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The Spatial Analysis of Weibo Check-in Data—— The Case Study of Wuhan
6 references·0 citing·29 second hop
The Spatial Analysis of Weibo Check-in Data—— The Case Study of Wuhan (2013)The Spatial Analysis of Weibo Check-in Da…Exploring Millions of Footprints in Location Sharing Services (2021)Exploring Millions of Footpri…Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr (2013)Spatial, temporal, and socioe…Journal of Spatial Information Science (2011)Journal of Spatial Informatio…Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment (2010)Predicting Elections with Twi…Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection (2010)Measuring geographical regula…Exploring place through user-generated content: Using Flickr to describe city cores (2010)Exploring place through user-…The political blogosphere and the 2004 U.S. election (2005)The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods (2009)Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2014)Network analysis reveals open forums and echo chambers in social media discussions of climate change (2015)The Development and Psychometric Properties of LIWC2015 (2015)Social media and loneliness: Why an Instagram picture may be worth more than a thousand Twitter words (2016)Citizens as sensors: the world of volunteered geography (2007)Why we tag (2007)Geographic Information Analysis (2010)Extracting and understanding urban areas of interest using geotagged photos (2015)Social media analytics for natural disaster management (2017)Social-media data for urban sustainability (2018)Interactive Spatial Data Analysis (1995)How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets (2010)Twitter mood predicts the stock market (2011)Social Media and the City: Rethinking Urban Socio-Spatial Inequality Using User-Generated Geographic Information (2015)Social Media Data Can Be Used to Understand Tourists’ Preferences for Nature‐Based Experiences in Protected Areas (2017)Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019)The scaling laws of human travel (2006)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Geo-located Twitter as proxy for global mobility patterns (2014)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Discovery of unusual regional social activities using geo-tagged microblogs (2011)Jasmine (2011)Location-Based Social Networks: Users (2011)The convergence of GIS and social media: challenges for GIScience (2011)A review of volunteered geographic information quality assessment methods (2016)
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References

  • 2010Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment2,649 cited
  • 2021Exploring Millions of Footprints in Location Sharing Services685 cited
  • 2011Journal of Spatial Information Science457 cited
  • 2013Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr420 cited
  • 2010Exploring place through user-generated content: Using Flickr to describe city cores328 cited
  • 2010Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection301 cited

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