A person seeking someone else's attention is normally able to quickly assess how interruptible they are. This assessment allows for behavior we perceive as natural, socially appropriate, or simply polite. On the other hand, today's computer systems are almost entirely oblivious to the human world they operate in, and typically have no way to take into account the interruptibility of the user. This paper presents a Wizard of Oz study exploring whether, and how, robust sensor-based predictions of interruptibility might be constructed, which sensors might be most useful to such predictions, and how simple such sensors might be. The study simulates a range of possible sensors through human coding of audio and video recordings. Experience sampling is used to simultaneously collect randomly distributed self-reports of interruptibility. Based on these simulated sensors, we construct statistical models predicting human interruptibility and compare their predictions with the collected self-report data. The results of these models, although covering a demographically limited sample, are very promising, with the overall accuracy of several models reaching about 78%. Additionally, a model tuned to avoiding unwanted interruptions does so for 90% of its predictions, while retaining 75% overall accuracy.

Advances in location-enhanced technology are making it easier for us to be located by others. These new technologies present a difficult privacy tradeoff, as disclosing one’s location to another person or service could be risky, yet valuable. To explore whether and what users are willing to disclose about their location to social relations, we conducted a three-phased formative study. Our results show that the most important factors were who was requesting, why the requester wanted the participant’s location, and what level of detail would be most useful to the requester. After determining these, participants were typically willing to disclose either the most useful detail or nothing about their location. From our findings, we reflect on the decision process for location disclosure. With these results, we hope to influence the design of future locationenhanced applications and services. ACM Classification Keywords

Current systems often create socially awkward interruptions or unduly demand attention because they have no way of knowing if a person is busy and should not be interrupted. Previous work has examined the feasibility of using sensors and statistical models to estimate human interruptibility in an office environment, but left open some questions about the robustness of such an approach. This paper examines several dimensions of robustness in sensor-based statistical models of human interruptibility. We show that real sensors can be constructed with sufficient accuracy to drive the predictive models. We also create statistical models for a much broader group of people than was studied in prior work. Finally, we examine the effects of training data quantity on the accuracy of these models and consider tradeoffs associated with different combinations of sensors. As a whole, our analyses demonstrate that sensor-based statistical models of human interruptibility can provide robust estimates for a variety of office workers in a range of circumstances, and can do so with accuracy as good as or better than people. Integrating these models into systems could support a variety of advances in human computer interaction and computer-mediated communication. Author Keywords Situationally appropriate interaction, managing human attention, sensor-based interfaces, context-aware computing, machine learning.

We report an empirical study of a synchronous messaging application with group-oriented functionality designed to support teams in the workplace. In particular, the tool supports group chat windows that allow members of a group to communicate with text that persists for about a day. We describe the experience of 6 globally-distributed work groups who used the tool over a period of 17 months. An analysis of use shows that the group functionality was used primarily for bursts of synchronous conversations and occasional asynchronous exchanges. The content was primarily focused on work tasks, and negotiating availability, with a smattering of non-work topics and humor. Nearly all groups were remarkably similar in the content of their group chat, although the research group chatted far more frequently than the others. We conclude with suggestions for future research, and a discussion of the place of team-oriented synchronous messaging tools in the workplace.

Although electronic communication plays an important role in the modern workplace, the interruptions created by poorly-timed attempts to communicate are disruptive. Prior work suggests that sharing an indication that a person is currently busy might help to prevent such interruptions, because people could wait for a person to become available before attempting to initiate communication. We present a context-aware communication client that uses the built-in microphones of laptop computers to sense nearby speech. Combining this speech detection sensor data with location, computer, and calendar information, our system models availability for communication, a concept that is distinct from the notion of presence found in widely-used systems. In a four week study of the system with 26 people, we examined the use of this additional context. To our knowledge, this is the first field study to quantitatively examine how people use automatically sensed context and availability information to make decisions about when and how to communicate with colleagues. Participants appear to have used the provided context to as an indication of presence, rather than considering availability. Our results raise the interesting question of whether sharing an indication that a person is currently unavailable will actually reduce inappropriate interruptions.

People use their awareness of others ' temporal patterns to plan work activities and communication. This paper presents algorithms for programatically detecting and modeling temporal patterns from a record of online presence data. We describe analytic and end-user visualizations of rhythmic patterns and the tradeoffs between them. We conducted a design study that explored the accuracy of the derived rhythm models compared to user perceptions, user preference among the visualization alternatives, and users' privacy preferences. We also present a prototype application based on the rhythm model that detects when a person is “away ” for an extended period and predicts their return. We discuss the implications of this technology on the design of computer-mediated communication.

Abstract. A study of email responsiveness was conducted to understand how the timing of email responses conveys important information. Interviews and observations explored users ’ perceptions of how they responded to email and formed expectations of others’ responses to them. We identified ways in which users maintain and cultivate a responsiveness image for projecting expectations about their email response. We also discuss other contextual cues people use to discover email responsiveness, which include using other tools such as the calendar and phone, accounting for the amount of work time overlap available, and establishing a pacing between email correspondents. These cues help users develop a sense of when to expect a response and when breakdown has occurred, requiring further action. Anyone who uses email regularly has sent a message and wondered, “When will I get a response to this email? ” Or, “How long should I wait for a response to this message before taking further action? ” Beyond the content of email messages, the timing of when email is sent, when it is read, and when a response is received are all examples of rhythms of email activity that help users coordinate their

WatchMe is a personal communicator with context awareness in a wristwatch form; it is meant to keep intimate friends and family always connected via awareness cues and text, voice instant message, or synchronous voice connectivity. Sensors worn with the watch track location (via GPS), acceleration, and speech activity; this is classified and conveyed to the other party, where it appears in iconic form on the watch face. When a remote person with whom this information is shared examines it, their face appears on the watch of the person being checked on. The working prototype was used as the focus of interviews to gauge the desirability of such a device.

Draft version. This paper has been submitted to HICSS-37, Persistent Conversation Track and is awaiting review. Anyone wishing to reference this paper should first contact Fernanda and danah.

The computer and communication systems that office workers currently use tend to interrupt at inappropriate times or unduly demand attention because they have no way to determine when an interruption is appropriate. Sensor-based statistical models of human interruptibility offer a potential solution to this problem. Prior work to examine such models has primarily reported results related to social engagement, but it seems that task engagement is also important. Using an approach developed in our prior work on sensor-based statistical models of human interruptibility, we examine task engagement by studying programmers working on a realistic programming task. After examining many potential sensors, we implement a system to log low-level input events in a development environment. We then automatically extract features from these low-level event logs and build a statistical model of interruptibility. By correctly identifying situations in which programmers are non-interruptible and minimizing cases where the model incorrectly estimates that a programmer is non-interruptible, we can support a reduction in costly interruptions while still allowing systems to convey notifications in a timely manner. Author Keywords Situationally appropriate interaction, managing human attention, sensor-based interfaces, context-aware computing, machine learning, interruptibility.