Social media index

16Jul07

logo - Social Media Index

Traditionally, an individual’s web influence was measured by the success of their blog. In its simplest form this was done by counting how many people subscribed and linked to it. However, in today’s Web 2.0 world, this is no longer a credible metric as people are currently using a variety of different social media tools to inform and hold conversations with their audience.

FACT: There is a definitive need to assess any social media publisher’s influence on the market as a whole.

What is becoming increasingly clear is that the more engaged an individual is within the different channels available, the broader influence that person has.

I have developed a model, which recognises and attempts to quantify the impact and influence of multiple social media tools.

FACT: This methodology is not the standard.
The standard is a long way down the road. I have selected one way (of many) to analyse different individuals and I would like this post to provoke debate so that together the community can create a standard. This could include what social media tools to analyse (e.g. Facebook or MySpace or both?) and what weighting should be given to each category (e.g. is Twitter just as important as blogging?).

I admit that I am comparing apples and pears, adding them together and giving a total. I am sure that this would make any statistician have a coronary – but without any other scale to work on I have created my own index and hope that it can act as a catalyst to create something better.

Tables:

  1. Create list of top 30 blogs (see table: Top 30 Blogs – SMI weighting: 100% blogs)
    I have created a ‘top 30 table’ of blogs by collating the following lists: CNET Blog 100, Times Top 50 Business Blogs, Power 150 Top Marketing Blogs, Friendly Ghost Top PR Blogs and Technobabble 2.0 Top Analyst Blogs. A blog’s score analysed Google Rank, inbound links, subscribers, alexa rank, content focus, frequency of updates, number of comments (see end of post for detailed methodology).
  2. Apply SMI weighted ranking to top 30 bloggers (see table: Top 30 Bloggers – SMI tiered weighting)
    This table clearly shows how someone’s score is adjusted when other social media tools are taken into consideration)
  3. Create list of SMI top 30 (see table: Social Media Index Top 30)
    This table creates a new list of top 30 individuals based upon the SMI scale. Note how blogs that were not previously in the top 30 are now included as well as some dropping out entirely.

Methodology summary: (full description at bottom of post)
Each blog has been given a score out of 10 based upon 6 criteria:

  • Blog – analysed Google Rank, inbound links, subscribers, alexa rank, content focus, frequency of updates, number of comments
  • Multi-format – analysed Facebook – number of friends
  • Mini-updates – analysed Twitter – number of friends, followers and updates
  • Business cards – analysed LinkedIn – number of contacts
  • Visual – analysed Flickr – number of photos uploaded from you or about you
  • Favourites – analysed Digg, del.icio.us

Each score out of 10 was given a defined weighting (see below) which created a total score for each category. The sum of each of these numbers created an individual’s Social Media Index. This index tells you the sum total of a person’s influence over multiple social media platforms.

Top 30 Blog – SMI weighting: 100% blogs

  Name blog multi format mini updates business cards visual favourites social media index

