1. Social Media and Links
The Love Story (with numbers)
Branko Rihtman
Linklove London 2012
@neyne

2. How People See Scientists
@neyne

3. The Real Scientists
The most exciting phrase to hear in science,
the one that heralds the most discoveries, is
not "Eureka!" (I found it!) but "That's funny..."
~Isaac Asimov
@neyne

4. Scientific principles
The official version The tl;dr version
1. Define the question 1. Don’t BS yourself (or the others)
2. Gather information and resources (observe)
3. Form hypothesis
4. Perform experiment and collect data 2. Truth above profit
5. Analyze data
6. Interpret data and draw conclusions that serve as a 3. Stay curious
starting point for new hypothesis
7. Publish results
8. Retest (frequently done by other scientists) 4. Share
@neyne

5. SEO and Social Media
What we’ve had so far
@neyne

6. What proof is missing?
http://4.bp.blog
spot.com/_clW9
Will it rank? 2NzmFvI/TFRRQ
brsg6I/AAAAAAA
AD8g/Gr91SYY7
D50/s1600/sea-
kitten.jpg
Links
@neyne

7. pinterest.com/
pinterest.com/nordstrom/
source/ikea.com
RTs 0 379
FB Shares 1 1
FB Likes 0 27
+1s 0 1
Links 4 849
Linking
2 134
domains
Not in top 1000
Rank for [ikea] #2 [nordstrom]
#3 for [ikea.com]
@neyne

8. Social Media in link building
Some eyeballs = a link
@neyne

9. Brute force approach
Lots of eyeballs = 2 links
@neyne

10. Let’s try and do it a bit smarter
http://4.bp.blog @user1
spot.com/_clW9 @user2
2NzmFvI/TFRRQ @user3
brsg6I/AAAAAAA @user4
AD8g/Gr91SYY7 ….
D50/s1600/sea- @user50000
kitten.jpg
@neyne

11. Let’s try and do it a bit smarter
http://4.bp.blog @user1
spot.com/_clW9 @user2
2NzmFvI/TFRRQ @user3
brsg6I/AAAAAAA @user4
AD8g/Gr91SYY7 ….
D50/s1600/sea- @user50000
kitten.jpg
Users that share more than one
piece of content = more important
@neyne

12. Let’s try and do it a bit smarter
http://4.bp.blog @user1
spot.com/_clW9 @user2
2NzmFvI/TFRRQ @user3
brsg6I/AAAAAAA @user4
AD8g/Gr91SYY7 ….
D50/s1600/sea- @user50000
kitten.jpg
Content shared by more than
one user = more important
@neyne

13. The Minesweeper Effect
@neyne

14. How do we get all that information?
APIs!!!
APIs? That is CODING!!!!
@neyne

15. @neyne

16. Ingredients
APIs:
Topsy
FB graph
Twitter API
G+ API (more complicated)
MajesticSEO API
And Her Royal Majesty:
EXCEL
+ His Royal Highness
Neil Bosma’s SEOTools Excel Add-in
@neyne

17. Targets
2 Awesome articles
http://www.distilled.net/excel-for-seo/ http://nielsbosma.se/projects/seotools/
@neyne

18. Scraping usernames of sharers
=MID(DownloadString("http://otter.topsy.com/trackbacks.json?url="&$B$1&"&perpage=100"),FIND("
nick",DownloadString("http://otter.topsy.com/trackbacks.json?url="&$B$1&"&perpage=100"),1)+7,FI
ND("""",DownloadString("http://otter.topsy.com/trackbacks.json?url="&$B$1&"&perpage=100"),FIN
D("nick",DownloadString("http://otter.topsy.com/trackbacks.json?url="&$B$1&"&perpage=100"),1)+
7)-
(FIND("nick",DownloadString("http://otter.topsy.com/trackbacks.json?url="&$B$1&"&perpage=100"),
1)+7))
@neyne

19. Scraping shared URLs
=IFERROR(MID(DownloadString("http://otter.topsy.
com/linkposts.json?url=http://twitter.com/"&$A$2
&"&perpage=100"),B6+LEN($A$4)+2,C6-
(B6+LEN($A$4)+2)),"N/A")
@neyne

20. Scraping social metrics
@neyne

21. Scraping backlink metrics
@neyne

22. Plotting links vs. tweets/likes/shares/+1s
Y = aX + b
a – slope of the graph,
rate at which number of links
increases as a function (???)
of number of tweets
@neyne

23. Links as a function of RTs
@neyne

24. Links as a function of FB Likes
@neyne

25. Links as a function of FB Shares
@neyne

26. Links as a function of +1s
@neyne

27. Comparison of graph slopes
@neyne

28. Things to remember
Figures and relative values WILL
vary from niche to niche (+1s may not be the
best link-producing social media strategy in
every industry)
Actual numbers are not precise/important/
proportional to number of achievable links.
What matters is the feel for differences between
different social media strategies
@neyne

29. Where did we start and where did we get to?
2 URLs 214 Twitter users ~ 12K URLs, 3.7K domains
Minesweeper effect
?
@neyne

30. Where did we start and where did we get to?
2 URLs 214 Twitter users ~ 12K URLs, 3.7K domains
1. Prune out the
“selfish users”
2. Identify power users
3. Identify relevant users
(search by keywords)
@neyne

31. Identifying the power users
Users that
have shared
both articles
@neyne

32. Where did we start and where did we get to?
2 URLs 214 Twitter users ~ 12K URLs, 3.7K domains
4. Prune out the
irrelevant URLs
(logos of 4sq, paper.li,
etc.)
5. Assign relevance to
domains (Google site 6. Create a pool of 7. Grade domains according to:
search + keywords) relevant domains for • Already linking to you
future linking outreaches • Not yet linking to you
(Google custom SE) • Linking to competitors
• Not yet linking to
competitors
@neyne

33. What these charts CAN do
Hint towards trends in social media contribution towards links in a
specific niche
Content generation ideas
Point out potential social media targets that may possibly perform
above average in terms of linking potential
Look pretty in reports/presentations
What these charts CANNOT do
Provide specific numbers and goals for link building campaigns
Decide for you whether to prefer one kind of social engagement
over another
Do the actual link building work for you
@neyne

34. Thank you!
Branko Rihtman
branko@rankabove.com
@neyne
http://il.linkedin.com/in/brankorihtman
http://www.seo-scientist.com
@neyne