</issue><spage>12008</spage><pages>12008-</pages><issn>1757-8981</issn><eissn>1757-899X</eissn><abstract>For facilitating users from information overloading by finding recent trends in twitter, several techniques are proposed. However, most of these techniques need to process extensive data. Therefore, in this paper, a geofencing-based recent trends identification technique is proposed, which acquires data based on a geofence. Afterwards, they are cleaned and the weight of these tweet data is calculated. For that, the frequency of tweet texts and hashtags are taken into account along with a boosting factor. Thereafter, they are ranked to recommend recent trends to the user. This proposed technique is applied in developing a system using Java and python. It is compared with other relevant systems, where it demonstrates that the performance of the proposed system is comparable. Over and above, since the proposed system integrates geofencing feature, it is more preferable over other systems.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1757-899X/769/1/012008</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1757-8981
ispartof IOP conference series. Materials Science and Engineering, 2020-02, Vol.769 (1), p.12008
issn 1757-8981
1757-899X
language eng
recordid cdi_proquest_journals_2562154830
source Publicly Available Content Database; Free Full-Text Journals in Chemistry
subjects Data acquisition
Geofences
Trends
title A Geofencing-based Recent Trends Identification from Twitter Data
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T06%3A59%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_iop_j&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Geofencing-based%20Recent%20Trends%20Identification%20from%20Twitter%20Data&rft.jtitle=IOP%20conference%20series.%20Materials%20Science%20and%20Engineering&rft.au=Saef%20Ullah%20Miah,%20M.&rft.date=2020-02-01&rft.volume=769&rft.issue=1&rft.spage=12008&rft.pages=12008-&rft.issn=1757-8981&rft.eissn=1757-899X&rft_id=info:doi/10.1088/1757-899X/769/1/012008&rft_dat=%3Cproquest_iop_j%3E2562154830%3C/proquest_iop_j%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c3128-621d49cb7e36b4fe50ebfee370281b3d22f3a1442a836a2ba70ee12ee445a18b3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2562154830&rft_id=info:pmid/&rfr_iscdi=true