1

TechCrunch

98

0

0

0

0 0

98

2 Search Engine Watch 98

0

0

0

0 0 98
3 Boing Boing 98

0

0

0

0 0 98

4

GigaOM

97

0

0

0

0 0

97

5

Micro Persuasion

96

0

0

0

0 0

96

6

Scobeleizer

96

0

0

0

0 0

96

7

Scripting News

96

0

0

0

0 0

96

8 John Battelle’s Searchblog 96

0

0

0

0 0 96
9 Techdirt 96

0

0

0

0 0 96
10 Pronet Advertising 96

0

0

0

0 0 96

11

Gaping Void

95

0

0

0

0 0

95

12

Marketing Pilgrim

95

0

0

0

0 0

95

13

Online Marketing Blog

95

0

0

0

0 0 95
14 Copyblogger 95

0

0

0

0 0 95

15

Buzz Machine

94

0

0

0

0 0

94

16 SEOmoz Blog 94

0

0

0

0 0 94
17 Jonathan’s Blog 94

0

0

0

0 0 94
18 The Blog Herald 94

0

0

0

0 0 94
19 direct2dell.com 93

0

0

0

0 0 93
20 Romenesko 93

0

0

0

0 0 93
21 PaidContent.org 92

0

0

0

0 0 92
22 Secret Diary of Steve Jobs 92

0

0

0

0 0 92

23

Web Strategy by Jeremiah

91

0

0

0

0 0 91

24

Adrants

91

0

0

0

0 0

91

25 Seth Godin 91

0

0

0

0 0 91
26 Logic+Emotion 91

0

0

0

0 0 91
27 tompeters! 91

0

0

0

0 0 91
28 GrokDotCom 91

0

0

0

0 0 91

29

PSFK

89

0

0

0

0 0

89

30 adfreak 89

0

0

0

0 0 89

Top 30 Blogs – SMI tiered weighting
Weighting: Blog – 30%; Multi-format – 20%; Mini-updates – 25%, Business cards – 7%, Visual – 3%; Favourites – 15%

  Name blog multi format mini updates business cards visual favourites social media index

1

Micro Persuasion

29 17 25 7 2 13 93
2

TechCrunch

29 20 22 0 3 13 87
3

Scobeleizer

29 20 25 0 3 9 86

4

GigaOM

29 20 13 7 3 13 85
5

Gaping Void

28 20 22 3 2 5 80
6

Scripting News

29 16 25 4 2 4 79
7

Web Strategy by Jeremiah

27 15 24 6 3 2 77
8

PSFK

27 16 14 0 3 12 72
9

Marketing Pilgrim

28 8 15 6 2 7 67
10

Adrants

27 5 19 4 1 8 64
11

Online Marketing Blog

28 15 12 0 2 5 62
12

John Battelle’s Searchblog

29 17 0 7 3 5 61
13

Buzz Machine

28 20 0 5 2 5 61
14

Copyblogger

28 13 3 0 0 8 53
15

Seth Godin

27 5 0 0 3 9 45
16

Techdirt

29 0 0 0 3 11 42
17 SEOmoz Blog 28

0

0

5 3 5 41
18 Search Engine Watch 29

0

0

0

0 9 38
19 Boing Boing 29

0

0

0

3 5 38
20 Jonathan’s Blog 28 1

0

0

2 4 36
21 PaidContent.org 27

0

0

0

3 4 34
22 Secret Diary of Steve Jobs 27

0

0

0

0 6 34
23 adfreak 27

0

0

0

0 6 32
24 The Blog Herald 28

0

0

0

0 3 31
25 direct2dell.com 28

0

0

0

0 3 31
26 Pronet Advertising 29

0

0

0

0 2 31
27 Romenesko 28

0

0

0

0 3 31
28 Logic+Emotion 27

0

0

0

1 2 30
29 tompeters! 27

0

0

0

0 3 30
30 GrokDotCom 27

0

0

0

0 3 30

Top 30 social media index
Weighting: Blog – 30%; Multi-format – 20%; Mini-updates – 25%, Business cards – 7%, Visual – 3%; Favourites – 15%

  Name blog multi format mini updates business cards visual favourites social media index

