Abstract
Neighborhood change is one of the most widely discussed and disputed concepts in urban studies with gentrification dominating the debate in many circles. While many scholars have highlighted the conceptual ambiguities related to various concepts related to neighborhood ascent, research has paid little attention to how the non-academic sphere understands and employs such concepts. Some studies have started addressing this issue by employing qualitative text analysis and, more recently, computational methods. In this paper, we further contribute to this nascent literature by investigating (1) how American media sources portray the terms gentrification and urban revitalization respectively, and (2) if the sociopolitical orientations of the media sources are related to such portrayal by employing computational social science methods. We explore how media sources often choose to select a certain concept to produce certain narratives and discourses on neighborhood change. We also address how the terms gentrification and neighborhood revitalization are used in conjunction with other related terms such as displacement, inequality, investment, and improvement. We draw on web-scraping techniques to collect articles published in the 2010s from major media sources in the United States and conduct various text analyses to understand the relationship between media bias and the media portrayal of neighborhood ascent. In so doing, our study sheds light on the different perceptions and judgments of social groups regarding urban redevelopment processes and ultimately, offer lessons for public officials and planning experts who engage with the public when dealing with gentrification.
Presenters
Taesoo SongStudent, PhD Student, University of California, Berkeley, California, United States Flavia Leite
Student, PhD, University of California, Berkeley, California, United States
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
GENTRIFICATION; NEIGHBORHOOD CHANGE; MEDIA BIAS; TEXT ANALYSIS; COMPUTATIONAL SOCIAL SCIENCE
Digital Media
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