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Due to unforeseen circumstances this talk is POSTPONED. We apologize for the inconvenience and will reschedule at a later date.

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Title:

Impact Assessment of Information Products and Data Provenance

Speaker:

Jana Diesner - Assistant Professor, School of Information Sciences, University of Illinois at Urbana-Champaign

Talk Abstract:

The emerging field of human-centered data science has led to several transformative advances in research and technology: With groups of people generating digital data, some social effects can be measured instead of having to be estimated. Also, the availability of such data may allow us to listen to peoples' signals instead of having to ask them questions. Finally, both the structure and content of human interactions can be considered for data analysis, and applying mixed methods to such data is becoming a routine approach.

These advances have broadened the scope in possibilities in impact assessment research, among other fields. I present our work on developing new computational solutions for identifying the impact of information products on people by leveraging theories from linguistics and the social sciences as well as methods from natural language processing and machine learning. I focus on a study where we developed and evaluated a theoretically grounded categorization schema, codebook, corpus annotation, and prediction model for detecting multiple practically relevant types of impact that documentary films can have on individuals, such as change versus reaffirmation of people's behavior, cognition, and emotions. This work uses reviews as a form of user-generated content. We use linguistic, lexical, and psychological features for supervised learning; achieving an accuracy rate of about 81% (F1).

The outlined advances also imply several challenges: Verifying the accuracy of large-scale data is crucial for enabling collaborations, sharing data, and generating reliable results, but is challenging if the data provenance process lacks transparency. While choices about data collection, preparation and analysis are increasingly embedded in datasets and technologies, we still have a poor understanding of the impact of these decisions on research results and further actions. I present on our work on entity resolution of social network data, highlight the impact of common strategies and shortcomings on node and graph level properties, and discuss implications of biased results for decision and policy making.

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