Henriette Cramer

Representation and communication: challenges in interpreting large social media datasets

Online services provide a range of opportunities for understanding human behaviour through the large aggregate data sets that their operation collects. Yet the data sets they collect do not unproblematically model or mirror the world events. In this paper we use data from Foursquare, a popular location check-in service, to argue for the importance of analysing social media as a communicative rather than representational system. Drawing on logs of all Foursquare check-ins over eight weeks we highlight four features of Foursquares use: the relationship between attendance and check-ins, event check-ins, commercial incentives to check-in, and lastly humorous check-ins These points show how large data analysis is affected by the end user uses to which social networks are put.

Hospital robot at work: something alien or an intelligent colleague?

This paper describes a case study of the initial reactions to a transport robot running in a semi-public hospital environment. The robot was transporting goods and samples for an orthopedic department, moving within and between different departments for 13 days, and was used by the staff for five days. Based on this case, we discuss how the robot was perceived by staff and visitors and purpose an initial framework, a utopian model, describing four different perspectives; an alien, a machine, a worker and as a work partner. This has been derived from interviews, questionnaires and observation, and exemplifies different qualities that the robot was ascribed. We discuss how the perspectives may mutually co-exist and change, and are affected by time and familiarity with the robot at work.

Research in the large. using app stores, markets, and other wide distribution channels in Ubicomp research

The mobile phones that people use in their daily lives now run advanced applications and come equipped with sensors once only available in custom hardware in UbiComp research. At the same time application distribution has become increasingly simple due to the proliferation of app stores and the like. Evaluation and research methods have to be adapted to this new context to get the best data and feedback from wide audiences. However, an overview of successful strategies to overcome research challenges inherent to wide deployment is not yet available. App store platform characteristics, devices, reaching target users, new types of evaluation data and dynamic, heterogeneous usage contexts have to be dealt with. This workshop provides a forum for researchers and developers to exchange experiences and strategies for wide distribution of applications. We aim at building an understanding of the opportunities of various distribution channels and obstacles involved in a research context.

Mobile exploration of geotagged photographs

Columbus is a mobile application that lets users explore their surroundings through geotagged photographs, presented to them at the location they were taken. By moving around the physical world, the user unlocks photographs and gets to see and experience them in unison with their location. During two consecutive field trials, we investigated how the application was used and experienced and how photographs and locations are explored together. We found that previous experience with the surroundings people was exploring affected how they experienced the localized content. We report on the system’s design and implementation, the trials as well as resulting insights that can be used by other developers of locative media applications.

Drawing the city: differing perceptions of the urban environment

In building location-based services, it is important to present information in ways that fit with how individuals view and navigate the city. We conducted an adaptation of the 1970s Mental Maps study by Stanley Milgram in order to better understand differences in peoples views of the city based on their backgrounds and technology use. We correlated data from a demographic questionnaire with the map data from our participants to perform a first-of-its-kind statistical analysis on differences in hand-drawn city maps. We describe our study, findings, and design implications for location-based services.

ViPleo and PhyPleo: artificial pet with two embodiments

In our current work we have designed and implemented an artificial pet with two embodiments. In both embodiments behavior is driven by needs that are used to maintain coherence and motivate user interaction. These needs are transferred between embodiments, with only one embodiment being active at a time. We performed an evaluation with 10-year old children participants. The retrieved data indicated that many children understood the concept of an artificial pet with two bodies, even without being given clues. Nevertheless, children did perceive differences between the two embodiments, which contributed for many stating that they interacted with two pets. Among other aspects, the physical version was perceived as less obedient due to problems concerning action recognition. Although caused by technical issues, this result raises the question if virtual embodiments should simulate action recognition problems that their physical counterparts have.

Revive!: reactions to migration between different embodiments when playing with robotic pets

This paper explores the issues that arise in the context of the migration of a robotic pet between different embodiments and the associated design challenges. In the following, we describe the perceptions that a group of children have of a dinosaur character crossing the boundary between its robotic embodiment (the Pleo commercial pet), and its virtual counterpart on a mobile phone. We analyse the childrens perceptions of, as well as emotional reactions to, the migration of this character, and show how seemingly subtle variations in the migration process can affect the childrens perception on the character and its embodiments. Among other findings, gaps in the migration process, or perceived unresponsiveness, appeared to be accompanied by anxiety in the participating children. Based on our results, we point to yet unsolved design challenges for migration in interactions with embodied characters, and offer insights for migration implementation.

Beyond the bar: the places where location-based services are used in the city

Abstract Mobile services are integrating into the places and routines of daily life. But which types of places afford the use of various services, and how important are these places in our lives? Through several studies, we have explored the types of places that are most important to people in their cities, and compare these to the place types where different location-based services are used. We find that services were used quite consistently between cities, but that between services places of personal salience, such as parks, are less common in the use of today’s check-in services compared with location-based storytelling systems. Supported with data from the StoryPlace.me service, we suggest that focusing on selective sharing and storytelling can facilitate use at these more personally meaningful places.

Describing and Understanding Neighborhood Characteristics through Online Social Media

Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy. We apply the GHM to a dataset of 8 million Flickr photos in order to discriminate between content (i.e. tags) that specifically characterizes a region (e.g. neighborhood) and content that characterizes surrounding areas or more general themes. Knowledge of the discriminative and non-discriminative terms used throughout the hierarchy enables us to quantify the uniqueness of a given region and to compare similar but distant regions. Our evaluation demonstrates that our model improves upon traditional Naive Bayes classification by 47% and hierarchical TF-IDF by 27%. We further highlight the differences and commonalities with human reasoning about what is locally characteristic for a neighborhood, distilled from ten interviews and a survey that covered themes such as time, events, and prior regional knowledge.

People's Perceptions of Personalized Ads

Advertising is key to the business model of many online services. Personalization aims to make ads more relevant for users and more effective for advertisers. However, relatively few studies into user attitudes towards personalized ads are available. We present a San Francisco Bay Area survey (N=296) and in-depth interviews (N=24) with teens and adults. People are divided and often either (strongly) agreed or disagreed about utility or invasiveness of personalized ads and associated data collection. Mobile ads were reported to be less relevant than those on desktop. Participants explained ad personalization based on their personal previous behaviors and guesses about demographic targeting. We describe both metrics improvements as well as opportunities for improving online advertising by focusing on positive ad interactions reported by our participants, such as personalization focused not just on product categories but specific brands and styles, awareness of life events, and situations in which ads were useful or even inspirational.