Heuristic usability evaluation on games: a modular approach

Heuristic evaluation is the preferred method to assess usability in games when experts conduct this evaluation. Many heuristics guidelines have been proposed attending to specificities of games but they only focus on specific subsets of games or platforms. In fact, to date the most used guideline to evaluate games usability is still Nielsen’s proposal, which is focused on generic software. As a result, most evaluations do not cover important aspects in games such as mobility, multiplayer interactions, enjoyability and playability, etc. To promote the usage of new heuristics adapted to different game and platform aspects we propose a modular approach based on the classification of existing game heuristics using metadata and a tool, MUSE (Meta-heUristics uSability Evaluation tool) for games, which allows a rebuild of heuristic guidelines based on metadata selection in order to obtain a customized list for every real evaluation case. The usage of these new rebuilt heuristic guidelines allows an explicit attendance to a wide range of usability aspects in games and a better detection of usability issues. We preliminarily evaluate MUSE with an analysis of two different games, using both the Nielsen’s heuristics and the customized heuristic lists generated by our tool.Unión Europea PI055-15/E0

Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping

We propose Quootstrap, a method for extracting quotations, as well as the names of the speakers who uttered them, from large news corpora. Whereas prior work has addressed this problem primarily with supervised machine learning, our approach follows a fully unsupervised bootstrapping paradigm. It leverages the redundancy present in large news corpora, more precisely, the fact that the same quotation often appears across multiple news articles in slightly different contexts. Starting from a few seed patterns, such as ["Q", said S.], our method extracts a set of quotation-speaker pairs (Q, S), which are in turn used for discovering new patterns expressing the same quotations; the process is then repeated with the larger pattern set. Our algorithm is highly scalable, which we demonstrate by running it on the large ICWSM 2011 Spinn3r corpus. Validating our results against a crowdsourced ground truth, we obtain 90% precision at 40% recall using a single seed pattern, with significantly higher recall values for more frequently reported (and thus likely more interesting) quotations. Finally, we showcase the usefulness of our algorithm's output for computational social science by analyzing the sentiment expressed in our extracted quotations.Comment: Accepted at the 12th International Conference on Web and Social Media (ICWSM), 201