I've broken this technique into two parts: Technique One — Leveraging Buzzsumo influencer data and twitter scraping to find the most influential journalists writing about any topic Technique Two — Leveraging the Gdelt Dataset to reveal deep story syndication networks between publishers using in-context links. Using Buzzsumo to understand journalist influencer networks on any topic First, you want to figure out who your topc influencers are your a topic. You can locate it on the influences tab, then follow these steps: Select only “Journalists.” This will limit the result to only the Twitter accounts of those known to be reporters and journalists of major publications. The next step is to grab all of the people each of these known journalist influencers follows — the goal is to understand which of these 100 or so influencers impacts the other 100 the most. The large, centrally located nodes are disproportionately followed by other members of the graph and enjoy popularity across the board (from many of the other influential nodes). So, if you had a campaign about a niche topic, you could consider pitching to an influencer surfaced from this data —according to our the visualization, an article shared in their network would have the most reach and potential ROI Using Gdelt to find the most influential websites on a topic with in-context link analysis The first example was a great way to find the best journalists in a niche to pitch to, but top journalists are often the most pitched to overall. We pulled all of the articles in Gdelt’s database that are known to be about a specific theme within a given timeframe. From here, the edge file can be used to build a network visualization where the nodes publishers and the edges between them represent the in-context links found from our Gdelt data pull around whatever topic we desired. Similar to the first example, the higher the betweenness centrality numbers, number of In-degree links, and the more centrally located in the graph, the more “important” that node can generally be said to be. Wrapping up I’ve outlined two different techniques we use at Fractl to understand the influence networks around specific topical areas, both in terms of publications and the writers at those publications.
In my last post, I explained how using network visualization tools can help you massively improve your content marketing PR/Outreach strategy —understanding which news outlets have the largest syndication networks empowers your outreach team to prioritize high-syndication publications over lower syndication publications. The result? The content you are pitching enjoys significantly more widespread link pickups.
Today, I’m going to take you a little deeper — we’ll be looking at a few techniques for forming an even better understanding of the publisher syndication networks in your particular niche. I’ve broken this technique into two parts:
- Technique One — Leveraging Buzzsumo influencer data and twitter scraping to find the most influential journalists writing about any topic
- Technique Two — Leveraging the Gdelt Dataset to reveal deep story syndication networks between publishers using in-context links.
Why do this at all?
If you are interested in generating high-value links at scale, these techniques provide an undeniable competitive advantage — they help you to deeply understand how writers and news publications connect and syndicate to each other.
In our opinion at Fractl, data-driven content stories that have strong news hooks, finding writers and publications who would find the content compelling, and pitching them effectively is the single highest ROI SEO activity possible. Done correctly, it is entirely possible to generate dozens, sometimes even hundreds or thousands, of high-authority links with one or a handful of content campaigns.
Let’s dive in.
Using Buzzsumo to understand journalist influencer networks on any topic
First, you want to figure out who your topc influencers are your a topic. A very handy feature of Buzzsumo is its “influencers” tool. You can locate it on the influences tab, then follow these steps:
- Select only “Journalists.” This will limit the result to only the Twitter accounts of those known to be reporters and journalists of major publications. Bloggers and lower authority publishers will be excluded.
- Search using a topical keyword. If it is straightforward, one or two searches should be fine. If it is more complex, create a few related queries, and collate the twitter accounts that appear in all of them. Alternatively, use the Boolean “and/or” in your search to narrow your result. It is critical to be sure your search results are returning journalists that as closely match your target criteria as possible.
- Ideally, you want at least 100 results. More is generally better, so long as you are sure the results represent your target criteria well.
- Once you are happy with your search result, click export to grab a CSV.
The next step is to grab all of the people each of these known journalist influencers follows — the goal is to understand which of these 100 or so influencers impacts the other 100 the most. Additionally, we want to find people outside of this group that many of these 100 follow in common.
To do so, we leveraged Twint, a handy Twitter scraper available on Github to pull all of the people each of these journalist influencers follow. Using our scraped data, we built an edge list, which allowed us to visualize the result in Gephi.
Here is an interactive version for you to explore, and here is a screenshot of what it looks like:
This graph shows us which nodes (influencers) have the most In-Degree links. In other words: it tells us who, of our media influencers, is most followed.
These are the top 10 nodes:
- Maia Szalavitz (@maiasz) Neuroscience Journalist, VICE and TIME
- Radley Balko (@radleybalko) Opinion journalist, Washington Post
- Johann Hari (@johannhari101) New York Times best-selling author
- David Kroll (@davidkroll) Freelance healthcare writer, Forbes Heath
- Max Daly (@Narcomania) Global Drugs Editor, VICE
- Dana Milbank (@milbank)Columnist, Washington Post
- Sam Quinones (@samquinones7), Author
- Felice Freyer (@felicejfreyer), Boston Globe Reporter, Mental health and Addiction
- Jeanne Whalen (@jeannewhalen) Business Reporter, Washington Post
- Eric Bolling (@ericbolling) New York Times best-selling author