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The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This article supports geo-social personalized queries in streaming data environments. We define temporal geo-social queries that provide users with real-time personalized answers based on their social graph. The new queries allow incorporating keyword search to get personalized results that are relevant to certain topics. To efficiently support these queries, we propose an indexing framework that provides lightweight and effective real-time indexing to digest geo-social data in real time. The framework distinguishes highly dynamic data from relatively stable data and uses appropriate data structures and a storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide a real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.
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Up-and-Coming or Down-and-Out? Social Media Popularity as an Indicator of Neighborhood Change
By quantifying Twitter activity and sentiment for each of 274 neighborhood areas in New York City, this study introduces the Neighborhood Popularity Index and correlates changes in the index with real estate prices, a common measure of neighborhood change. Results show that social media provide both a near-real-time indicator of shifting attitudes toward neighborhoods and an early warning measure of future changes in neighborhood composition and demand. Although social media data provide an important complement to traditional data sources, the use of social media for neighborhood studies raises concerns regarding data accessibility and equity issues in data representativeness and bias.
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- Award ID(s):
- 1653772
- PAR ID:
- 10323407
- Date Published:
- Journal Name:
- Journal of Planning Education and Research
- ISSN:
- 0739-456X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/0739456X21998445
