Building Links with Great Content – Natural Syndication Networks

The debate is over and the results are clear: the best way to improve domain authority is to generate large numbers of earned links from high-authority publishers.

Getting these links is not possible via:

• Link exchanges
• Buying links
• Private Blog Networks, or PBNs
• Comment links
• Paid native content or sponsored posts
• Any other method you may have encountered

There is no shortcut. The only way to earn these links is by creating content that is so interesting, relevant, and newsworthy to a publisher’s audience that the publisher will want to write about that content themselves.

Success, then, is predicated on doing three things extremely well:

1. Developing newsworthy content (typically meaning that content is data-driven)
2. Understanding who to pitch for the best opportunity at success and natural syndication
3. Writing and sending pitches effectively

We’ve covered point 1 and point 3 on other Moz posts. Today, we are going to do a deep dive into point 2 and investigate methods for understanding and choosing the best possible places to pitch your content. Specifically, we will reveal the hidden news syndication networks that can mean the difference between generating less than a handful or thousands of links from your data-driven content.

Understanding News Syndication Networks

Not all news publishers are the same. Some publishers behave as hubs, or influencers, generating the stories and content that is then “picked up” and written about by other publishers covering the same or similar beats.

Some of the top hubs should be obvious to anyone: CNN, The New York Times, BBC, or Reuters, for instance. Their size, brand authority, and ability to break news make them go-to sources for the origination of news and some of the most common places journalists and writers from other publications go to for story ideas. If your content gets picked up by any of these sites, it’s almost certain that you will enjoy widespread syndication of your story to nearly everywhere that could be interested without any intervention on your part.

Unfortunately, outside of the biggest players, it’s often unclear which other sites also enjoy “Hub Status,” acting as a source for much of the news writing that happens around any specific topic or beat.

At Fractl, our experience pitching top publishers has given us a deep intuition of which domains are likely to be our best bet for the syndication potential of content we create on behalf of our clients, but we wanted to go a step further and put data to the question. Which publishers really act as the biggest hubs of content distribution?

To get a better handle on this question, we took a look at the link networks of the top 400 most trafficked American publishers online. We then utilized Gephi, a powerful network visualization tool to make sense of this massive web of links. Below is a visualization of that network.

An interactive version is available here.

Before explaining further, let’s detail how the visualization works:

• Each colored circle is called a node. A node represents one publisher/website
• Node size is related to Domain Authority. The larger the node, the more domain authority it has.
• The lines between the nodes are called edges, and represent the links between each publisher.
• The strength of the edges/links corresponds to the total number of links from one publisher to another. The more links from one publisher to another, the stronger the edge, and the more “pull” exerted between those two nodes toward each other.
• You can think of the visualization almost like an epic game of tug of war, where nodes with similar link networks end up clustering near each other.
• The colors of the nodes are determined by a “Modularity” algorithm that looks at the overall similarity of link networks, comparing all nodes to each other. Nodes with the same color exhibit the most similarity. The modularity algorithm implemented in Gephi looks for the nodes that are more densely connected together than to the rest of the network

Once visualized, important takeaways that can be realized include the following:

1. The most “central” nodes, or the ones appearing near the center of the graph, are the ones that enjoy links from the widest variety of sites. Naturally, the big boys like Reuters, CNN and the NYTimes are located at the center, with large volumes of links incoming from all over.
2. Tight clusters are publishers that link to each other very often, which creates a strong attractive force and keeps them close together. Publishers like these are often either owned by the same parent company or have built-in automatic link syndication relationships. A good example is the Gawker Network (at the 10PM position). The closeness of nodes in this network is the result of heavy interlinking and story syndication, along with the effects of site-wide links shared between them. A similar cluster appears at the 7PM position with the major NBC-owned publishers (NBC.com, MSNBC.com, Today.com, etc.). Nearby, we also see large NBC-owned regional publishers, indicating heavy story syndication also to these regional owned properties.
3. Non-obvious similarities between the publishers can also be gleaned. For instance, notice how FoxNews.com and TMZ.com are very closely grouped, sharing very similar link profiles and also linking to each other extensively. Another interesting cluster to note is the Buzzfeed/Vice cluster. Notice their centrality lies somewhere between serious news and lifestyle, with linkages extending out into both.
4. Sites that cover similar themes/beats are often located close to each other in the visualization. We can see top-tier lifestyle publishers clustered around the 1PM position. News publishers clustered near other news publishers with similar political leanings. Notice the closeness of Politico, Salon, The Atlantic, and The Washington Post. Similarly, notice the proximity of Breitbart, The Daily Caller, and BizPacReview. These relationships hint at hidden biases and relationships in how these publishers pick up each other’s stories.

A More Global Perspective

Last year, a fascinating project by Kalev Leetaru at Forbes looked at the dynamics Google News publishers in the US and around the world. The project leveraged GDelt’s massive news article dataset, and visualized the network with Gephi, similarly to the above network discussed in the previous paragraph.

This visualization differs in that the link network was built looking only at in-context links, whereas the visualization featured in the previous paragraph looked at all links. This is perhaps an even more accurate view of news syndication networks because it better parses out site-wide links, navigation links, and other non-context links that impact the graph. Additionally, this graph was generated using more than 121 million articles from nearly every country in the world, containing almost three-quarters of a billion individual links. It represents one of the most accurate pictures of the dynamics of the global news landscape ever assembled.

Edge weights were determined by the total number of links from each node to each other node. The more links, the stronger the edge. Node sizes were calculated using Pagerank in this case instead of Domain Authority, though they are similar metrics.

