Recognizing deviants in Social Networking Sites: Case study fupei.com

In the last few years, social networking sites (SNSs) have grown rapidly as a new media used by people to create and maintain relationship. Ironically, social network sites are also used to do deviant behaviors, i.e. pornography, racism, predators, and fake profiles. A need then emerges to reduce th...

Full description

Saved in:
Bibliographic Details
Main Authors: Yohannis, A.R., Sastramihardja, H.
Format: Conference Proceeding
Language:English
Subjects:
Communities
Correlation
Data mining
deviance
deviant characteristics
Information services
Internet
Social network services
social networking sites
Web sites
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 512
container_issue
container_start_page 507
container_title
container_volume 2
creator Yohannis, A.R.
Sastramihardja, H.
description In the last few years, social networking sites (SNSs) have grown rapidly as a new media used by people to create and maintain relationship. Ironically, social network sites are also used to do deviant behaviors, i.e. pornography, racism, predators, and fake profiles. A need then emerges to reduce those deviances. This research tries to recognize the deviants based on their characteristics. Descriptive and inferensial statistics are used to seek out the differences between deviants and nondeviants on certain attributes. Analysis finds the deviants and nondeviants are significantly different on certain attributes and not on some attributes. Some of those findings confirm the theories of deviance. Based on the findings, several design implications are proposed. A social control system then issued in order to reduce deviances in SNS.
doi_str_mv 10.1109/ICEEI.2009.5254685
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5254685</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5254685</ieee_id><sourcerecordid>5254685</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-83c37db2455de558949e4bce547594ee593e5e7e13a7799110ee6d7443cefcd03</originalsourceid><addsrcrecordid>eNo9kM1OwkAUhScqiYC8gG7mBVrn5962151pqjYhmgh7Uqa3ZBRawhQNPr0Qiauz-HK-5BwhbrWKtVZ0X-ZFUcZGKYrRICQZXoih0YhRkll1KUYaDACQttnVPzBmIEanDqlEk7oWkxA-lFJHYUIGhqJ4Z9etWv_j25Ws-ctXbR-kb-Wsc75ay1fuv7vd54nOfM_hQeZVYBn6fX2QzX7LPnbd5kYMmmodeHLOsZg_FfP8JZq-PZf54zTypPoos86m9dIAYs2IGQExLB0jpEjAjGQZOWVtqzQlOq5mTuoUwDpuXK3sWNz9aT0zL7Y7v6l2h8X5DfsL5eJPEA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Recognizing deviants in Social Networking Sites: Case study fupei.com</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Yohannis, A.R. ; Sastramihardja, H.</creator><creatorcontrib>Yohannis, A.R. ; Sastramihardja, H.</creatorcontrib><description>In the last few years, social networking sites (SNSs) have grown rapidly as a new media used by people to create and maintain relationship. Ironically, social network sites are also used to do deviant behaviors, i.e. pornography, racism, predators, and fake profiles. A need then emerges to reduce those deviances. This research tries to recognize the deviants based on their characteristics. Descriptive and inferensial statistics are used to seek out the differences between deviants and nondeviants on certain attributes. Analysis finds the deviants and nondeviants are significantly different on certain attributes and not on some attributes. Some of those findings confirm the theories of deviance. Based on the findings, several design implications are proposed. A social control system then issued in order to reduce deviances in SNS.</description><identifier>ISSN: 2155-6822</identifier><identifier>ISBN: 1424449138</identifier><identifier>ISBN: 9781424449132</identifier><identifier>EISSN: 2155-6830</identifier><identifier>DOI: 10.1109/ICEEI.2009.5254685</identifier><identifier>LCCN: 2009906190</identifier><language>eng</language><publisher>IEEE</publisher><subject>Communities ; Correlation ; Data mining ; deviance ; deviant characteristics ; Information services ; Internet ; Social network services ; social networking sites ; Web sites</subject><ispartof>2009 International Conference on Electrical Engineering and Informatics, 2009, Vol.2, p.507-512</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5254685$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27924,54554,54919,54931</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5254685$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yohannis, A.R.</creatorcontrib><creatorcontrib>Sastramihardja, H.