Two years ago I thought about how we could trace back to the original source of information, mostly news, on the web: the first tweet, the igniting blog post, the seminal article that got shared, sliced to quotes, linked to, built upon, changed, remixed, archived. Not only it’s a good way to understand the current online ecosystem, but if analyzed correctly, this flow could provide new insights to create new distribution strategies for news contents, and a real assessment of the impact of a specific event in social media (all media is social these days, by the way). I presented my thoughts about this last year to a class of MA students in their Cyberjournalism class at Porto University and the plan was to build a tool that would track that flow from the very start.
Of course, better said than done, except at the NYTimes, where they built a tool quite similar to my original concept called Cascade. They did in 3D, like I wanted to, and it’s beautiful to look at. Check their video but read on afterwards to dive into my own concept, which is similar in basics but somewhat different.
The way information about a news event is distributed has changed dramatically in the last years. The so called traditional media are no longer the diving force in this process rather to be substituted, in part, by the active participation of users, that became creators, distributors, and sharers of news contents, using tools like Twitter, Youtube, Facebook, blogs and other social networks.
We already know this, but what I’m looking for with this proposal is a model that provides a clearer view on this change, its consequences, and how media should rework their strategy in this decentralized logic, and take the most from it. To understand that logic allows a better, more profitable management of their resources, in gathering and subsequent information distribution, leaving their central role as source of all journalistic contents, but intervening in different parts and ways along the flow. It’s the end of the mainstream media concept and their transformation to stream media.
The ideas presented here were the basis for a prototype, that could be validated and improved with a more scientific and empirical approach. My goal was to create a three dimensional, dynamic visualization, that corresponded to reality. The NYTimes example presented above is a good example of that. But for now, I’ll use my drafts, which are quite simple, to give a basic perspective on the current and future changes in the news paradigm.
In the age of pyramids
For decades, news were built and distributed in the same way,, and we have to understand the classic news model that dominated from the industrialization of journalism, specially in the 20th century, and that remained relatively unchanged even with the coming of new media like Radio or Television. The breaking point occurs with the internet and the Digital Revolution.
Media as source of the information flow
In a pretty simple way, this is how it worked:
- Journalists gathered information in the field with the news story characters, witnesses, official entities;
- Information was edited, published;
- The audience had access to the information in print the next day or in the following news segments on TV or Radio;
- After consuming the news, all the information could be discussed by the readers/listeners/viewers in a more or less private way, in a direct, interpersonal relationship. The information could be recovered in new news pieces if new developments occurred;
The very own ritualization of the journalistic process, with news at fixed hours and institutional establishment next to communities and sovereign bodies, contributed to make it closed and limited to a reduced number of people that determined the degree of importance of a specific event, following rules and guides, almost clerically. They were the defenders of public interest, the public opinion makers, and the power to reach the masses granted them the status of the Fourth Estate. News consumers participated in the information routine simultaneously as actors and public, with very few interference in the practice of news professionals, that held absolute control on what and how it should be published.
The media were the creating hub of all news contents, and all their work was directed from top to bottom, in a pyramidal structure, whether inside news organizations, in content distribution, or in content building (the inverted pyramid), and they were its keepers. But with the new technologies it all collapsed, and we watched the horizontalization of the news process, that now occurs also beyond the borders of the traditional journalistic structures.
From carpet bombing communication to relational communication
RelationalSo if in the previous paradigm information was static, closed, finite and with a short half-life, the current situation is pretty much the opposite. If the audience was indiscriminately bombarded with information, the internet provided room for niches, with specialized information. And links and the link economy changed everything: we can comment and quote on a specific piece of information providing immediate access to it. We share and point the path to information. Add the social media/aggregation/recommendation/distribution tools and we are no longer passive elements in the news flow, but active characters in the creation and distribution of information.
- Of or arising from kinship.
- Indicating or constituting relation.
With the 24 hour news cycle and the permanent breaking news status (news are no longer “breaking” these days, they’re just “happening”; news can be “exclusive”, but basing their importance in a time factor is, to say the least, irrelevant) the pressure on media to keep the information flowing has become intense:for example, the traditional news cycles for newspapers no longer comply with the needs of a permanently connected audience, nor the construction of ritualized information published at a specific moment if made with “breaking news” in mind.
