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 social 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 particular location data. For instance, information on user location depends on users’ agreement to share own geographic data. Instead of directly collecting personal user information, we aim to infer user preferences by detecting behaviour patterns from publicly available microblogging 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 communication 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 experience in microblogs, for instance, in friend recommendation 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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