The rapid growth of large scale event datasets with timestamps has given rise to the dynamically evolving multi-relational knowledge graphs. Temporal reasoning over such data brings on many challenges and is still not well understood. Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times. This observation has given rise to the recent interest in low-shot learning methods that are able to generalize from only a few examples. The existing approaches, however, are tailored to static knowledge graphs and not easily generalized to temporal settings, where data scarcity poses even bigger problems, due to the occurrence of new, previously unseen relations. The goal of my doctoral research is to introduce new approaches for learning meaningful representation that capture the dynamics of temporal knowledge graphs while tackling various existing challenges such as data scarcity.

Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in the recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraudity-such as “pump and dump” scams-where the goal is to artificially inflate the perceived worth of a currency, luring victims into investing before the fraudsters can sell their holdings. Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump. In this work, we propose and evaluate a computational approach that can automatically identify pump and dump scams as they unfold by combining information across social media platforms. We also develop a multi-modal approach for predicting whether a particular pump attempt will succeed or not. Finally, we analyze the prevalence of bots in cryptocurrency related tweets, and observe a significant increase in bot activity during the pump attempts.

Forecasting of geopolitical events is a notoriously difficult task, with experts failing to significantly outperform a random baseline across many types of forecasting events. One successful way to increase the performance of forecasting tasks is to turn to crowdsourcing: leveraging many forecasts from non-expert users. Simultaneously, advances in machine learning have led to models that can produce reasonable, although not perfect, forecasts for many tasks. Recent efforts have shown that forecasts can be further improved by ``hybridizing'' human forecasters: pairing them with the machine models in an effort to combine the unique advantages of both. In this demonstration, we present Synergistic Anticipation of Geopolitical Events (SAGE), a platform for human/computer interaction that facilitates human reasoning with machine models.