Another thing that doesn’t work is closed content, or content that cannot be shared or distributed in different platforms. Facebook has become an important place to access information, where individual articles from specific brands chosen by users are shared and commented by personal networks immediately. Twitter was probably the first place where this happened in a massive scale. Recommendation became a simple process, that took news from the media platforms to individual platforms, that are based in the sharing logic. Check this Pew Research Center report about how people navigate news online.
What we are watching today is the “user curation of content”, where news spread faster and farther than they did, just because the users are no longer mere recipients of indiscriminate information but active participants in its distribution. This new ecosystem broke the previous model where media were in a stand bombing the audience with information, in a one way relationship, moving now to a situation where exchange is the rule.
And media is no longer the sole source of news, just remember how many events were first transmitted by users through social networks and online tools only to be picked up but news pros afterwards: earthquakes, the Iran election and, more recently, Bin Laden’s death are great examples about the role of users and social media in the distribution of news.
With these factors in mind, I thought about how we could visualize the flow of information, from the very first tweet, post, video, etc.
The upward spiral
The main difference between my idea and the NY Times visualization is that I thought about a spiral instead of ramifications of content. This seemed to be the most effective way because I had a few parameters in mind: time, range and audience attention. In my draft Facebook wasn’t considered because it was 2009 and it wasn’t as important in the flow like Twitter was, and blogs had more relevance in this ecosystem.
So, this is how it works: at the epicentre there’s the event, first tweets and posts, picked up afterwards by media as breaking news, retweets/shares in social networks, comments, more media articles, new user generated content based on new information or built upon the existing one,and so on and on. Audience attention is higher at the beginning, and it fades as the flow widens. This is not a process closed in time, because information can be recovered and reutilized days, weeks, months, years later, which is another feature of digital content: it’s perennial, and database or archive journalism is something that has been growing recently.
These are measurable parameters, if only there was a tool to compile and calculate them…
Of course in reality things might differ, and the constant elements may vary, because media companies can be the creators of the first tweet, or other tools can be used – Iran elections had a huge impact also because of the YouTube videos made available by the protesters – but the core idea is the following: there is a root (or roots) that generate more content, linking opportunities through sharing, recommendation or referral, and construction of new content based on the pre-existing information.
What I had in mind and that Cascade does superbly is to track the various connections, and evaluate the ripple effect caused by a single piece of content. Mapping the origins of information and assess their impact can be useful not only to validate that information but also to develop new strategies in content distribution.
The Tornado Effect
The development of this flow, represented by a spiral, would create what I call the Tornado Effect. Imagine a horizontal axis, a timeline representing the event developing chronologically, and the vertical axis where the information spirals upwards in time and connections. The more numerous the connections and links, the higher the spiral; the longer the interest lasts, the lengthier the timeline. Most events would briefly touch down and dissolve away, others would be powerful enough to drag a significant number of users and platforms into it, or even generate new tornadoes.
There is a scale to measure tornado power, the Fujita scale, that goes from F0 to F10, and I was thinking about having one applicable to these phenomena, let’s say from G1 to G10 (the Gamela scale – meant it as a joke, ok?). Events like Michael Jackson’s death, the revolutions in Northern Africa, William and Kate’s marriage would be high up in the scale; blog posts and articles shared on Twitter by a small group of people within a short period of time would be close to zero.
And if we selected paths of information, from an original source to it’s various ramifications, like we can observe in Cascade, we would have something like lightnings inside the tornado, connecting the related content dots along the spiral. Sounds fun doesn’t it?
Leaving visual metaphors aside, what’s the purpose? First, we could analyze where does the information come from; then how it is shared and used; and finally, who contributed the most. A thorough analysis of the path of information could represent a shift in the strategy for media companies that could reconsider how to find, distribute, create and aggregate content, making it more viral, and rethinking it to augment its longevity and usefulness.
The role of the users in this process would be better observed and weighed, and a more complete view on the platforms and mechanisms they apply accessing and sharing information would be possible.
And above all, new types of journalism practices would have to be applied. Curation has become a huge concern for media pros, and there are many tools to curate content, professionally produced or not. Archive journalism would become more important than it is now, since curation is creating archives in almost real time in a way, but with these tornadoes mapped out we could infer who tweeted first, what was the relationship between events and content, and related contents, down to the millisecond, since everything online has a timestamp. Information is now perennial, and should be used taking that feature into account.
Of course, this is not a fully developed idea, and I lack the skills to build such analytic tool. But as concept, I believe it would be useful to understand the spreading of news and create new strategies from that understanding, and at the same time mapping out and monitoring the evolution of information. And the Cascade project is quite close to that.
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