Doctoral student Shadi Rezapour and Assistant Professor Jana Diesner will present a paper at the 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017), which will be held February 25-March 1 in Portland, Oregon. CSCW brings together experts from industry and academia to explore the technical, social, material, and theoretical challenges of designing technology to support collaborative work and life activities.
Rezapour and Diesner will present, "Classification and Detection of Micro-Level Impact of Issue-Focused Films based on Reviews."
Abstract: We present novel research at the intersection of review mining and impact assessment of issue-focused information products, namely documentary films. We develop and evaluate a theoretically grounded classification schema, related codebook, corpus annotation, and prediction model for detecting multiple types of impact that documentaries can have on individuals, such as change versus reaffirmation of behavior, cognition, and emotions, based on user-generated content, i.e., reviews. This work broadens the scope of review mining tasks, which typically comprise the prediction of ratings, helpfulness, and opinions. Our results suggest that documentaries can change or reinforce peoples’ conception of an issue. We perform supervised learning to predict impact on the sentence level by using data driven as well as predefined linguistic, lexical, and psychological features; achieving an accuracy rate of 81% (F1) when using a Random Forest classifier, and 73% with a Support Vector Machine.
Rezapour, a second-year doctoral student studying with Diesner, received the ACM-W Scholarship to attend CSCW 2017. She is conducting research on topics related to natural language processing, machine learning, and information retrieval.
Diesner joined the iSchool faculty in 2012 and is a 2016 Dori J. Maynard Senior Fellow. Her research in human-centered data science and computational social sciences combines theories and methods from natural language processing, social network analysis, and machine learning. The presented paper is part of her lab's work on assessing the impact of information on individuals, communities, and society.