Cultural and Geolocation Aspects of Communication in Twitter

Elena Daehnhardt, Nicholas Taylor, Yanguo Jing

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review


Web applications exploit user information from so- cial networks and online user activities to facilitate interaction and create an enhanced user experience. Due to privacy issues however, it might be difficult to extract user data from social network, in particu- lar location data. For instance, information on user location depends on users’ agreement to share own ge- ographic data. Instead of directly collecting personal user information, we aim to infer user preferences by detecting behavior patterns from publicly available micro blogging content and users’ followers’ network. With statistical and machine-learning methods, we employ Twitter-specific features to predict country origin of users on Twitter with an accuracy of more than 90% for users from the most active countries. We further investigate users’ interpersonal communi- cation with their followers. Our findings reveal that belonging to a particular cultural group is playing an important role in increasing users responses to their friends. The knowledge on user cultural origins thus could provide a differentiated state-of-the-art user ex- perience in microblogs, for instance, in friend recom- mendation scenario.
Original languageEnglish
Title of host publication3rd ASE International Conference on Social Informatics (2014)
PublisherAcademy of Science and Engineering
Number of pages12
ISBN (Print)978-1-62561-003-4
Publication statusPublished - 14 Dec 2014
EventThe 3rd ASE International Conference on Social Informatics - Harvard University,, MA, USA, Cambridge, United States
Duration: 13 Dec 201416 Dec 2014


ConferenceThe 3rd ASE International Conference on Social Informatics
Country/TerritoryUnited States
Internet address


  • Learning systems
  • Positive Lons
  • Communication
  • preference behavior
  • social network
  • cation
  • Statistical learning
  • User Preferences
  • User experience
  • Web Application
  • privacy
  • Machine learning
  • Predict
  • scenario


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