We present a technique to identify Twitter users we trust to be regular Twitter users and not spam or fake accounts. The technique starts with an initial set of trusted users based on Twitter\u2019s verified users and recursively includes other users the trusted users communicate with. Conversations must be initiated by trusted users. We show that this technique produces a set of users that is over 200 times larger than Twitter\u2019s verified users. Our evaluation shows that the share of non-spam users within the resulting set of trusted users is more than 92% while the share of non-spam users within all tweeting users is 74%.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a technique to identify Twitter users we trust to be regular Twitter users and not spam or fake accounts. The technique starts with an initial set of trusted users based on Twitter\u2019s verified users and recursively includes other users the trusted users communicate with. Conversations must be initiated by trusted users. We show […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13559,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-334751","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-social-sciences","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of the Eighth International AAAI Conference on Weblogs and Social 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