</creatorcontrib><title>Recognizing deviants in Social Networking Sites: Case study fupei.com</title><title>2009 International Conference on Electrical Engineering and Informatics</title><addtitle>ICEEI</addtitle><description>In the last few years, social networking sites (SNSs) have grown rapidly as a new media used by people to create and maintain relationship. Ironically, social network sites are also used to do deviant behaviors, i.e. pornography, racism, predators, and fake profiles. A need then emerges to reduce those deviances. This research tries to recognize the deviants based on their characteristics. Descriptive and inferensial statistics are used to seek out the differences between deviants and nondeviants on certain attributes. Analysis finds the deviants and nondeviants are significantly different on certain attributes and not on some attributes. Some of those findings confirm the theories of deviance. Based on the findings, several design implications are proposed. A social control system then issued in order to reduce deviances in SNS.</description><subject>Communities</subject><subject>Correlation</subject><subject>Data mining</subject><subject>deviance</subject><subject>deviant characteristics</subject><subject>Information services</subject><subject>Internet</subject><subject>Social network services</subject><subject>social networking sites</subject><subject>Web sites</subject><issn>2155-6822</issn><issn>2155-6830</issn><isbn>1424449138</isbn><isbn>9781424449132</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9kM1OwkAUhScqiYC8gG7mBVrn5962151pqjYhmgh7Uqa3ZBRawhQNPr0Qiauz-HK-5BwhbrWKtVZ0X-ZFUcZGKYrRICQZXoih0YhRkll1KUYaDACQttnVPzBmIEanDqlEk7oWkxA-lFJHYUIGhqJ4Z9etWv_j25Ws-ctXbR-kb-Wsc75ay1fuv7vd54nOfM_hQeZVYBn6fX2QzX7LPnbd5kYMmmodeHLOsZg_FfP8JZq-PZf54zTypPoos86m9dIAYs2IGQExLB0jpEjAjGQZOWVtqzQlOq5mTuoUwDpuXK3sWNz9aT0zL7Y7v6l2h8X5DfsL5eJPEA</recordid><startdate>200908</startdate><enddate>200908</enddate><creator>Yohannis, A.R.</creator><creator>Sastramihardja, H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200908</creationdate><title>Recognizing deviants in Social Networking Sites: Case study fupei.com</title><author>Yohannis, A.R. ; Sastramihardja, H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-83c37db2455de558949e4bce547594ee593e5e7e13a7799110ee6d7443cefcd03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Communities</topic><topic>Correlation</topic><topic>Data mining</topic><topic>deviance</topic><topic>deviant characteristics</topic><topic>Information services</topic><topic>Internet</topic><topic>Social network services</topic><topic>social networking sites</topic><topic>Web sites</topic><toplevel>online_resources</toplevel><creatorcontrib>Yohannis, A.R.</creatorcontrib><creatorcontrib>Sastramihardja, H.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yohannis, A.R.</au><au>Sastramihardja, H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Recognizing deviants in Social Networking Sites: Case study fupei.com</atitle><btitle>2009 International Conference on Electrical Engineering and Informatics</btitle><stitle>ICEEI</stitle><date>2009-08</date><risdate>2009</risdate><volume>2</volume><spage>507</spage><epage>512</epage><pages>507-512</pages><issn>2155-6822</issn><eissn>2155-6830</eissn><isbn>1424449138</isbn><isbn>9781424449132</isbn><abstract>In the last few years, social networking sites (SNSs) have grown rapidly as a new media used by people to create and maintain relationship. Ironically, social network sites are also used to do deviant behaviors, i.e. pornography, racism, predators, and fake profiles. A need then emerges to reduce those deviances. This research tries to recognize the deviants based on their characteristics. Descriptive and inferensial statistics are used to seek out the differences between deviants and nondeviants on certain attributes. Analysis finds the deviants and nondeviants are significantly different on certain attributes and not on some attributes. Some of those findings confirm the theories of deviance. Based on the findings, several design implications are proposed. A social control system then issued in order to reduce deviances in SNS.</abstract><pub>IEEE</pub><doi>10.1109/ICEEI.2009.5254685</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2155-6822
ispartof 2009 International Conference on Electrical Engineering and Informatics, 2009, Vol.2, p.507-512
issn 2155-6822
2155-6830
language eng
recordid cdi_ieee_primary_5254685
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Communities
Correlation
Data mining
deviance
deviant characteristics
Information services
Internet
Social network services
social networking sites
Web sites
title Recognizing deviants in Social Networking Sites: Case study fupei.com
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T23%3A05%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Recognizing%20deviants%20in%20Social%20Networking%20Sites:%20Case%20study%20fupei.com&rft.btitle=2009%20International%20Conference%20on%20Electrical%20Engineering%20and%20Informatics&rft.au=Yohannis,%20A.R.&rft.date=2009-08&rft.volume=2&rft.spage=507&rft.epage=512&rft.pages=507-512&rft.issn=2155-6822&rft.eissn=2155-6830&rft.isbn=1424449138&rft.isbn_list=9781424449132&rft_id=info:doi/10.1109/ICEEI.2009.5254685&rft_dat=%3Cieee_6IE%3E5254685%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i90t-83c37db2455de558949e4bce547594ee593e5e7e13a7799110ee6d7443cefcd03%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5254685&rfr_iscdi=true