In this article, I will talk about the recent developments with the alpha version of Talk Digger and how it could be use to visualize the interactions between conversations tracked by it.
Recent developments
Yesterday I started to crawl most of the URLs submitted to Talk Digger in the past 6 months and indexing all the results in its new database.
Right now Talk Digger is tracking about 2500 URLs (so it has about 2500 conversations), and it indexed about 80 000 sources (other web pages linking to these 2500 conversations).
These numbers are not big, but the preliminary results are quite impressive (in my humble opinion). In fact, each time new URLs were tracked, new conversations was created, new sources was indexed, I discovered new ways to use it, to discover new stuff, to visualize relations between the data, etc.: the patterns were starting to emerge.
Visualizing interactions between Web conversations
In only 30 minutes of conversation browsing, I noticed 7 interesting use cases (patterns) in the system. I will present all of them by describing what is happening with each of them.
I added two visualization tools in the right sidebar of each conversation page.
The first tool
The first tool will help the users to answer to these two questions:
- What are the other conversations that are talking about the current one?
- What are the conversations the current one is talking about?
The current conversation is the one in light-blue, in the middle of the panel: “Talk Digger: find, follow and join discussions evolving on the Internet”.
From there, I know that the “Talk Digger: find, follow and join discussions evolving on the Internet” conversation is talking (in relation) with the other conversation “Frédérick Giasson – Computer scientist, software developer and consultant”.
It makes sense considering that I am the creator of Talk Digger and that the conversation “Frédérick Giasson – Computer scientist, software developer and consultant” is created by the URL of my personal web page.
I can also see that the conversations: “3spots”, “Library clips”, “ Digg Tools”, “decor8”, are also in relation with the current one.
That way, I can easily visualize the relationship between the conversations tracked by Talk Digger.
The second tool
The second tool will help the users to know what are the different conversations tracked by Talk Digger that came from the same source (URL).
From this panel, I know that Talk Digger is tracking two other conversations closely related to the current one: “Talk Digger Tools: Bookmarklet” and “Talk Digger Tour: Use the bookmarklet”.
In reality, these two other conversations are two different pages from a same domain name: talkdigger.com
Okay, now it is the time to check at the use cases to understand how these two tools can be used.
Use case #1: A normal blog or personal webpage.
This is the case of a conversation evolving around a single blog (or personal web page) and its interactions with other conversations:
In this example, the current conversation is the one of my personal web page.
What is interesting here is that we can see how it relates to itself. We can see that from my main page, I link to two other pages that have their conversations tracked by Talk Digger.
Also, I see that “jotsheet – blog o’ tom Sherman” also has a relation with me. In fact, tom Sherman is a old user of Talk Digger and talked about it in many of his blog posts.
I can also see other pages, from the same domain name, which has a conversation tracked by Talk Digger.
The difference between these results and the above ones is that they are not necessarily linking together (in opposition to the above relations).
Use case #2: Discovering the relation between a web page and its blog
In this example, I found the relation between a normal website (Library Law) and its blog (LibraryLaw Blog). What is interesting is that if you go on the Library Law’s web site, its blog is not clearly displayed. However, the relation between the two is clearly apparent.
Use case #3: Topic specific blogs and web sites.
Another interesting pattern is the one created by topic-specific blogs and web sites.
In this example, I used the Micro Persuasion blog wrote by Steve Rubel. This blog is focus on Web 2.0 news. As you can see, the “Micro Persuasion” conversation is relation (talk about) the conversations of other Web 2.0 services like “del.icio.us”, “Rollyo” and “Netvibes”.
So the relations here are topic centered.
Use case #4: Egocentric blogger.
This use case is fascinating because it shows how someone can relate to itself.
In this example, Robert Sanzalone, the writer behind the Pacific IT blog started track conversations for many of his blog posts. That way, we can easily visualize of one post is relating with the others.
Use case #5: Who cares about my photos?
Some people also care about what people say about their photo.
If we check the conversation evolving around nattu’s Flikr photo album, we will see two things:
- That the conversation created by this photo album is in relation with another conversation tracked by Talk Digger.
- That there is many other people that care about the conversation evolving around other people’s photo album.
Use case #6: In the news
Other people like to know what is the conversation evolving around specific pieces of news.
This is really interesting. We have a piece of news from ZDNet called “The new meaning of programming”. We instantly know that it relate with another conversation called “SocialNets & The Power of The URL”.
We also know that later, other pieces of news talked about it: “Mark Cuban is Wrong”, etc.
This is really interesting to find out how news could relate one between the other.
Use case #7: Online communities’ users.
Other people like to know the conversation evolving around their online persona present on online communities’ web site like MySpaces and LiveJournal.
In this example, we want to see the conversation about the user “2220s” on MySpaces. As we can see, 22-20s’s LiveJournal is talking about him.
We can also see a list of conversations evolving around many other MySpaces users’ page.
Conclusion
As we saw, depending on the source (URL), many different relationship patterns can emerge from Talk Digger’s conversations.
These preliminary results are quite exciting considering that I just started to crawl URLs since yesterday. I think the current infrastructure I developed in the past months is promising, the next steps is to continue crawling URLs and to get users using it.
Subscribe to the Alpha version of Talk Digger
If you would like to test these features, you can always subscribe for a user account. The next round of account created is planned for mid-August.
Technorati: Talkdigger | conversation | web | data | visualization | web2.0 | semantic | pattern |
