Utilizing linguistically enhanced keystroke dynamics to predict typist cognition and demographics

Entering information on a computer keyboard is a ubiquitous mode of expression and communication. We investigate whether typing behavior is connected to two factors: the cognitive demands of a given task and the demographic features of the typist. We utilize features based on keystroke dynamics, stylometry, and \"language production\", which are novel hybrid features that capture the dynamics of a typists linguistic choices. Our study takes advantage of a large data set (~350 subjects) made up of relatively short samples (~450 characters) of free text. Experiments show that these features can recognize the cognitive demands of task that an unseen typist is engaged in, and can classify his or her demographics with better than chance accuracy. We correctly distinguish High vs. Low cognitively demanding tasks with accuracy up to 72.39%. Detection of non-native speakers of English is achieved with F1=0.462 over a baseline of 0.166, while detection of female typists reaches F1=0.524 over a baseline of 0.442. Recognition of left-handed typists achieves F1=0.223 over a baseline of 0.100. Further analyses reveal that novel relationships exist between language production as manifested through typing behavior, and both cognitive and demographic factors. HighlightsRecognition of cognitive task with linguistic and keystroke features with accuracy of 72.39%.Recognition of gender, handedness, and native-language from short unconstrained text at F1=.462, 0.223, and 0.524, respectively.Developed novel Language Production features hybridizing keystroke dynamics and stylometry.