Results identify behavioral correlates and potential risks of mind wandering that might enable efforts to detect and mitigate driver inattention.

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We propose the use of Twitter analysis as an alternative source of data to document weekly trends in emotion and stress, and attempt to use the method to estimate the work recovery effect of weekends. On the basis of 2,102,176,189 Tweets, we apply Pennebakers linguistic inquiry word count (LIWC) approach to measure daily Tweet content across 18 months, aggregated to the US national level of analysis. We derived a word count dictionary to assess work stress and applied p-technique factor analysis to the daily word count data from 19 substantively different content areas covered by the LIWC dictionaries. Dynamic factor analysis revealed two latent factors in day-level variation of Tweet content. These two factors are: (a) a negative emotion/stress/somatic factor, and (b) a positive emotion/food/friends/home/family/leisure factor, onto which elements of work, money, achievement, and health issues have strong negative loadings. The weekly trend analysis revealed a clear "Friday dip" for work stress and negative emotion expressed on Twitter. In contrast, positive emotion Tweets showed a "mid-week dip" for Tuesday-Wednesday-Thursday and "weekend peak" for Friday through Sunday, whereas work/money/achievement/health problem Tweets showed a small "weekend dip" on Fridays through Sundays. Results partially support the Effort-Recovery theory. Implications and limitations of the method are discussed.

We present a semantic imitation model of social tagging and exploratory search based on theories of cognitive science. The model assumes that social tags evoke a spontaneous tag-based topic inference process that primes the semantic interpretation of resource contents during exploratory search, and the semantic priming of existing tags in turn influences future tag choices. The model predicts that (1) users who can see tags created by others tend to create tags that are semantically similar to these existing tags, demonstrating the social influence of tag choices; and (2) users who have similar information goals tend to create tags that are semantically similar, but this effect is mediated by the semantic representation and interpretation of social tags. Results from the experiment comparing tagging behavior between a social group (where participants can see tags created by others) and a nominal group (where participants cannot see tags created by others) confirmed these predictions. The current results highlight the critical role of human semantic representations and interpretation processes in the analysis of large-scale social information systems. The model implies that analysis at both the individual and social levels are important for understanding the active, dynamic processes between human knowledge structures and external folksonomies. Implications on how social tagging systems can facilitate exploratory search, interactive information retrievals, knowledge exchange, and other higher-level cognitive and learning activities are discussed.

Wearable devices, such as smartwatches, are furnished with state-of-the-art sensors that enable a range of context-aware applications. However, malicious applications can misuse these sensors, if access is left unaudited. In this paper, we demonstrate how applications that have access to motion or inertial sensor data on a modern smartwatch can recover text typed on an external QWERTY keyboard. Due to the distinct nature of the perceptible motion sensor data, earlier research efforts on emanation based keystroke inference attacks are not readily applicable in this scenario. The proposed novel attack framework characterizes wrist movements (captured by the inertial sensors of the smartwatch worn on the wrist) observed during typing, based on the relative physical position of keys and the direction of transition between pairs of keys. Eavesdropped keystroke characteristics are then matched to candidate words in a dictionary. Multiple evaluations show that our keystroke inference framework has an alarmingly high classification accuracy and word recovery rate. With the information recovered from the wrist movements perceptible by a smartwatch, we exemplify the risks associated with unaudited access to seemingly innocuous sensors (e.g., accelerometers and gyroscopes) of wearable devices. As part of our efforts towards preventing such side-channel attacks, we also develop and evaluate a novel context-aware protection framework which can be used to automatically disable (or downgrade) access to motion sensors, whenever typing activity is detected.