Proliferation of GPS-enabled mobile devices has brought a plurality of location-aware applications leveraging the location characteristics in the shared content, like photos and check-ins. While these applications provide contextual and relevant information, they also assume geo-tagged contents to be representative of the geo-bounded characteristics of location. In this paper, however, we show that the characteristics geo-tagged contents capture about a location can vary based on the familiarity of user (sharing the content) with the location. Using a large dataset of geo-tagged photos, we learn descriptive spatial photo characteristics and user temporal-location-familiarity to highlight unique characteristics photos capture of location, which vary significantly if taken by locals versus tourists. We then propose a ranking-approach to find most representative photos for a given city. A user-based evaluation shows photos are more diverse and characteristic of location compared to other popular baselines while being representative of how locals and tourists would describe the city.

Recommendation systems rely on various definitions of similarities. These definitions while having numerous design factors in different domains help identify and recommend relevant content. For example, similarity between users, or items, are measured based on, but not limited to, explicit feedback such as ratings, thumbs up; or/and implicit feedback such as clicks, views etc; or/and based on composition of item such as tags, metadata etc. In this paper, we explore a similarity model while very intuitive to find similar items using a very common natural law of attraction between bodies, that is gravitational law. We show how the two attributes, relative mass and distance between the bodies, of gravitation law can be interpreted for an effective personalized recommendations; in both spatial and non-spatial domains. Finally, we illustrate the use of distance and mass in a non-spatial domain and we exhibit the accuracy in recommendations against popular baselines.

People taking photographs with their tablets is an increasingly common sight. While the design of hardware is larger and more cumbersome than the smaller and more often carried cameraphones, the underlying photographic software is essentially the same. At the same time, cameraphone hardware usually contains objectively better quality cameras with more megapixels, better lenses, and faster image processing. So why do people choose one over the other? In this paper we explore the experience of tablet photography and aim to compare it with camera phone photography. In a mixed-methods study, we use quantitative and qualitative data to explore perceptions of photo quality, factors influencing photo quality, and the affordances of tablet computers that lead users to prefer them over camera phones. Our results indicate that participants take photos with their camera phone for the convenience of the small device form factor, but prefer taking photos with their larger tablet for photos that require higher definition. We discuss the design implications of this research for the design of mobile cameras.

Animated GIFs have been around since 1987 and recently gained more popularity on social networking sites. Tumblr, a large social networking and micro blogging platform, is a popular venue to share animated GIFs. Tumblr users follow blogs, generating a feed or posts, and choose to "like' or to "reblog' favored posts. In this paper, we use these actions as signals to analyze the engagement of over 3.9 million posts, and conclude that animated GIFs are significantly more engaging than other kinds of media. We follow this finding with deeper visual analysis of nearly 100k animated GIFs and pair our results with interviews with 13 Tumblr users to find out what makes animated GIFs engaging. We found that the animation, lack of sound, immediacy of consumption, low bandwidth and minimal time demands, the storytelling capabilities and utility for expressing emotions were significant factors in making GIFs the most engaging content on Tumblr. We also found that engaging GIFs contained faces and had higher motion energy, uniformity, resolution and frame rate. Our findings connect to media theories and have implications in design of effective content dashboards, video summarization tools and ranking algorithms to enhance engagement.

Increasingly online reviews are relied upon to make choices about the purchases and services we use daily. Businesses, on the other hand, depend on online review sites to find new customers and understand people's perception of them. In order for an online review community to be effective to both users and businesses, it is important to understand what constitutes a high quality review as perceived by people, and how to maximize quality of reviews in the community. In this paper, we study Yelp to answer these questions. We analyze about 230,000 reviews and member interaction ("votes") with these reviews. We find that active and regular members are the highest contributors to good quality reviews and longer reviews have higher chances of being popular in the community. We find that reviews voted "useful" tend to be the early ones for a specific business. Our findings have implications on enabling high quality member contributions and community effectiveness. We discuss the implications to design of social systems with diverse feedback signals.

Photos are becoming prominent means of communication online. Despite photos' pervasive presence in social media and online world, we know little about how people interact and engage with their content. Understanding how photo content might signify engagement, can impact both science and design, influencing production and distribution. One common type of photo content that is shared on social media, is the photos of people. From studies of offline behavior, we know that human faces are powerful channels of non-verbal communication. In this paper, we study this behavioral phenomena online. We ask how presence of a face, it's age and gender might impact social engagement on the photo. We use a corpus of 1 million Instagram images and organize our study around two social engagement feedback factors, likes and comments. Our results show that photos with faces are 38% more likely to receive likes and 32% more likely to receive comments, even after controlling for social network reach and activity. We find, however, that the number of faces, their age and gender do not have an effect. This work presents the first results on how photos with human faces relate to engagement on large scale image sharing communities. In addition to contributing to the research around online user behavior, our findings offer a new line of future work using visual analysis.

Online recommendation communities, like Yelp, are valuable information sources for people. Yet, we assert, review communities have their own dynamics behind the social interactions therein. In this work, we study the Yelp review votes of useful, funny, and/or cool to understand these social perceptions of the review. We examine the relationship between these social signals and the emotional valence of the review itself (text and rating). We aim to understand the community's perception of each of these signaling contributions. We construct a conditional inference tree of social signals from 230K Yelp reviews to study how social signals shape the deviance in review rating from the mean rating, an indicator of the overall business rating on Yelp. We find two effects of social signals. First, reviews voted as useful and funny are associated with lower user ratings and relatively negative tone in the review text. Second, reviews voted as cool tend to have a relatively positive tone and higher ratings. Our findings open a research direction for further understanding of perceptions of social signals and have implications for design of recommendation systems.

Online recommendation sites are valuable information sources that people contribute to, and often use to choose restaurants. However, little is known about the dynamics behind participation in these online communities and how the recommendations in these communities are formed. In this work, we take a first look at online restaurant recommendation communities to study what endogenous (i.e., related to entities being reviewed) and exogenous factors influence people's participation in the communities, and to what extent. We analyze an online community corpus of 840K restaurants and their 1.1M associated reviews from 2002 to 2011, spread across every U.S. state. We construct models for number of reviews and ratings by community members, based on several dimensions of endogenous and exogenous factors. We find that while endogenous factors such as restaurant attributes (e.g., meal, price, service) affect recommendations, surprisingly, exogenous factors such as demographics (e.g., neighborhood diversity, education) and weather (e.g., temperature, rain, snow, season) also exert a significant effect on reviews. We find that many of the effects in online communities can be explained using offline theories from experimental psychology. Our study is the first to look at exogenous factors and how it related to online online restaurant reviews. It has implications for designing online recommendation sites, and in general, social media and online communities.

Over the past decade, social network sites have become ubiquitous places for people to maintain relationships, as well as loci of intense research interest. Recently, a new site has exploded into prominence: Pinterest became the fastest social network to reach 10M users, growing 4000% in 2011 alone. While many Pinterest articles have appeared in the popular press, there has been little scholarly work so far. In this paper, we use a quantitative approach to study three research questions about the site. What drives activity on Pinterest? What role does gender play in the site's social connections? And finally, what distinguishes Pinterest from existing networks, in particular Twitter? In short, we find that being female means more repins, but fewer followers, and that four verbs set Pinterest apart from Twitter: use, look, want and need. This work serves as an early snapshot of Pinterest that later work can leverage.