Scott A. Hale

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.

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.

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.

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.

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.