1

Micro Persuasion

29

17

25

7

2

13

93

2

TechCrunch

29

20

22

0

3

13

87

3

Scobeleizer

29

20

25

0

3

9

86

4

GigaOM

29

20

13

7

3

13

85

5

Gaping Void

28

20

22

3

2

5

80

6

Scripting News

29

16

25

4

2

4

79

7

Web Strategy by Jeremiah

27

15

24

6

3

2

77

8

/Message

25

20

23

0

3

6

76

9

POP! PR Jots

21

19

23

6

2

3

72

10

PSFK

27

16

14

0

3

12

72

11

James Governor’s Monkchips

26

12

21

5

1

6

71

12

hyku | blog

23

15

20

5

2

3

68

13

Ars Technica

18

16

25

2

3

4

67

14

Marketing Pilgrim

28

8

15

6

2

7

67

15

Marketing Nirvana

23

16

15

6

2

4

65

16

a shel of my former self

24

15

15

5

2

4

65

17

Adrants

27

5

19

4

1

8

64

18

People over Process

23

5

23

5

1

5

63

19

What’s Next Blog

25

5

23

1

2

6

63

20

Strumpette

24

15

21

0

1

2

62

21

Online Marketing Blog

28

15

12

0

2

5

62

22

John Battelle’s Searchblog

29

17

0

7

3

5

61

23

Buzz Machine

28

20

0

5

2

5

61

24

Tecosystems

24

4

17

5

3

6

59

25

The Groundswell

26

11

12

6

0

4

58

26

Marketing Begins at Home

20

12

18

4

3

1

58

27

Common Sense PR

20

15

15

3

0

4

57

28

PR meets the WWW

19

12

15

4

1

5

56

29

Russell Davies

25

0

22

0

3

6

56

30

Drew’s Marketing Minute

26

5

17

6

0

1

56

Review

What is immediately evident is that an individuals social media index (SMI) can alter significantly as the weighting is adjusted. Individuals such as Seth Godin (widely recognised as the top marketing blogger) score falls by 50% once the weighting is changed. (SMI with 100% blog weighting = 91, SMI score with tiered weighting =45.) This is because he has chosen to focus his engagement via a few specific sources of media. Does this mean that he is missing a trick or is he being more prudent and selecting which media he aims to master. In addition, several people who would not normally feature in a top list, suddenly appear when the SMI versions are used.

This model is not perfect – moving ahead I request your comments both on the need to score people over multiple social media tools and what weighting should be given to each category.

Together we can grow a new standard. I don’t care what it is, but I know it’s grown far beyond inbound links, page views and the like.

It’s time for a new debate.

Please join us.

Looking Ahead

a) Social Media Index Conference (September, London)
If there is sufficient interest, Edelman will hold a summit on this topic in London and we encourage all those with an opinion on this subject to attend (in person or via WebEx). More details to follow.

b) Wiki debate
I will setup a wiki to debate this topic and welcome contributions from all involved.

c) Social Media Index widget
Made available to everyone, simply enter your data and receive your SMI number.

d) Specific lists
Tables covering multiple areas will be published. If you would like to suggest a list, please let me know.

Methodology

a) Blogs

Google PageRank
Google PageRank is a link analysis algorithm that interprets web links and assigns a numerical weighting to each site. High-quality sites receive a higher PageRank. The ranking uses the actual PageRank as part of its algorithm.

Bloglines Subscribers
Bloglines displays the amount of subscribers each blog has to its feed(s). Subscriber ranges were determined and each range was assigned a number that was used as part of the algorithm.

Technorati Ranking
Technorati ranking relates the authority of a particular blog (via the number of sites pointing to it). The more link sources referencing your blog, the higher the Technorati ranking. Similar to the Bloglines Subscribers value, and each range was assigned a number that was used as part of the algorithm.

Content/Frequency/Comments
Scores with strict criteria were assigned to content focus, frequency of posts and number of comments. The combined score was used as part of the algorithm.

Alexa Ranking
Alexa ranks web pages based on usage of millions of people. It is a combined measure of page views and users (reach). As a first step, Alexa computes the reach and number of page views for all sites on the Web on a daily basis. The main Alexa traffic rank is based on the geometric mean of these two quantities averaged over time (so that the rank of a site reflects both the number of users who visit that site as well as the number of pages on the site viewed by those users). Ranks closer to 0 have been assigned a high number that was used as part of the algorithm.

b) Multi-Format

Facebook Ranking
As a multi-format tool, Facebook was selected to identify a persons influence/popularity. Other formats such as MySpace could be used at a latter date. The number of friends was determined and each range was assigned a number that was used as part of the algorithm.

c) Mini-Updates

Twitter Friends and Followers Ranking
As a multi-format tool, Twitter was selected to identify a persons influence/popularity. Other formats such as Pownce could be used at a latter date. The number of friends and followers were combined to determine a total friends and followers score. Each range was assigned a number that was used as part of the algorithm.

Twitter Updates Ranking
The number of twitter updates was determined and each range was assigned a number which was combined with the friends and followers score to give a total that was used as part of the algorithm.

d) Business Cards

LinkedIn Ranking
For Business Cards, LinkedIn was selected to identify a persons influence/popularity. Other formats such as Plaxo could be used at a latter date. The number of connections was determined and each range was assigned a number that was used as part of the algorithm.

e) Visual

Flickr Ranking
For visual tools, Flickr was selected to identify a persons influence/popularity. Other formats such as YouTube could be used at a latter date. The number of pictures about uploaded about an individual or about that person was identified and assigned a number. Each range was assigned a number that was used as part of the algorithm.

f) Favourites

Digg Score
The number of Digg’s an individual has had was identified and assigned a number. Each range was assigned a number that was used as part of the algorithm.

