The Information Ecology of Social Media and Online Communities

By Finin, Tim; Joshi, Anupam et al. | AI Magazine, Fall 2008 | Go to article overview
Save to active project

The Information Ecology of Social Media and Online Communities

Finin, Tim, Joshi, Anupam, Kolari, Pranam, Java, Akshay, Kale, Anubhav, Karandikar, Amit, AI Magazine

Web-based social media systems such as blogs, wikis, media-sharing sites, and message forums have become an important new way to transmit information, engage in discussions, and form communities on the Internet. Their reach and impact is significant, with tens of millions of people providing content on a regular basis around the world. Recent estimates suggest that social media systems are responsible for as much as one third of new web content. Corporations, traditional media companies, governments, and nongovernmental organizations (NGOs) are working to understand how to adapt to them and use them effectively. Citizens, both young and old, are also discovering how social media technology can improve their lives and give them more voice in the world. We must better understand the information ecology of these new publication methods in order to make them and the information they provide more useful, trustworthy, and reliable.

The blogosphere is part of the web and therefore shares most of its general characteristics. It differs, however, in ways that affect how it should be modeled, analyzed, and exploited. The common model for the general web is as a directed graph of web pages with undifferentiated links between pages. The blogosphere has a much richer network structure in that there are more types of nodes that have more kinds of relations between them (figure 1). For example, the people who contribute to blogs and author blog posts form a social network with their peers, which can be induced by the links between blogs. The blogs themselves form a graph, with direct links to other blogs through blog rolls and indirect links through their posts. Blog posts are linked to their host blogs and typically to other blog posts and web resources as part of their content. A typical blog post has a set of comments that link back to people and blogs associated with them. Finally, the blogosphere trackback protocol generates implicit links between blog posts. Still more detail can be added by taking into account post tags and categories, syndication feeds, and semistructured metadata in the form of extensible markup language (XML) and resource description framework (RDF) content.

In this article, we discuss our ongoing research in modeling the blogosphere and extracting useful information from it. We begin by describing an overarching task of discovering which blogs and bloggers are most influential within a community or about a topic. Pursuing this task uncovers a number of problems that must be addressed, three of which we describe in more detail. The first is recognizing spam in the form of spare blogs (splogs) and spam comments. The second is developing more effective techniques to recognize the social structure of blog communities. The final one involves devising a better abstract model for the underlying blog network structure and how it evolves.


Modeling Influence in the Blogosphere

The blogosphere provides an interesting opportunity to study online social interactions including spread of information, opinion formation, and influence. Through original content and sometimes through commentary on topics of current interest, bloggers influence each other and their audience. We are working to study and characterize these social interactions by modeling the blogosphere and providing novel algorithms for analyzing social media content. Figure 2 shows a hypothetical blog graph and its corresponding flow of information in the influence graph.

Studies on influence in social networks and collaboration graphs have typically focused on the task of identifying key individuals who play an important role in propagating information. This is similar to finding authoritative pages on the web. Epidemic-based models like linear threshold and cascade models (Kempe, Kleinberg, and Tardos 2003 and 2005; Leskovec et al. 2007) have been used to find a small set of individuals who are most influential in a social network.

The rest of this article is only available to active members of Questia

Sign up now for a free, 1-day trial and receive full access to:

  • Questia's entire collection
  • Automatic bibliography creation
  • More helpful research tools like notes, citations, and highlights
  • Ad-free environment

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
Loading One moment ...
Project items
Cite this article

Cited article

Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

Cited article

The Information Ecology of Social Media and Online Communities


Text size Smaller Larger
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

While we understand printed pages are helpful to our users, this limitation is necessary to help protect our publishers' copyrighted material and prevent its unlawful distribution. We are sorry for any inconvenience.
Full screen

matching results for page

Cited passage

Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

Cited passage

Welcome to the new Questia Reader

The Questia Reader has been updated to provide you with an even better online reading experience.  It is now 100% Responsive, which means you can read our books and articles on any sized device you wish.  All of your favorite tools like notes, highlights, and citations are still here, but the way you select text has been updated to be easier to use, especially on touchscreen devices.  Here's how:

1. Click or tap the first word you want to select.
2. Click or tap the last word you want to select.

OK, got it!

Thanks for trying Questia!

Please continue trying out our research tools, but please note, full functionality is available only to our active members.

Your work will be lost once you leave this Web page.

For full access in an ad-free environment, sign up now for a FREE, 1-day trial.

Already a member? Log in now.

Are you sure you want to delete this highlight?