Using Knowledge Graphs to understand noisy naturalistic data has gained significant prominence in recent years. In this paper, we apply Knowledge Graphs to a new dataset of tweets of an ideologically far-right Twitter network by sourcing tweet histories of users who discussed QAnon in the summer of 2018 [1]. We further develop a new method that arms topic models with relational information from Knowledge Graphs and apply the new technique to study this dataset. Our analysis shows that users do not form a monolithic belief or social network, but rather comprise many smaller interlinking communities which discuss unique key political events (e.g., the January 6thCapitol riots).

The construction and application of knowledge graphs have seen a rapid increase across many disciplines in re-cent years. Additionally, the problem of uncovering relationships between developments in the COVID-19 pandemic and social me-dia behavior is of great interest to researchers hoping to curb the spread of the disease. In this paper we present a knowledge graph constructed from COVID-19 related tweets in the Los Angeles area, supplemented with federal and state policy announcements and disease spread statistics. By incorporating dates, topics, and events as entities, we construct a knowledge graph that describes the connections between these useful information. We use natural language processing and change point analysis to extract tweet-topic, tweet-date, and event-date relations. Further analysis on the constructed knowledge graph provides insight into how tweets reflect public sentiments towards COVID-19 related topics and how changes in these sentiments correlate with real-world events.

This paper aims to accelerate scientific discovery by studying link prediction in a semantic network. The nodes are unidentified concepts in machine learning, and the time-stamped edges indicate co-occurrence in scientific papers. Taking advantage of this temporal information, we perform node embedding on the graph at every year from 1994 to 2017, and apply two methods to find features for node pairs: the first method uses a transformer, while the other uses distance metrics combined with known link prediction features. The latter feature extraction technique with a 3-layer multi-layer perceptron achieved an AUC of 0.902 on predicting edges in the 2020 graph. Inspection of the resulting features suggests that the model does indeed pay attention to the dynamic nature of the features, e.g., how node-pair distance in embedding space changes over the years.