Multilingual Persuasion Detection: Video Games as an Invaluable Data Source for NLP

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Pöyhönen Teemu Hämäläinen Mika Alnajjar Khalid
2022 DiGRA ’22 – Proceedings of the 2022 DiGRA International Conference: Bringing Worlds Together

Role-playing games (RPGs) have a considerable amount of text in video game dialogues. Quite often this text is semi-annotated by the game developers. In this paper, we extract a multilingual dataset of persuasive dialogue from several RPGs. We show the viability of this data in building a persuasion detection system using a natural language processing (NLP) model called BERT. We believe that video games have a lot of unused potential as a datasource for a variety of NLP tasks. The code and data described in this paper are available on Zenodo.

 

GameNet and GameSage: Videogame Discovery as Design Insight

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Ryan James Kaltman Eric Hong Timothy Isbister Katherine Mateas Michael Wardrip-Fruin Noah
2016 DiGRA/FDG '16 - Proceedings of the First International Joint Conference of DiGRA and FDG

The immense proliferation of videogames over the course of recent decades has yielded a discoverability problem that has largely been unaddressed. Though this problem affects all videogame stakeholders, we limit our concerns herein to the particular context of game designers seeking prior work that could inform their own ideas or works in progress. Specifically, we present a tool suite that solicits text about a user’s idea for a game to generate an explorable listing of the existing games most related to that abstract idea. From a study in which 182 game-design students used these tools to find games related to their own, we observe a demonstrated utility exceeding that of the current state of the art, which is the coordinated usage of assorted web resources. More broadly, this paper provides the first articulation of videogame discovery as an emerging application area.