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registered users since its launch in October 2010. It offers its users a unique way to post pictures and videos using their smartphones, apply different manipulation tools – 16 filters – in order to transform the appearance of an image, and share them instantly on multiple platforms (e.g., Twitter) in addi- tion to the user’s Instagram page. It also allows users to add captions, hashtags using the # symbol to describe the pic- tures and videos, and tag or mention other users by using the @ symbol (which effectively creates a link from their posts to the referenced user’s account) before posting them. (a) (b) Figure 1: Interfaces of Instagram. (a) Instagram app home- page, (b) Transforming a photo using filters In addition to its photo capturing and manipulation func- tions, Instagram also provides similar social connectivity as Twitter that allows a user to follow any number of other users, called “friends”. On the other hand, the users follow- ing a Instagram user are called “followers”. Instagram’s so- cial network is asymmetric, meaning that if a user A follows B, B need not follow A back. Besides, users can set their privacy preferences such that their posted photos and videos are available only to the user’s followers that requires ap- proval from the user to be his/her follower. By default, their images and videos are public which means they are visible to anyone using Instagram app or Instagram website. Users consume photos and videos mostly by viewing a core page showing a “stream” of the latest photos and videos from all their friends, listed in reverse chronological order. They can also favorite or comment on these posts. Such actions will appear in referenced user’s “Updates” page so that users can keep track of “likes” and comments about their posts. Given these functions, we regard Instagram as a kind of so- cial awareness stream (Naaman, Boase, and Lai 2010) like other social media platforms such as Facebook and Twitter. 3 Approach Our analysis based on the Instagram data collected using the Instagram API, is a qualitative categorization of Instagram photos; and a quantitative examination of users’ character- istics with respect to their photos. The data includes profile information, photos, captions and tags associated with pho- tos, and users’ social network that includes friends and fol- lowers. Below, we first provide details about the dataset we used, and later discuss how we develop a coding scheme for categorizing the photos and the coding process. 3.1 Data Collection Methodology To obtain a random sample of Instagram users and retrieve their public photos, we first got the IDs of users who had media (photos or videos) that appeared on Instagram’s pub- lic timeline, which displays a subset of Instagram media that was most popular at the moment. This process resulted in a set of 37 unique users. By careful examination of each user in this set, we found that these users were mostly celebrities (which may explain why their posts were popular). We then crawled the IDs of both their followers and friends, and later merged these two lists to form one unified list that contained 95,343 unique seed users. Next, we built a random sample of regular active Instagram users using this seed user list. Specifically, we operationalized the notion of regular ac- tive users as those who are 1) not organizations, brands, or spammers, and 2) had at least 30 friends, 30 followers, and had posted at least 60 photos.2 In practice, we found 13,951 users (14.6% of the seed users) who satisfied those crite- ria, out of which we randomly selected 50 users and down- loaded their profiles, 20 recent photos (note that we cannot randomly download photos due to the limitations of Insta- gram API), and their social network (lists of friends and fol- lowers). We chose to sample only 50 users here since we are performing manual coding of their photos which is not feasible over large number of users. This dataset allows us to make predictions with a 95% confidence level and a 13% confidence interval for typical users, accurate enough for the analysis in this paper (i.e., the sample is representative). 3.2 Content Categories and Coding Process To characterize the types of photos posted on Instagram we used a grounded approach to thematize and code (i.e., cat- egorize) a sample of 200 photos from 1,000 photos we ob- tained (50 users by 20 photo per user). Coming up with good meaningful content categories is known to be challenging, especially for images since they contain much richer fea- tures than text. Therefore, as an initial pass, we sought help from computer vision techniques to get an overview of what categories exist in an efficient manner. Specifically, we first used the classical Scale Invariant Feature Transform (SIFT) algorithm (Lowe 1999) to detect and extract local discrimi- native features from photos in the sample. The feature vec- tors for photos are of 128 dimensions. Following the stan- dard image vector quantization approach (i.e., SIFT feature clustering (Szeliski 2011)), we obtained the codebook vec- tors for each photo 3. Finally, we used k-means clustering to obtain 15 clusters of photos where the similarity between two photos are calculated in terms of Euclidean distance be- tween their codebook vectors. These clusters served as an initial set of our coding categories, where each photo be- longs to only one category. 2It is worth noting that during our crawling process, many users (about 9.4%) changed their privacy settings from public to private which made their profiles and photos unretrievable. 3A photo I of a dog can have 125 SIFT features corresponding to the dog’s eyes, legs, ears and so on, which are expressed in terms of the codebook vector (of size n) as I =< C1 : f1,C2 : f2,C3 : f3, ..., Cn : fn >, where 0≤i≤n fi = 125 and Ci is the cluster of all the features about specific characteristic of an object in the image.PDF Image | Instagram: Analysis of Instagram Photo Content and User Types
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