Using this visualization, Mr. Leetaru was able to infer some incredibly interesting and potentially powerful relationships that have implications for anyone who pitches mainstream publishers. Some of the most important include:

1. In the center of the graph, we see a very large cluster. This cluster can be thought of as essentially the “Global Media Core,” as Mr. Leetaru puts it. Green nodes represent American outlets. This, as with the previous example, shows the frequency with which these primary news outlets interlink and cover each other’s stories, as well as how much less frequently they cite sources from smaller publications or local and regional outlets.
2. Interestingly, CNN seems to play a unique role in the dissemination to local and regional news. Note the many links from CNN to the blue cluster on the far right. Mr. Leetaru speculates this could be the result of other major outlets like the NYTimes and the Washington Post using paywalls. This point is important for anyone who pitches content. Paywalls should be something taken into consideration, as they could potentially significantly reduce syndication elsewhere.
3. The NPR cluster is another fascinating one, suggesting that there is heavy interlinking between NPR-related stories and also between NPR and the Washington Post and NYTimes. Getting a pickup on NPR’s main site could result in syndication to many of its affiliates. NYTimes or Washington Post pickups could also have a similar effect due to this interlinking.
4. For those looking for international syndication, there are some other interesting standouts. Sites like NYYibada.com cover news in the US. They are involved with Chinese language publications, but also have versions in other languages, including English. Sites like this might not seem to be good pitch targets, but could likely be pitched successfully given their coverage of many of the same stories as US-based English language publications.
5. The blue and pink clusters at the bottom of the graph are outlets from the Russian and Ukrainian press, respectively. You will notice that while the vast majority of their linking is self-contained, there seem to be three bridges to international press, specifically via the BBC, Reuters, and AP. This suggests getting pickups at these outlets could result in much broader international syndication, at least in Eastern Europe and Russia.
6. Additionally, the overall lack of deep interlinking between publications of different languages suggests that it is quite difficult to get English stories picked up internationally.
7. Sites like ZDnet.com have foreign language counterparts, and often translate their stories for their international properties. Sites like these offer unique opportunities for link syndication into mostly isolated islands of foreign publications that would be difficult to reach otherwise.

I would encourage readers to explore this interactive more. Isolating individual publications can give deep insight into what syndication potential might be possible for any story covered. Of course, many factors impact how a story spreads through these networks. As a general rule, the broader the syndication network, the more opportunities that exist.

Link Syndication in Practice

Over our 6 years in business, Fractl has executed more than 1,500 content marketing campaigns, promoted using high-touch, one-to-one outreach to major publications. Below are two views of content syndication we have seen as a result of our content production and promotion work.

Let’s first look just at a single campaign.

Recently, Fractl scored a big win for our client Signs.com with our “Branded in Memory” campaign, which was a fun and visual look at how well people remember brand logos. We had the crowd attempt to recreate well-known brand logos from memory, and completed data analysis to understand more deeply which brands seem to have the best overall recall.

As a result of strategic pitching, the high public appeal, and the overall “coolness” factor of the project, it was picked up widely by many mainstream publications, and enjoyed extensive syndication.

Here is what that syndication looked like in network graph form over time:

If you are interested in seeing and exploring the full graph, you can access the interactive by clicking on the gif above, or clicking here. As with previous examples, node size is related to domain authority.

A few important things to note:

• The orange cluster of nodes surrounding the central node are links directly to the landing page on Signs.com.
• Several pickups resulted in nodes (publications) that themselves generated many numbers of links pointing at the story they wrote about the Signs.com project. The blue cluster at the 8PM position is a great example. In this case it was a pickup from BoredPanda.com.
• Nodes that do not link to Signs.com are secondary syndications. They pass link value through the node that links to Signs.com, and represent an opportunity for link reclamation. Fractl follows up on all of these opportunities in an attempt to turn these secondary syndications into do-follow links pointing directly at our client’s domain.
• An animated view gives an interesting insight into the pace of link accumulation both to the primary story on Signs.com, but also to the nodes that garnered their own secondary syndications. The GIF represents a full year of pickups. As we found in my previous Moz post examining link acquisition over time, roughly 50% of the links were acquired in the first month, and the other 50% over the next 11 months.

Now, let’s take a look at what syndication networks look like when aggregated across roughly 3 months worth of Fractl client campaigns (not fully comprehensive):

If you are interested in exploring this in more depth, click here or the above image for the interactive. As with previous examples, node size is related to domain authority.

A few important things to note:

1. The brown cluster near the center labeled “placements” are links pointing back directly to the landing pages on our clients’ sites. Many/most of these links were the result of pitches to writers and editors at those publications, and not as a result of natural syndication.
2. We can see many major hubs with their own attached orbits of linking nodes. At 9PM, we see entrepreneur.com, at 12PM we see CNBC.com, 10PM we see USAToday, etc.
3. Publications with large numbers of linking nodes surrounding them are examples of prime pitching targets, given how syndications link back to stories on those publications appear in this aggregate view.

Putting it All Together

New data tools are enabling the ability to more deeply understand how the universe of news publications and the larger “blogosphere” operate dynamically. Network visualization tools in particular can be put to use to yield otherwise impossible insights about the relationships between publications and how content is distributed and syndicated through these networks.

The best part is that creating visualizations with your own data is very straightforward. For instance, the link graphs of Fractl content examples, along with the first overarching view of news networks, was built using backlink exports from SEMrush. Additionally, third party resources such as Gdelt offer tools and datasets that are virtually unexplored, providing opportunity for deep understanding that can convey significant advantages for those looking to optimize their content promotion and syndication process.

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