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This is the dataset used for the study "Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls". Savvas Zannettou, Tristan Caulfield, William Setzer, Michael Sirivianos, Gianluca Stringhini, Jeremy Blackburn. Arxiv, 2019. DOI: 10.5281/zenodo.2558560 The dataset consists of the data released by Twitter on October 2018 for Russian and Iranian state-sponsored troll accounts, which is available at https://about.twitter.com/en_us/values/elections-integrity.html#data as well as intermediate data that we generated after processing the raw data. For instance, we include trained Word2Vec and LDA models, the output of our influence estimation experiments via Hawkes Processes, and a lot of other data necessary to reproduce the results in the paper. To use the provided data simply download the compressed file from <URL> and make sure that the uncompressed data folder is in the same directory as the IPython Notebook. The code used for this study can be found here: https://github.com/zsavvas/trolls_analysis Please cite our paper if any publication, of any form and kind results of you using this data: @article{zannettou2018let, title={Who let the trolls out? towards understanding state-sponsored trolls}, author={Zannettou, Savvas and Caulfield, Tristan and Setzer, William and Sirivianos, Michael and Stringhini, Gianluca and Blackburn, Jeremy}, journal={arXiv preprint arXiv:1811.03130}, year={2018} }
Twitter Data
Twitter Data
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 25 | |
| downloads | 3 |

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