Del.icio.us Own Library
The number of pages in an individuals own del.icio.us library was identified and assigned a number. Each range was assigned a number that was used as part of the algorithm.

Del.icio.us Others Library
The number of pages of an individuals own postings that have been saved in other users’ del.icio.us library was identified and assigned a number. Each range was assigned a number that was used as part of the algorithm.

Moving forward other tools such as Reddit will be included to make the scoring more complete.

g) Flexible weighting

Each specific social media outlet listed above was given a standard score out of 10. Using a flexible weighting scale it is possible to vary the importance of the different tools available and consequently establish different total scores of individuals web influence. For the table listed above the following weightings were used:

Blogs 30%
Multi-Format (e.g. Facebook) 20%
Mini-Updates (e.g. Twitter) 25%
Business Cards (e.g. LinkedIn) 7%
Visual (e.g. Flickr) 3%
Favourites (e.g. Digg, del.icio.us) 15%

The weighting scale listed above was created through discussions with several key new media gurus. I do not anticipate this weighting scale to be the final standard and welcome everyone’s views as to what this should be.
Future copies of the Social Media Index will allow you to assign your own subjective weightings to the index to establish your own score.

Critique

There are inevitably going to be errors in the above table. Some people will be missing, some will have inaccurate values assigned to them. All these mistakes are my own – please email me with any queries and I will endeavour to correct them. However, I have also listed below some of the my personal critiques of this table and my reasoning why I have have made certain decisions:

Question: Why did you select the certain social media tools over others?
Answer: I analysed the tools that the most respected online influencers currently used. I do not doubt that the fickle nature of the Internet will mean that in a year’s time there will be a new product that everyone will be using. At that stage, I will update the methodology to incorporate this. It is fully intended that this product remains a flexible tool that can be easily adapted.

Question: Why is my score so low?
Answer: To analyse an individual, data from several different sources must be analysed – many of which are hard to find. In my opinion if someone is determined to publicise their views over different social media tools, it must not be too difficult to find the data for these people. A great example of someone doing this well is: Pop! PR Jots

Question: Why did you use Bloglines – surely Feedburner gives a more accurate figure?
Answer: Bloglines is a great tool as it allows anyone to calculate the number of feeds over multiple formats. Whereas Feedburner gives an individuals score (if you own a blog) it will not provide this data to anyone. Consistency was deemed more appropriate in this instance

Question: What tools didn’t make the cut?
Answer: Wikipedia was the hardest tool to remove from the list. Many people use this religiously and contribute many pages to it – however, identifying these people is exceptionally difficult. Looking ahead I anticipate virtual worlds like Second Life to have a far more prominent place.

Question: Why can’t I just continue to look at blogs – there’s no money in social media tools?
Answer: According to several people there are. Recommended reading:

Social Media Drives Procurement – Knowledge for the digital economy
ITtoolbox/PJA Social Media Index – IT Toolbox
Is the A list dead? Is blogging dying? – Robert Scoble
The time of the a-list is dead. thank christ. not a moment too soon. – Hugh Macleod
Review of the new influencers – Drew B’s take on tech PR
The problem with measurement – BuzzMachine by Jeff Jarvis

Technorati Tags: edelman, edelman’s social media index, social media index, top influencers, a list, blogger relations, analyst relations, technobabble 2.0, new media, blogging

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15 Responses to “Social media index”

  1. Nice work Johnny. Lots to take in, but should prove a great debate. It would be interesting to see how the likes of Podcasts/Videocasts would skew the results – might bump a few people like Shel up the list…

    Also thought Dennis Howlets comment on David Brains post on this subject was interesting i.e. how can we get a measure for the influence of the audiences these influencers are influencing… that would be an interesting methodology to work out – perhaps something you could knock up tomorrow?

  2. 2 h

    interesting concept – good luck with it

  3. Hi, i’m working on a project and I was wondering if you had any sources that I could use to very the statement you made (Quoted below):

    “What is becoming increasingly clear is that the more engaged an individual is within the different channels available, the broader influence that person has.”

    Thanks in advance. Great posting!

    ~Papa

  4. 4 chiginya

    Hey, great staff. Would love to receive the Social Media Index widget. Look forward to getting it

    Cheers


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  6. 6 Stalkk.ed Bookmarks del 9 Agosto 2007 [del.icio.us] | Stalkk.ed
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