Search engines are the primary gateways of information. Yet, they do not take into account the credibility of search results. There is a growing concern that YouTube, the second largest search engine and the most popular video-sharing platform, has been promoting and recommending misinformative content for certain search topics. In this study, we audit YouTube to verify those claims. Our audit experiments investigate whether personalization (based on age, gender, geolocation, or watch history) contributes to amplifying misinformation. After shortlisting five popular topics known to contain misinformative content and compiling associated search queries representing them, we conduct two sets of audits-Search-and Watch-misinformative audits. Our audits resulted in a dataset of more than 56K videos compiled to link stance (whether promoting misinformation or not) with the personalization attribute audited. Our videos correspond to three major YouTube components: search results, Up-Next, and Top 5 recommendations. We find that demographics, such as, gender, age, and geolocation do not have a significant effect on amplifying misinformation in returned search results for users with brand new accounts. On the other hand, once a user develops a watch history, these attributes do affect the extent of misinformation recommended to them. Further analyses reveal a filter bubble effect, both in the Top 5 and Up-Next recommendations for all topics, except vaccine controversies; for these topics, watching videos that promote misinformation leads to more misinformative video recommendations. In conclusion, YouTube still has a long way to go to mitigate misinformation on its platform.

Graph data mining is defined as searching in an input graph for all subgraphs that satisfy some property that makes them interesting to the user. Examples of graph data mining problems include frequent subgraph mining, counting motifs, and enumerating cliques. These problems differ from other graph processing problems such as PageRank or shortest path in that graph data mining requires searching through an exponential number of subgraphs. Most current parallel graph analytics systems do not provide good support for graph data mining. One notable exception is Arabesque, a system that was built specifically to support graph data mining. Arabesque provides a simple programming model to express graph data mining computations, and a highly scalable and efficient implementation of this model, scaling to billions of subgraphs on hundreds of cores. This demonstration will showcase the Arabesque system, focusing on the end-user experience and showing how Arabesque can be used to simply and efficiently solve practical graph data mining problems that would be difficult with other systems.