What We Instagram: A First Analysis of Instagram Photo Content and User Types

Abstract

Instagram is a relatively new form of communication where users can easily share their updates by taking photos and tweaking them using filters. It has seen rapid growth in the number of users as well as uploads since it was launched in October 2010. In spite of the fact that it is the most popular photo capturing and sharing application, it has attracted relatively less attention from the research community. In this paper, we present both qualitative and quantitative analysis on Instagram. We use computer vision techniques to examine the photo content. Based on that, we identify the different types of active users on Instagram using clustering. Our results reveal several insights about Instagram which were never studied before, that include: 1) Eight popular photos categories, 2) Five distinct types of Instagram users in terms of their posted photos, and 3) A user's audience (number of followers) is independent of his/her shared photos on Instagram. To our knowledge, this is the first in-depth study of content and users on Instagram.

Factors Building Consumer Trust in Instagram Stores and the Influence of Trust in Instagram Stores on Purchasing Intention

“…Initially a photo-sharing platform, Instagram was first made available for iOS operated mobile devices and was integrated into Android cell phones. The considerable user interest it attracted prompted Facebook to buy it (Hu, Manikonda, & Kambhampati, 2014;Çetinkaya & Özdemir, 2014;Deniz, Derici & Kılıç, 2018). Basically, Instagram is a social network and usercreated content (UCC) application whereby users create a profile, form a network by following others, share photos and videos, and interact with other users through likes, comments, or direct messages.…”

Education to Improve Public Health: An Application that Inspires the Design of Creative and Meaningful Tangible Play for Children

“…Social media allows people to browse abundant information without time and location constraints. According to a study in 2014, an average of 55 million photos were uploaded by users on Instagram per day, and more than 16 billion photos were shared till 2013 (Hu et al, 2014). As mentioned in interviews and surveys, Youtube, Instagram, and Xiaohongshu (the Chinese version of Instagram) are frequently used by educators and designers as sources of inspiration, learning tools, and information searching tools.…”

Tweeting the Mind and Instagramming the Heart: Exploring Differentiated Content Sharing on Social Media

“…Using the OpenCV library (http://opencv.org/) on these two datasets, we extracted Speeded Up Robust Features (SURF) for each image. We used the vector quantization approach Visual categories on Instagram agree with our previous work (Hu, Manikonda, and Kambhampati 2014) which detected eight different categories of images. We tried to categorize the Twitter images into the same format as Instagram images.…”

“…It is established that Twitter is primarily a news medium (Kwak et al 2010). Research on Instagram has focused mostly on understanding the user behavior through analyzing color palettes (Hochman and Schwartz 2012), categories (Hu, Manikonda, and Kambhampati 2014), filters (Bakhshi et al 2015), etc. On the other hand, it has been of significant interest to the researchers to investigate the behavior of a user (Benevenuto et al 2009), mapping same user accounts (Zafarani and Liu 2013), study how users reveal their personal information (Chen et al 2012), etc all across multiple OSNs.…”