Mining microblogs for culture-awareness in web adaptation
Daehnhardt, Elena Alexandrovna
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Prior studies in sociology and human-computer interaction indicate that persons from diﬀerent countries and cultural origins tend to have their preferences in real-life communication and the usage of web and social media applications. With Twitter data, statistical and machine learning tools, this study advances our understand ing of microblogging in respect of cultural diﬀerences and demonstrates possible solutions of inferring and exploiting cultural origins for building adaptive web ap plications. Our ﬁndings reveal statistically signiﬁcant diﬀerences in Twitter feature usage in respect of geographic locations of users. These diﬀerences in microblogger behaviour and user language deﬁned in user proﬁles enabled us to infer user country origins with an accuracy of more than 90%. Other user origin predictive solutions we proposed do not require other data sources and human involvement for training the models, enabling the high accuracy of user country inference when exploiting information extracted from a user followers’ network, or with data derived from Twitter proﬁles. With origin predictive models, we analysed communication and privacy preferences and built a culture-aware recommender system. Our analysis of friend responses shows that Twitter users tend to communicate mostly within their cultural regions. Usage of privacy settings showed that privacy perceptions diﬀer across cultures. Finally, we created and evaluated movie recommendation strategies considering user cultural groups, and addressed a cold-start scenario with a new user. We believe that the ﬁndings discussed give insights into the sociological and web research, in particular on cultural diﬀerences in online communication.