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

Live streaming platforms such as Twitch and Periscope have become some of the most popular synchronous social networking services. To attract viewers, streamers are motivated to broadcast exciting video content while actively interacting with viewers. The emerging stream of research on the live streaming community has examined streamers’ motivations and how the viewers react to streamers. However, few studies have focused on understanding the characteristics of popular streamers. Popular streamers create tremendous business value for social media influencer investors, as they have high potential to create persuasive advertisements and endorsements for firms by promoting their products and services. We aim at examining the key characteristics associated with streamers’ viewer base, namely their personality, professionalism, and streaming affordance. Based on text mining and analyses of video content, our results show: (1) certain personality traits (such as openness) are negatively associated with both cumulative and current popularity, (2) professional players are more likely to attract a larger viewer base, and (3) social affordance, including profile building affordance, social connectivity, and social interactivity, is positively associated with both cumulative and current popularity. Our results provide useful insights into measuring and evaluating streamers’ popularity, which can, in turn, generate actionable strategies for social media influencer investors and platform operators.

It is important to provide adaptive data processing in wireless sensor networks in order to deal with various applications. In this paper,we propose a WIreless Sensor Networks Ontology (WISNO) for flexible modeling of sensor data. WISNO contains two-tier ontologies, a front-end for coarsegrained analysis and a back-end for high-level fine-grained data processing. We also describes the WISNO reasoning rules that adopts description logic and SWRL for managing data automatically.

People are increasingly turning to social media for help. According to a recent report by Twitter, over 5.5M customer service-related tweets are generated per month. In this work, we aim to explore firms' strategy when engaging customers regarding their concerns and complaints on Twitter. Specifically, we focus on how politeness, a linguistic factor indicating how a customer is questioning or complaining rather than the content of a query, affects firms' customer service engagement strategy. We develop a novel text mining methodology to mine politeness from tweets. Using this approach, our estimation results show several interesting results, including that firms are more likely to respond to more polite customers, and that this effect is augmented for customers with high social status. However, firms are more likely to engage impolite customers with a high social status in a private channel such as through direct messaging.