- Entities of interest
- Discovery in digital traces
- Award date
- 16 June 2017
- Number of pages
- Document type
- PhD thesis
- Faculty of Science (FNWI)
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. 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 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. We propose computational methods that aim to support the exploration and sense-making process of large collections of textual digital traces, e.g., emails and social media streams. We address methods for analyzing the textual content of digital traces, and the contexts in which they are created, with the goal of predicting people’s future activity, by leveraging their historic digital traces.
- SIKS dissertation series no. 2017-23.
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