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100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Friends Food Gadget Captioned photo Pet Activities Selfies Fashion Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Photo Categories Figure 3: Proportion of users w.r.t content categories. Bin1 contains 0-2 photos; Bin2 contains 3-5 photos; Bin3 con- tains 6-8 photos; Bin4 contains 9-11 photos; Bin5 contains ≥ 11 photos. Figure 4: Clustering users based on the categories of their photos. C1 to C5 represent five different user clusters. C1 (n=11, 22%), C2 (n=7, 14%), C3 (n=7, 14%), C4 (n=3, 6%), and C5 (n=22, 44%) an 8-dimensional vector for each user (since we have 8 cate- gories of photos), where each dimension represents the pro- portion of user’s photos in the corresponding category. Af- ter that, we utilize k-means clustering to generate clusters of users accordingly. We perform the clustering multiple times to determine the best k – the number of clusters, whose root mean square error is minimized. As shown in Fig. 4 shows the clustering results that distin- guish 5 types of users. Within each cluster, the histograms indicate the proportion of each of the 8 content categories. The users on Instagam clearly exhibit distinctive character- istics in terms of the photo they share. For example, there exists “selfies-lovers” (C4) who almost post self-portraits exclusively (C4’s entropy is H(x)=1.4). Similarly, people in C2 post mostly captioned photos whose embedded text mentions about quotes, mottos, poetries or even popular hashtags (C2’s entropy H(x)=1.6). On the other hand, there exist common users like C1 where even though they focus (slightly) more on posting photos of food, they like to post other categories of photos as well. Therefore, C1’s entropy is the highest (H(x)=1.96). Also, it is interesting to know that people in C5 (22 users in total) care about their friends as seriously as caring about themselves, by posting nearly equal number of photos from both categories (while ignor- ing the other categories) (C5’s entropy is H(x)=1.54). To answer RQ3, we examine if the type of users directly correlates with the users’ number of followers. In other words, do “selfies-lovers” (C4) attract significantly more fol- lowers than common users in C1? To this end, we perform a two-tailed t-test on the follower distributions from different user clusters. We find that all the other types of users agree with the null hypothesis that followers are independent of the user clusters (two-tailed t-test; p–value = 0.171). Since our analysis does not show any statistical significance over the “number of followers – types of users” correlations, we conclude that the size of a user’s audience (followers) is in- dependent of the type of the user (characterized in terms of the user’s shared photos on Instagram). 5 Conclusions and Future Work In this paper, we performed an analysis of photos and users on Instagram – the fastest growing social media application. To our knowledge, this is the first paper that conducts such analysis on Instagram data. In this paper we have shown how the image data was handled and analyzed to answer three fundamental research questions on Instagram. Our analysis shows that there are largely 8 different types of photo categories on Instagram. Based on the content posted by users, this analysis derives 5 different types of users (or user clusters). We also showed that there is no direct relationship between the number of followers and the type of users characterized in terms of her shared photos, through statistical significance tests. As a part of our future work, we want to extend this work by incorporating other features on Instagram such as user’s bio, hashtags, comments, and social network. We also plan to analyze sentiments and events associated with the photos and their associated text (Hu, Wang, and Kambhampati 2013). Acknowledgements This research is supported in part by the ONR grants N00014-13-1-0176, N0014-13-1-0519, ARO grant W911NF-13-1-0023 and a Google Research Grant. References Ellison, N. B., et al. 2007. Social network sites: Definition, history, and scholarship. JCMC. Hochman, N., and Manovich, L. 2013. Zooming into an instagram city: Reading the local through social media. First Monday. Hu, Y.; Wang, F.; and Kambhampati, S. 2013. Listening to the crowd: automated analysis of events via aggregated twitter senti- ment. In IJCAI. Instagram. 2013. Instagram statistics. {http://instagram. com/press/}. Lowe, D. G. 1999. Object recognition from local scale-invariant features. In CVPR. McCune, Z. 2011. Consumer production in social media networks : A case study of the instagram iphone app. Dissertation, University of Cambridge. Naaman, M.; Boase, J.; and Lai, C.-H. 2010. Is it really about me?: message content in social awareness streams. In CSCW. Rainie, L.; Brenner, J.; and Purcell, K. 2012. Photos and videos as social currency online. Pew Internet & American Life Project. Silva, T. H.; Melo, P. O.; Almeida, J. M.; Salles, J.; and Loureiro, A. A. 2013. A picture of instagram is worth more than a thousand words: Workload characterization and application. In DCOSS. IEEE. Szeliski, R. 2011. Computer vision: algorithms and applications. Springer. 0.6 0.5 0.4 0.3 0.2 0.1 0 Friends Food Gadget Captioned photo Pet Activity Selfies Fashion C1 C2 C3 C4 C5 Density of category w.r.t cluster Distribution of Bins w.r.t CategoryPDF Image | Instagram: Analysis of Instagram Photo Content and User Types
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