The problem of identifying rumors is of practical importance especially in online social networks, since information can diffuse more rapidly and widely than the of\ufb02ine counterpart. In this paper, we identify characteristics of rumors by examining the following three aspects of diffusion: temporal, structural, and linguistic. For the temporal characteristics, we propose a new periodic time series model that considers daily and external shock cycles, where the model demonstrates that rumor likely have \ufb02uctuations over time. We also identify key structural and linguistic differences in the spread of rumors and non-rumors. Our selected features classify rumors with high precision and recall in the range of 87% to 92%, that is higher than other states of the arts on rumor classi\ufb01cation.<\/p>\n","protected":false},"excerpt":{"rendered":"
The problem of identifying rumors is of practical importance especially in online social networks, since information can diffuse more rapidly and widely than the of\ufb02ine counterpart. In this paper, we identify characteristics of rumors by examining the following three aspects of diffusion: temporal, structural, and linguistic. For the temporal characteristics, we propose a new periodic […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13559],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-246431","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-social-sciences","msr-locale-en_us"],"msr_publishername":"","msr_edition":"In Proceedings of the 13th IEEE International Conference on Data Mining (ICDM'2013), Dallas, Texas, U.S.A., Dec. 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