For everyone attending (in the flesh or via Twitter and other means) the PDF Europe, here’s linkfluence’s presentation about the European political web, its structure and dynamics, and the level of interest of different national political communities for the designation of the first President of the European Council. More explanations and details coming today as an update to this post…

UPDATE  & DETAILS (21/11/09)

Let’s get to the bottom of things, shall we.

First, what exactly is included in the map of the Eurosphere. Inside each of the spheres included in this preliminary piece of research (which rests on the analysis of 4 European countries, namely France, Germany, Italy and the Netherlands and a specific European-affairs sphere), there are communities of political bloggers and portals (i.e. communities whose members are affiliated to a given party or clearly advocating a political platform, represented in shades of blue), communities of journalists and experts (shades of green), communities of political pundits commenting on public issues without a clear or distinctive party line (under the label “opinion”, shades of red), media websites (shades of orange), trade unions (shades of purple), think tanks (light blue), institutions (websites of public bodies or international organisations, brown), NGOs and activists (grey).

Second, how do we explore and segment the social web and build maps of online communities in general, and of the political Eurosphere in particular. According to theories and concepts drawn from sociology and the social graph field (see this research paper we published at ESOMAR for a lot more details), we use proprietary web crawlers and algorithms to reveal clusters of websites (blogs, social networks, forums, etc.) that flock together and form true communities whose members listen to and influence one another, mainly around their shared topics of interest (politics in a broad sense for the Eurosphere). The distance between websites (in a given community) and between communities (on a given map like the Eurosphere) simply reveal the density of their interactions. The closer they are, the more they engage each other (or engage the same websites around them, meaning that two websites or two communities may not engage each other but may interact with pretty much the same websites around them), the further away they are, the less they engage each other in conversations. We base ourselves on these concepts to draw conclusions as to the dynamics of the social web and the Eurosphere in particular.

Finally, we also conduct opinion research by way of monitoring all the public conversations happening inside the communities we have mapped. This is how we are able to measure quantitatively and analyse qualitatively different items of perception, interest or judgement.

All the findings of our preliminary piece of research (which will be updated in the coming months) can be found in the PDF file below. Here’s a summary:

Map & Analysis of the European political web

Linkfluence is known for its web cartography expertise, but that’s not all we do. Maps are tools, they define a territory, a “playing field”, which we can then mine and analyze. One way of analyzing the context and content of discussions is by creating word clouds: word clouds provide a convenient albeit imperfect overview of the gist of a large volume of content. Unfortunately, word clouds are typically used as ‘snapshots’ of said content at a given time. This is fine when looking at homogeneous content, but becomes counterproductive when trying to analyze trends, rise or decline of certain words, brands or concepts over time, particularly when considering the ebb and flow of online conversations.

We have been working on this issue for some time, with the objective of combining the convenience of word clouds with a chronological dimension to follow semantic trends. And here it is: “timecloud” allows you to manipulate the time line and update the associated word clouds accordingly.

In the following example, which represents a “timecloud” for the brand Apple within the Mac-fans community, one can easily visualize which words, brands, adjectives come and go over the past 90 days. Unsurprisingly, “iphone” and “app store” remain very prominent throughout the entire period, while product launches like “snow leopard” or “tablet” pop up at the time of the launch then recede in the background.

Hence, this “timecloud” not only displays the most frequently-used words over the selected time period, it also shows the evolution of the conversation through the appearance and disappearance of those key words and concepts. It is also possible to toggle the “list mode” (icon in the upper right hand corner, next to the magnifying glass) to display all the words with the same size, including “unused” words (in light grey) for the selected period. When selecting a word on the tag cloud, a graph will appear at the bottom, showing relative volume of mentions over the entire timeline. Using the ctrl key, one can select up to four words and follow their evolution over time.

It’s been a busy week in the politicosphere, with heavy coverage and discussions about three prominent news topics: the war in Afghanistan and the need for more troops, the U.N. General Assembly and Security Council resolution, and the perennial health care reform debate.

News Trends - Content published across a sample of 13 000 sites

What’s newsworthy –or blogworthy- however largely depends on which part of the web you’re looking at, or which community one hails from. By and large, health care reform remains the dominant topic of discussion within the conservative and progressive communities.

Out of a sample of 1202 conservative blogs and 1190 progressive blogs, health care reform accounted for 9.7% and 12.3% of new content respectively during the week of Sept. 21 to Sept. 27. By comparison, discussions about the need for more troops in Afghanistan “only” accounted for 4.8% of content published on the conservative side vs. 3.5 on the liberal side. The same topic however fired up the Defense (23.1%) and International Affairs (15.7%) communities which naturally dedicated more content to this topic than any other this week.

The International Affairs community got even busier by the second half of the week with a series of news coming out of the United Nations General Assembly, environment summit and security council resolution. The environmental community got stirred up as well by the U.N. Climate Summit in the run-up to Copenhagen.

Let’s see next week if the G-20 summit manages to garner as much attention.

(cross-posted on politicosphere.net) A couple of weeks ago, the New York Times dedicated a very clear and concise article to the ambitious research spearheaded by Jon Kleinberg of Cornell University on “Meme-tracking and the Dynamics of the News Cycle”. The research team “studied the news cycle by looking for repeated phrases and tracking their appearances on 1.6 million mainstream media sites and blogs. Some 90 million articles and blog posts, which appeared from August through October, were scrutinized with their phrase-finding software”.

The research concluded that blogs typically trail behind “MSM”, except in rare instances (3.5% of story lines to be precise) where a handful of blogs picked up on the news first. Unsurprisingly, these are not just any blogs but professional and quasi-professional political blogs with the connections, competence and credibility that in effect puts them on par with traditional news organizations.

We recently had the opportunity to look up close at one such instance where an influential political blog “broke the news”, initiating a new news cycle as national media followed suit. The following video is a visualization of the propagation of the story as it moves across the online political landscape.

Blogs breaking news – firedoglake case study from linkfluence on Vimeo.

note: this visualization was created afterwards, based on a sample of articles covering the story, to dynamically represent the speed at which this particular piece of information spread from a well-identified source to many other blogs and media sites. It is not “real-time” propagation and does not represent every article and every site referencing the story. For methodology questions regarding the map itself, please refer to the “Keys” section on this page .