We present a concept and tool for studying language use in everyday mobile text communication (e.g. chats). Our approach for the first time enables researchers to collect comprehensive data on language use during unconstrained natural typing (i.e. no study tasks) without logging readable messages to preserve privacy. We achieve this with a combination of three customisable text abstraction methods that run directly on participants' phones. We report on our implementation as an Android keyboard app and two evaluations: First, we simulate text reconstruction attempts on a large text corpus to inform conditions for minimising privacy risks. Second, we assess people's experiences in a two-week field deployment (N=20). We release our app as an open source project to the community to facilitate research on open questions in HCI, Linguistics and Psychology. We conclude with concrete ideas for future studies in these areas.

Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people’s thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this development, the present study demonstrates how smartphones can help establish associations between the psychological perception and the physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective situation cues obtained via smartphone sensing. To account for the complexity of real-world situations, we extracted a total of 1,356 granular situation cues from different sensing modalities. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight DIAMONDS, finding significant out-of-sample predictions for the five dimensions capturing the situations’ Duty, Intellect, Mating, pOsitivity, and Sociality. Analyses of (grouped) feature importance revealed that these predictions relied on complex constellations of cues representing various situational information about the Persons/Interactions and Objects present, the Events/Activities happening, and the current Location and Time. Furthermore, a nomological network analysis provided evidence for the construct validity of our cue-based DIAMONDS predictions. We conclude by discussing how smartphone-based situational snapshots, in general, and our prediction models, in particular, advance psychological research on situations.

Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people’s thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this opportunity, the present study demonstrates how smartphones can help establish associations between the psychological perception and physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective cues obtained via smartphone sensing. More precisely, we extracted a total of 1,356 granular cues from different sensing modalities to account for the complexity of real-world situations. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, Sociality (DIAMONDS), finding significant out-of-sample predictions for the five dimensions reflecting the situations’ Duty, Intellect, Mating, pOsitivity, and Sociality. In a series of follow-up analyses, we further explored the data patterns captured by our models, revealing, for example, that those cues related to time and location were particularly informative of the respective situation characteristics. We conclude by interpreting the mapping between cues and characteristics in real-world situations and discussing how smartphone-based situational snapshots may push the boundaries of psychological research on situations.

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Previous studies have shown that when individuals join groups for lunch, they tend to conform to the decision of the group. As result, people do not always have the chance to pick the food they wish for, which in turn may have negative consequences, such as not abiding to healthy diets. To address this problem, we created Lunchocracy, an anonymous decision support tool for lunch spots in a workplace based on feedback from a focus group with 7 participants. The tool implements a conversational skype-bot, Lunchbot, that allows users to express interest in joining lunch and to vote for diners to eat at. We deployed the tool for four weeks with 14 participants from the same university department. Post-interviews with 5 participants revealed an overall satisfaction with Lunchocracy, in particular due to it structuring the lunch decision-making and saving time. We discuss how the use of Lunchocracy can positively influence the group's eating dynamics.