![]() ![]() Whence differences in value priorities?: Individual, cultural, or artifactual sources. In Proceedings of the 2017 ACM on Web Science Conference (Troy, New York, USA) ( WebSci’17). Using Twitter data to estimate the relationship between short-term mobility and long-term migration. ![]() Fiorio Lee, Abel Guy, Cai Jixuan, Zagheni Emilio, Weber Ingmar, and Vinué Guillermo.Cultural divides and digital inequalities: Attitudes shaping Internet and social media divides. Springer International Publishing, Cham, 63– 77. The Use of Twitter in 2013 Italian Political Election. Di Fraia Guido and Missaglia Maria Carlotta.Association for Computational Linguistics, Minneapolis, Minnesota, 4171– 4186. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). BERT: Pre-training of deep bidirectional transformers for language understanding. Devlin Jacob, Chang Ming-Wei, Lee Kenton, and Toutanova Kristina.Twitter: Number of monthly active users 2010–2019. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (Baltimore, Maryland, USA) ( CSCW’14). Understanding individuals’ personal values from social media word use. Chen Jilin, Hsieh Gary, Mahmud Jalal U., and Nichols Jeffrey.Semantics derived automatically from language corpora contain human-like biases. Caliskan Aylin, Bryson Joanna J., and Narayanan Arvind.In Proceedings of the 30th International Conference on Neural Information Processing Systems (Barcelona, Spain) ( NIPS’16). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Bolukbasi Tolga, Chang Kai-Wei, Zou James, Saligrama Venkatesh, and Kalai Adam.Bollen Johan, Mao Huina, and Zeng Xiao-Jun.Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. Bollen Johan, Mao Huina, and Pepe Alberto.Enriching Word Vectors with Subword Information. Bojanowski Piotr, Grave Edouard, Joulin Armand, and Mikolov Tomas.The geographic embedding of online echo chambers: Evidence from the Brexit campaign. Bastos Marco, Mercea Dan, and Baronchelli Andrea.Annals of the American Association of Geographers 107, 5 ( 2017), 1194– 1215. Digital hegemonies: The localness of search engine results. Ballatore Andrea, Graham Mark, and Sen Shilad.Linking Twitter and survey data: The impact of survey mode and demographics on consent rates across three UK studies. l., Sloan Luke, Jessop Curtis, Williams Matthew L., and Burnap Pete. Using Social Media to Measure Labor Market Flows. Antenucci Dolan, Cafarella Michael, Levenstein Margaret, Ré Christopher, and Shapiro Matthew D.Large-scale physical activity data reveal worldwide activity inequality. Althoff Tim, Sosič Rok, Hicks Jennifer, King Abby C., Delp Scott, and Leskovec Jure.In Proceedings of the 2018 World Wide Web Conference (Lyon, France) ( DOI: Socioeconomic dependencies of linguistic patterns in Twitter: A multivariate analysis. Abitbol Jacob Levy, Karsai Márton, Magué Jean-Philippe, Chevrot Jean-Pierre, and Fleury Eric.Our method is generic, and we believe it is useful for social sciences specialists, such as demographers and sociologists, that can use their domain knowledge and expertise to create their own Online Values Inquiries, allowing them to analyze human values in the online environment. We also show that some online values are highly correlated (up to c = 0.69, p < 0.05) with the corresponding offline values, especially religion-related ones. We observe that our methodology is indeed capable of capturing human values online for different counties and different topics. We create a list of 22 Online Values Inquiries (OVI), each one capturing different questions from the World Values Survey, related to several values such as religion, science, and abortion. Our methodology is applied with a dataset of 1.7 billion tweets, and then we identify their location among 59 countries. In this work we develop a methodology for measuring data from textual online sources using word embedding models, to create a country-based online human values index that captures cultural traits and values worldwide. As a global platform, the Internet is a great source of information for researching the online culture of many different countries. What is being created and published online is a reflection of people’s values and beliefs. It has the potential of constructing or modifying the opinion, the mental perception, and the values of individuals. As the Internet grows in number of users and in the diversity of services, it becomes more influential on peoples lives. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |