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Here we analyze the role and influence of socialbots on Twitter by determining how they contribute to retweet diffusions. We collect a large dataset of tweets during the 1st U.S. ... Presidential Debate in 2016 (#DebateNight) and we analyze its 1.5 million users from three perspectives: user influence, political behavior (partisanship and engagement) and botness. ... This research is sponsored in part by the Air Force Research Laboratory, under agreement number FA2386-15-1-4018. ...arXiv:1802.09808v3 fatcat:ubb4irsgend43olvccfovmkst4
This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades). ... For retweet cascades, we observe changes in distributions of tweet inter-arrival time and user influence, which will affect models that rely on these features. ... Acknowledgments This work is supported in part by AOARD Grant 19IOA078, Australian Research Council Project DP180101985, and the National eResearch Collaboration Tools and Resources (Nectar). ...arXiv:2003.09557v3 fatcat:oyca3rugorby5isxld3djo4p5q
We use YouTube URLs shared on Twitter as a sampling protocol to obtain a collection of videos, and we track their prevalence from 2015 to 2019. ... To obtain a list of items, one common method is sampling based on the item prevalence in social media streams. ... our organization's archive of the Twitter firehose 6 The role and influence of socialbots on Twitter during the first 2016 U.S. presidential debate firehose Twitter discussions that occurred during ...doi:10.25911/en55-sd26 fatcat:bkxvk4nbkfahflpqa2adycz7va