  In the era of big data, we continuously - and at times unknowingly - leave
behind digital traces, by browsing, sharing, posting, liking, searching,
watching, and listening to online content. When aggregated, these digital
traces can provide powerful insights into the behavior, preferences,
activities, and traits of people. While many have raised privacy concerns
around the use of aggregated digital traces, it has undisputedly brought us
many advances, from the search engines that learn from their users and enable
our access to unforeseen amounts of data, knowledge, and information, to, e.g.,
the discovery of previously unknown adverse drug reactions from search engine
logs.
  Whether in online services, journalism, digital forensics, law, or research,
we increasingly set out to exploring large amounts of digital traces to
discover new information. Consider for instance, the Enron scandal, Hillary
Clinton's email controversy, or the Panama papers: cases that revolve around
analyzing, searching, investigating, exploring, and turning upside down large
amounts of digital traces to gain new insights, knowledge, and information.
This discovery task is at its core about "finding evidence of activity in the
real world."
  This dissertation revolves around discovery in digital traces, and sits at
the intersection of Information Retrieval, Natural Language Processing, and
applied Machine Learning. We propose computational methods that aim to support
the exploration and sense-making process of large collections of digital
traces. We focus on textual traces, e.g., emails and social media streams, and
address two aspects that are central to discovery in digital traces.
