Classification of twitter accounts into automated agents and human users
| cam.issuedOnline | 2017-07-31 | |
| dc.contributor.author | Gilani, Z | |
| dc.contributor.author | Kochmar, E | |
| dc.contributor.author | Crowcroft, J | |
| dc.contributor.orcid | Crowcroft, Jonathon [0000-0002-7013-0121] | |
| dc.date.accessioned | 2019-10-30T00:30:14Z | |
| dc.date.available | 2019-10-30T00:30:14Z | |
| dc.date.issued | 2017-07-31 | |
| 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.identifier.doi | 10.17863/CAM.45234 | |
| dc.identifier.isbn | 9781450349932 | |
| dc.identifier.uri | https://www.repository.cam.ac.uk/handle/1810/298180 | |
| dc.language.iso | eng | |
| dc.publisher | ACM | |
| dc.publisher.url | http://dx.doi.org/10.1145/3110025.3110091 | |
| dc.rights | All rights reserved | |
| dc.subject | 4605 Data Management and Data Science | |
| dc.subject | 46 Information and Computing Sciences | |
| dc.subject | 4608 Human-Centred Computing | |
| dc.title | Classification of twitter accounts into automated agents and human users | |
| dc.type | Conference Object | |
| prism.endingPage | 496 | |
| prism.publicationDate | 2017 | |
| prism.publicationName | Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 | |
| prism.startingPage | 489 | |
| pubs.conference-finish-date | 2017-08-03 | |
| pubs.conference-name | ASONAM '17: Advances in Social Networks Analysis and Mining 2017 | |
| pubs.conference-start-date | 2017-07-31 | |
| rioxxterms.licenseref.startdate | 2017-07-31 | |
| rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
| rioxxterms.type | Conference Paper/Proceeding/Abstract | |
| rioxxterms.version | AM | |
| rioxxterms.versionofrecord | 10.1145/3110025.3110091 |
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