Speed limit enforcement as perceived by offenders: Implications for roads policing

Copyright@ 2010 The Authors. This is the post-print version of the article. The final published version may be accessed at the link below.Getting caught for speeding is an emotive issue. This paper analyzes an unexpected source of data captured by unprompted comments left at the end of a questionnaire by a sample of British drivers who all had penalty points on their licences, many for speeding.The paper’s relevance to roads policing is that perceived fairness of police procedures is crucial in shaping public support, and comments made by this sample of offending drivers indicated that speed limit enforcement through the operation of the speed camera system was often seen as unfair. Since roads policing is closely linked with this and with many drivers having penalty points on their licences, the views of such drivers could be instructive, given the continuing reliance on camera technology and the need for police to offer public reassurance. Finally, the implications for roads policing are considered.The data used in this paper are derived from a study funded by the Department for Transport (DfT)

Deep Learning for User Comment Moderation

Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation