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.

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Live streaming is becoming prevalent and its rapid rise also makes it an attractive scientific research subject. Despite recent research focuses on understanding the motivations and behavior of people engaging live streaming, we know little about how the adoption of live streaming strategy for e-commerce on product sales. In this paper, we establish a causal relationship between adopting live streaming strategy for e-commerce and online product sales. Our results indicate that there is a 21.8% increase in online sales volume after adopting live streaming strategy. Furthermore, we find live streaming strategy is more efficient for the sellers who mainly sell experience goods-they have 27.9% more than those whose products are mainly search goods. This work is the first quantitative study, to our knowledge, on how the adoption of live streaming strategy on online product sales.

Twitter offers tremendous opportunities for people to engage with real-world events (e.g., political