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Transforming Qualitative Data: Description, Analysis, and Interpretation

Transforming qualitative data: Description, analysis, and interpretation

Akaike information criterion statistics

Social media and loneliness

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.

/pdf/faces-engage-us-photos-with-faces-attract-more-likes-and-44r8cwhons.pdf

Faces engage us: photos with faces attract more likes and comments on Instagram

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.

http://comp.social.gatech.edu/papers/gilbert.chi13.pinterest.pdf

"I need to try this"?: a statistical overview of pinterest

A variety of simple graphical filters are available to camera phone users to enhance their photos on the fly; these filters often stylize, saturate or age a photo. In this paper, we present a combination of large-scale data analysis and small scale in-depth interviews to understand filter-work. We look at producers’ practices of photo filtering and gain insights in the roles filters play in engaging photo consumers’ by driving their social interactions. We first interviewed 15 Flickr mobile app users (photo producers) to understand their use and perception of filters. Next, we analyzed how filters affect a photo’s engagement (consumers’ perspective) using a corpus of 7.6 million Flickr photos. We find two groups of serious and casual photographers among filter users. The serious see filters as correction tools and prefer milder effects. Casual photographers, by contrast, use filters to significantly transform their photos with bolder effects. We also find that filtered photos are 21% more likely to be viewed and 45% more likely to be commented on by consumers of photographs. Specifically, filters that increase warmth, exposure and contrast boost engagement the most. Towards the ongoing research in social engagement and photo-work, these findings suggest several practical implications such as designing filters for both serious and casual photographers or designing methods to prioritize and rank content in order to maximize engagement.

https://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/download/10573/10484

Why We Filter Our Photos and How It Impacts Engagement

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.

Fast, Cheap, and Good: Why Animated GIFs Engage Us

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.

https://s.yimg.com/ge/labs/v2/uploads/www2014bakhshi.pdf

Saeideh Bakhshi

Papers

Faces engage us: photos with faces attract more likes and comments on Instagram

"I need to try this"?: a statistical overview of pinterest

Why We Filter Our Photos and How It Impacts Engagement

Fast, Cheap, and Good: Why Animated GIFs Engage Us

Demographics, weather and online reviews: a study of restaurant recommendations