The Tweet and the City: Comparing Twitter Activities in Informational World Cities

This paper informetrically monitors Twitter activities that are related to 31 Informational World Cities. It is a big data analysis of 18 million tweets that have been downloaded via Twitter’s Search API (content-based approach) and Twitter’s Streaming API (location-based approach). The Tweets have been filtered either by search terms (i. e. the city’s name) or geo-locations (coordinates of a city). The analysis was made by mainly using quantitative statistic methods endorsed by several qualitative investigations. It shows that tweet activity related to Informational World Cities varies from city to city. A city’s area or its size of population does not necessarily affect these activities. Factors like the penetration rate of smart phones, number of tourists etc. influences the amount of tweets that are produced in or about a city. Topics are mostly event-driven or related to sports and politics. City names are popular in spam tweets and they are often chained to draw the attention to messages which are not city-related at all (e. g., religious comments). The paper presents an approach for quantitatively analysing tweeting behaviour in Information World Cities to prospectively find distinct indicators of how Twitter activities in Informational World Cities can be classified and how they vary between the different cities.

[1]  Kristy Jones,et al.  ARC Centre of Excellence for Creative Industries and Innovation , 2009 .

[2]  Marina Schmid,et al.  The Third Wave , 2016 .

[3]  Sarah L. Nesbeitt Ethnologue: Languages of the World , 1999 .

[4]  Wei Hu,et al.  Twitter spammer detection using data stream clustering , 2014, Inf. Sci..

[5]  A. Bruns,et al.  #qldfloods and @QPSMedia: Crisis Communication on Twitter in the 2011 South East Queensland Floods , 2012 .

[6]  Jimmy J. Lin,et al.  Visualizing the "Pulse" of World Cities on Twitter , 2013, ICWSM.

[7]  Kyumin Lee,et al.  You are where you tweet: a content-based approach to geo-locating twitter users , 2010, CIKM.

[8]  Adriana Gamazo,et al.  EURYDICE (2013): Key data on teachers and school leaders in Europe. 2013 edition Eurydice report (Luxembourg Publications Office of the European Union) , 2013 .

[9]  C Weidemann Social Media Location Intelligence: The Next Privacy Battle - An ArcGIS add-in and Analysis of Geospatial Data Collected from Twitter.com , 2013 .

[10]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[11]  M. Castells,et al.  The Informational City. Information Technology, Economic Restructuring, and the Urban-Regional Process , 1995 .

[12]  Philip Barker,et al.  Blogs, Wikipedia, Second Life, and beyond: From Production to Produsage , 2009 .

[13]  Tom McGorrian The Unisys security index , 2014 .

[14]  Manuel Castells,et al.  EUROPEAN CITIES, THE INFORMATIONAL SOCIETY, AND THE GLOBAL ECONOMY , 1993 .

[15]  Barry Wellman,et al.  Geography of Twitter networks , 2012, Soc. Networks.

[16]  Neil Leach,et al.  The Informational City , 2015 .

[17]  Mourad Oussalah,et al.  A software architecture for Twitter collection, search and geolocation services , 2013, Knowl. Based Syst..

[18]  Axel Bruns,et al.  Blogs, Wikipedia, Second Life, and Beyond: From Production to Produsage , 2008 .

[19]  Hongfei Yan,et al.  Comparing Twitter and Traditional Media Using Topic Models , 2011, ECIR.

[20]  Tom Baum,et al.  Seasonality in Tourism: Issues and Implications , 2001 .