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dc.contributor.authorGilani, Z
dc.contributor.authorKochmar, Ekaterina
dc.contributor.authorCrowcroft, Jonathon
dc.date.accessioned2019-10-30T00:30:14Z
dc.date.available2019-10-30T00:30:14Z
dc.date.issued2017-07-31
dc.identifier.isbn9781450349932
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/298180
dc.description.abstract© 2017 Association for Computing Machinery. Online social networks (OSNs) have seen a remarkable rise in the presence of surreptitious automated accounts. Massive human user-base and business-supportive operating model of social networks (such as Twitter) facilitates the creation of automated agents. In this paper we outline a systematic methodology and train a classifier to categorise Twitter accounts into ‘automated’ and ‘human’ users. To improve classification accuracy we employ a set of novel steps. First, we divide the dataset into four popularity bands to compensate for differences in types of accounts. Second, we create a large ground truth dataset using human annotations and extract relevant features from raw tweets. To judge accuracy of the procedure we calculate agreement among human annotators as well as with a bot detection research tool. We then apply a Random Forests classifier that achieves an accuracy close to human agreement. Finally, as a concluding step we perform tests to measure the efficacy of our results.
dc.publisherACM
dc.rightsAll rights reserved
dc.titleClassification of twitter accounts into automated agents and human users
dc.typeConference Object
prism.endingPage496
prism.publicationDate2017
prism.publicationNameProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
prism.startingPage489
dc.identifier.doi10.17863/CAM.45234
rioxxterms.versionofrecord10.1145/3110025.3110091
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-07-31
dc.contributor.orcidCrowcroft, Jonathon [0000-0002-7013-0121]
dc.publisher.urlhttps://doi.org/10.1145/3110025
rioxxterms.typeConference Paper/Proceeding/Abstract
cam.issuedOnline2017-07-31
pubs.conference-nameASONAM '17: Advances in Social Networks Analysis and Mining 2017
pubs.conference-start-date2017-07-31
pubs.conference-finish-date2017-08-03
rioxxterms.freetoread.startdate2018-07-31


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