S
Scott A. Hale
Researcher at University of Oxford
Publications - 94
Citations - 2270
Scott A. Hale is an academic researcher from University of Oxford. The author has contributed to research in topics: Social media & Collective action. The author has an hindex of 23, co-authored 91 publications receiving 1792 citations. Previous affiliations of Scott A. Hale include The Turing Institute & Eckerd College.
Papers
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Journal ArticleDOI
Where in the World Are You? Geolocation and Language Identification in Twitter
TL;DR: This article compares three automated language identification packages to Twitter's user interface language setting and to a human coding of languages to identify common sources of disagreement and demonstrates that in many cases user-entered profile locations differ from the physical locations from which users are actually tweeting.
Book
Political Turbulence: How Social Media Shape Collective Action
TL;DR: In this paper, the authors use experiments that test how social media influence citizens deciding whether or not to participate and identify which types of people are willing to participate at an early stage in a mobilization when there are few supporters or signals of viability.
Proceedings ArticleDOI
Challenges and frontiers in abusive content detection
TL;DR: In this article, the authors delineate and clarify the main challenges and frontiers in the abusive content detection field, critically evaluate their implications and discuss potential solutions, and highlight ways in which social scientific insights can advance research.
Proceedings ArticleDOI
Demographic Inference and Representative Population Estimates from Multilingual Social Media Data
Zijian Wang,Scott A. Hale,David Ifeoluwa Adelani,Przemyslaw A. Grabowicz,Timo Hartman,Fabian Flöck,David Jurgens +6 more
TL;DR: This work creates a new multimodal deep neural architecture for joint classification of age, gender, and organization-status of social media users that operates in 32 languages and substantially outperforms current state of the art while also reducing algorithmic bias.
Proceedings ArticleDOI
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings
TL;DR: A new evaluation framework for semantic change detection is proposed and it is found that using the whole time series is preferable over only comparing between the first and last time points; independently trained and aligned embeddings perform better than continuously trainedembeddings for long time periods; and that the reference point for comparison matters.