The proliferation of social media platforms changed the way people interact online. However, engagement with social media comes with a price, the users’ privacy. Breaches of users’ privacy, such as the Cambridge Analytica scandal, can reveal how the users’ data can be weaponized in political campaigns, which many times trigger hate speech and anti-immigration views. Hate speech detection is a challenging task due to the different sources of hate that can have an impact on the language used, as well as the lack of relevant annotated data. To tackle this, we collected and manually annotated an immigration-related dataset of publicly available Tweets in UK, US, and Canadian English. In an empirical study, we explored anti-immigration speech detection utilizing various language features (word n-grams, character n-grams) and measured their impact on a number of trained classifiers. Our work demonstrates that using word n-grams results in higher precision, recall, and f-score as compared to character n-grams. Finally, we discuss the implications of these results for future work on hate-speech detection and social media data analysis in general.

We present a study of collaboration with expert participants for the purpose of the responsible design of a conversational agent. The Delphi study was used to identify and develop design and evaluation criteria for an automated career support intervention. Career support tasks present complex design problems as they are highly personalized and the definition of success for a single intervention is ambiguous. The study engaged domain experts in a structured communication process to explore the opportunities and risks of introducing a conversational agent to complement existing services provided to young people. Three rounds of questionnaires were used to build consensus across the expert panel. The questionnaire design incorporated design fictions, qualitative data from the panel, and requirement statements. The study produced a validated set of criteria that can be used for the design and evaluation of a conversational agent, that aligns with professional ethics and intended outcomes for a career support intervention. Our approach demonstrates the value of mixed method Delphi studies to facilitate participatory design of conversational user experiences by bridging knowledge gaps between technical and domain experts. The resulting evaluation criteria establish a meaningful foundation for future human-centered conversation design for career support.

Carl Strathearn, Dimitra Gkatzia. Proceedings of the 14th International Conference on Natural Language Generation. 2021.

Nikolaos Panagiaris, Emma Hart, Dimitra Gkatzia. Proceedings of the 13th International Conference on Natural Language Generation. 2020.