Web-scale provenance reconstruction of implicit information diffusion on social media

Fast, massive, and viral data diffused on social media affects a large share of the online population, and thus, the (prospective) information diffusion mechanisms behind it are of great interest to researchers. The (retrospective) provenance of such data is equally important because it contributes to the understanding of the relevance and trustworthiness of the information. Furthermore, computing provenance in a timely way is crucial for particular use cases and practitioners, such as online journalists that promptly need to assess specific pieces of information. Social media currently provide insufficient mechanisms for provenance tracking, publication and generation, while state-of-the-art on social media research focuses mainly on explicit diffusion mechanisms (like retweets in Twitter or reshares in Facebook).The implicit diffusion mechanisms remain understudied due to the difficulties of being captured and properly understood. From a technical side, the state of the art for provenance reconstruction evaluates small datasets after the fact, sidestepping requirements for scale and speed of current social media data. In this paper, we investigate the mechanisms of implicit information diffusion by computing its fine-grained provenance. We prove that explicit mechanisms are insufficient to capture influence and our analysis unravels a significant part of implicit interactions and influence in social media. Our approach works incrementally and can be scaled up to cover a truly Web-scale scenario like major events. We can process datasets consisting of up to several millions of messages on a single machine at rates that cover bursty behaviour, without compromising result quality. By doing that, we provide to online journalists and social media users in general, fine grained provenance reconstruction which sheds lights on implicit interactions not captured by social media providers. These results are provided in an online fashion which also allows for fast relevance and trustworthiness assessment

Einführung in das Thema PROVENANCE

In diesem Artikel wird Provenance in ihren verschiedenen Arten und Anwendungsbe- reichen vorgestellt. Es wird zwischen vier verschiedenen Provenancearten unterschie- den. Die Meta Data Provenance gilt als die allgemeinste Provenance und beschreibt alle Metadaten von einem Objekt. Die Information System Provenance beschränkt sich auf die in einem Informationssystem entstandenen digitalen Daten. Wenn ein Arbeitsablauf modelliert werden soll, wird die Workflow Provenance verwendet. Bei dieser wird ein Einblick in die drei Dimensionen Granularität, Domäne und Form wird gegeben. Um die Herkunft von Daten innerhalb einer Datenbank zu beschrei- ben, wird die Data Provenance verwendet. Diese kann zu dem Ergebnis einer Anfrage die Quelldaten ausgeben. Weiterhin gibt es eine Einführung in den PROV-Standard des W3C. Außerdem werden verschiedene Tools, darunter Kepler, zum Umgang mit Provenance vorgestellt