78,197 research outputs found
Online Popularity and Topical Interests through the Lens of Instagram
Online socio-technical systems can be studied as proxy of the real world to
investigate human behavior and social interactions at scale. Here we focus on
Instagram, a media-sharing online platform whose popularity has been rising up
to gathering hundred millions users. Instagram exhibits a mixture of features
including social structure, social tagging and media sharing. The network of
social interactions among users models various dynamics including
follower/followee relations and users' communication by means of
posts/comments. Users can upload and tag media such as photos and pictures, and
they can "like" and comment each piece of information on the platform. In this
work we investigate three major aspects on our Instagram dataset: (i) the
structural characteristics of its network of heterogeneous interactions, to
unveil the emergence of self organization and topically-induced community
structure; (ii) the dynamics of content production and consumption, to
understand how global trends and popular users emerge; (iii) the behavior of
users labeling media with tags, to determine how they devote their attention
and to explore the variety of their topical interests. Our analysis provides
clues to understand human behavior dynamics on socio-technical systems,
specifically users and content popularity, the mechanisms of users'
interactions in online environments and how collective trends emerge from
individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201
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Investigation of the use of navigation tools in web-based learning: A data mining approach
Web-based learning is widespread in educational settings. The popularity of Web-based learning is in great measure because of its flexibility. Multiple navigation tools provided some of this flexibility. Different navigation tools offer different functions. Therefore, it is important to understand how the navigation tools are used by learners with different backgrounds, knowledge, and skills. This article presents two empirical studies in which data-mining approaches were used to analyze learners' navigation behavior. The results indicate that prior knowledge and subject content are two potential factors influencing the use of navigation tools. In addition, the lack of appropriate use of navigation tools may adversely influence learning performance. The results have been integrated into a model that can help designers develop Web-based learning programs and other Web-based applications that can be tailored to learners' needs
The applications of social media in sports marketing
n the era of big data, sports consumer's activities in social media become valuable assets to sports marketers. In this paper, the authors review extant literature regarding how to effectively use social media to promote sports as well as how to effectively analyze social media data to support business decisions. Methods: The literature review method. Results: Our findings suggest that sports marketers can use social media to achieve the following goals, such as facilitating marketing communication campaigns, adding values to sports products and services, creating a two-way communication between sports brands and consumers, supporting sports sponsorship program, and forging brand communities. As to how to effectively analyze social media data to support business decisions, extent literature suggests that sports marketers to undertake traffic and engagement analysis on their social media sites as well as to conduct sentiment analysis to probe customer's opinions. These insights can support various aspects of business decisions, such as marketing communication management, consumer's voice probing, and sales predictions. Conclusion: Social media are ubiquitous in the sports marketing and consumption practices. In the era of big data, these "footprints" can now be effectively analyzed to generate insights to support business decisions. Recommendations to both the sports marketing practices and research are also addressed
Social Bootstrapping: How Pinterest and Last.fm Social Communities Benefit by Borrowing Links from Facebook
How does one develop a new online community that is highly engaging to each
user and promotes social interaction? A number of websites offer friend-finding
features that help users bootstrap social networks on the website by copying
links from an established network like Facebook or Twitter. This paper
quantifies the extent to which such social bootstrapping is effective in
enhancing a social experience of the website. First, we develop a stylised
analytical model that suggests that copying tends to produce a giant connected
component (i.e., a connected community) quickly and preserves properties such
as reciprocity and clustering, up to a linear multiplicative factor. Second, we
use data from two websites, Pinterest and Last.fm, to empirically compare the
subgraph of links copied from Facebook to links created natively. We find that
the copied subgraph has a giant component, higher reciprocity and clustering,
and confirm that the copied connections see higher social interactions.
However, the need for copying diminishes as users become more active and
influential. Such users tend to create links natively on the website, to users
who are more similar to them than their Facebook friends. Our findings give new
insights into understanding how bootstrapping from established social networks
can help engage new users by enhancing social interactivity.Comment: Proc. 23rd International World Wide Web Conference (WWW), 201
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