Media coverage at the highest level for your brand can be a thrill. You dramatically increase awareness of your brand, and the links themselves can significantly improve your organic rankings.
When a very popular, authoritative website covers your content, it is often syndicated by other websites. Earn more links organically over time, with no extra effort.
But as a creator of content, I asked myself, "How can I more consciously use link syndication to take advantage of these brand awareness and SEO benefits? Why do some stories enjoy massive syndication after the first earned coverage, while others do not? "
To achieve this goal, I used new machine learning classification techniques to better understand which categories of content and which specific entities (people, places, things), organizations, ideas) appear to have a disproportionately high degree of linkage -Syndication cause.
Continue reading to learn how to use Natural Link Syndication to your advantage when promoting your content to online publishers.
Which topics are most frequently represented in "successful" stories?
First a brief overview of my process:
- I have selected the top publishers online (based on my internal experience and my understanding of the media landscape). , In the end, I had 1
- retrieved and filtered all the stories of the last two years to keep only those stories for which at least 10 links were made. In the end, I had about 4,000.
- Scratched the text of each of these stories.
- Scratched the text by Google's Natural Language API for "c ategories " and "e ntities ."
- Analyzed the data to examine trends and correlations between domains, story categories, entities in stories, and their links to links obtained.
The following analysis reveals interesting insights into the topics and publications that lead to the most syndications (and thus the most productive) regarding link building goals).
Perhaps among the most valuable data I've found in this analysis is understanding which topics and subtopics are most common in my "successful" story record, or which have at least 10 links ( as described above in the description of the process).
Since Google's natural language API labeler returns the category / subcategory / Subcategory, there is a lot of granularity to explore.
Let's start with the topmost categories (category / subcategory) of all messages that contain 10 or more links:
top-level categories (category)
- Arts and Entertainment
- Sensitive topics
- Business and industry
- Law and government
- Spo rts
- People and society
- Computer and electronics
- Science  Top second-level categories (Category / Subcategory)
- Team sports
- TV and video  Energy and supply
- Public security
- Music and audio  Computer and Video Games
Although the results here are not entirely unexpected, they can be quite enlightening if you look at the Ers decide on content between topics.
Of course, creating content related to your business / product / service is essential, but understanding that art and entertainment work extremely well, for example, could be a reason to connect with your company's niche + To discuss art and entertainment.
Takeaway: Think about which of these topics might make sense for your brand and brainstorming, how to create content in those topics to increase the likelihood of link building success.
For example, if your product is a nutrition tracker app, you might consider sensitive topics that you like to talk about, such as: B. the topic of medical discrimination of overweight persons links?
Another way to examine the ability to drive links by subject area is to look at the median links of all the content I've sent through Google's category detection AI (instead of just seeing which Categories / subcategories were the most common).
In this way, I can better understand which categories and subcategories cause disproportionate link volume compared to other categories and subcategories in a typical median case.
Here I see some similar results, although there are some interesting differences.
Top Overall Categories by Median Links
Top First-Level Categories by Median Links (Category)
- Online Communities
- Business and Finance Industry
- Sensitive topics
- Law and government
- Computers and electronics
- People and society
Top second-level categories by median links (category / subcategory)
- Social networks
- Computer security
- Television and video
- Social and advocacy
Tableau dashboards  An interesting example is the topic of health as these two views can inform each other. It is displayed in both previous views, but it is not. 7 in the first, while none. 1 in the median of all links per article.
This shows that there may be less powerful health-related articles overall, but the ones that are available generally perform exceptionally well.
In contrast, I see in art and entertainment that while there are many stories of this kind, it does not crack the top 10 for median links. This could mean that there are many of these types of stories that work reasonably well but less that really stand out.
Takeaway: If you are producing a lower volume of content, it may be wise to invest in it. A topic that tends to outperform the generated median links. If you produce a high volume, you can experiment more to see which topics and subtopics are best for your industry.
To continue with the previous example: If you're an app for the diet tracker that can only produce a small amount of content, it may be most effective to focus on the financial aspect of a healthy diet, given its popularity use of the topic "finances".
Which stores generate the highest median for unique join domains?
This analysis provides the added benefit of providing insights into the publishers themselves. If your primary content marketing goals are to build links and brand awareness, it makes sense to target publications that are prone to good syndication.
Takeaway: Many general news sites are included here (and it's always great to create content for them). Note, however, that there are also other vertical publishers such as Quartz for Business, Gizmodo for Technology and E News! for entertainment.
When creating your pitch lists, do not overlook the industry-specific (and industry-recognized) publishers.
Which entities with wiki entries have the most links?
In addition to category labeling, Google's native-language APIs can also return you "entities" (person, place, thing, brand, etc.).
Google returns two types of entities: a generic "web entity" and a "wiki entity," which is an entity with a well-known Wikipedia page.
These wiki entities are more useful in this case because they allow us to exclude the long tail of entities that are not found in many articles, leaving only the more general entities. Translating this tag to my record with the most powerful link building articles will help me discover additional valuable lessons.
The above view shows the entities with the highest number of median join domains. These topics may be considered "most recent" or "most likely syndicated".
Keep in mind that many are location-dependent and mostly current news about a specific location. I also see individual people like Paul Manafort, Colin Kaepernick and other people who often take top positions. Take a look at the full Tableau dashboard to get more interesting insights on which entities tend to overperform, as you might be able to incorporate them into your content.
Back to our Hypothetical Nutrition App – You may have noticed that many places are listed in the overall list. Consider geographic angles to increase the attractiveness of your content. Maybe a list of local restaurants offering healthy food!
If you want to dive deeper, you can explore the additional tabs in the Tableau Dashboard which contains views of the more detailed entities.
In general, I've found that using AI-driven tools is a great asset to gaining actionable insights on how to identify the right categories / themes / entities for creating content and which media outlets the best chance to provide a high level of visibility and success in the link syndication.
Published October 10, 2019 – 11:00 UTC