Publication:
Event estimation accuracy of social sensing with facebook for social internet of vehicles

dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorAkan, Özgür Barış
dc.contributor.kuauthorÇepni, Kardelen
dc.contributor.kuauthorÖzger, Mustafa
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-11-09T13:20:37Z
dc.date.issued2018
dc.description.abstractSocial Internet of Vehicles (SIoV) is a new paradigm that enables social relationships among vehicles via the Internet. People in the vehicles using online social networks (OSNs) can be an integral part of SIoV that enables the collection of data for sensing a physical phenomenon, i.e., social sensing. In this paper, we study the main social sensing mechanism in Facebook, comment thread network (CTN), which is based on the interactions of users through user walls in Facebook for SIoV. After seeing their commuters' contents about an event, users either add comments or like these posts, and Facebook CTN emerges as a social sensing medium in estimation of an event through social consensus. For the first time, this paper investigates the social sensing capability of Facebook CTN, i.e., the accuracy of collective observations for SIoV. The accuracy depends on the user characteristics and the features of the OSN, since perceptions of the users and how they use Facebook may manipulate their observation signals. We analyze the reliability of Facebook CTN for varying user behaviors, user relationships, Facebook features, and network size. The results indicate that the polarized weighting of the observations and the use of less reliable post types in CTN deteriorate the accuracy of the estimate signal, i.e., social consensus. Furthermore, the selection of users is likely to be an important factor in social sensing.
dc.description.fulltextYES
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.issue4
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipN/A
dc.description.versionAuthor's final manuscript
dc.description.volume5
dc.identifier.doi10.1109/JIOT.2018.2846697
dc.identifier.eissn2327-4662
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR02299
dc.identifier.issn2327-4662
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85048573519
dc.identifier.urihttps://doi.org/10.1109/JIOT.2018.2846697
dc.identifier.wos441428700020
dc.keywordsEvent estimation
dc.keywordsFacebook
dc.keywordsSocial Internet of Vehicles (SIoV)
dc.keywordsSocial networks
dc.keywordsSocial sensing
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.grantnoNA
dc.relation.ispartofIEEE Internet of Things
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/8911
dc.subjectComputer science
dc.subjectEngineering
dc.subjectTelecommunications
dc.titleEvent estimation accuracy of social sensing with facebook for social internet of vehicles
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorÖzger, Mustafa
local.contributor.kuauthorAkan, Özgür Barış
local.contributor.kuauthorÇepni, Kardelen
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Electrical and Electronics Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
relation.isOrgUnitOfPublication21598063-a7c5-420d-91ba-0cc9b2db0ea0
relation.isOrgUnitOfPublication3fc31c89-e803-4eb1-af6b-6258bc42c3d8
relation.isOrgUnitOfPublication.latestForDiscovery21598063-a7c5-420d-91ba-0cc9b2db0ea0
relation.isParentOrgUnitOfPublication8e756b23-2d4a-4ce8-b1b3-62c794a8c164
relation.isParentOrgUnitOfPublication434c9663-2b11-4e66-9399-c863e2ebae43
relation.isParentOrgUnitOfPublication.latestForDiscovery8e756b23-2d4a-4ce8-b1b3-62c794a8c164

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
8911.pdf
Size:
393.26 KB
Format:
Adobe Portable Document Format