
Abstract In the last years big social networks like Facebook or Twitter admit that on their networks are fake and duplicate accounts, fake news and fake likes. With these accounts, their creators can distribute false information, support or attack an idea, a product, or an election candidate, influencing real network users in making a decision. In this paper, we present our system build with the aim of identifying fake users and fake news in the Twitter social network.
| 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). | 71 | |
| 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. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
