Three phases of social media network success for marketing

The social media landscape is complex.  Social media network analysis makes it easier to understand and navigate social media.

Using the NodeXL social media network analysis add-in for Excel from the Social Media Research Foundation, I have made a large collection of network visualizations and reports, many of which can be seen in the NodeXL Graph Gallery.

Now that I have seen many social media network maps I observe that marketers are often interested in building “broadcast” network patterns.  This is one of the six basic social media network patterns documented in the recent Pew Research Internet Project report about Mapping Twitter Topic Networks.

There are at least three phases of possible success for a social media marketing effort: phase 1, you get an audience of people who will retweet what you post.  Phase 2, some of your audience gets its own audience for the content they repost from you.  Phase 3, a dense web of relationships emerges, a community of relationships.  This is a desirable phase because it sustains the conversation event when new messages from the brand account are not created.

20141018-Three pahses of social media network success

Data Bank or Data Pimp: choosing the future of social media repositories

The Key Bank Vault door or http://www.flickr.com/photos/cambodia4kidsorg/2274922356/?

Are social media sites data banks, secure repositories of personal assets, or data pimps, soliciting intimate exposure for profit?

I think these services need to choose.  I notice that the setting for who can see what in various systems is in flux.  I can set something to private today and may have to reset it keep it private later.

When I upload content to a site, shouldn’t the expectation be that the deposit is governed by the terms at the time of the contribution?  Why should terms change after I upload?  At least, shouldn’t new rules apply only to new content or content explicitly that has had permissions altered.

Banks do lend out the money I provide them, but only in an anonymous way.  No one knows my dollars are in their mortgage or car loan.  Only legally authorized entities can see my banking records (or so I hope).

Data pimps seem to want to give away anything I give up.  They sell my data as quickly and for as much as possible.

Banks have now developed a reputation that does not make them a great contrast for data pimps, but they still try to represent values like security, confidentiality, and reliability.

I have personally assumed that all data I upload is public.  Only my pictures of my kids have been made “private” and I would not be surprised if those pictures ultimately become public.

Photo credit: cambodia4kidsorg

Bernie Hogan’s Facebook Social Network Data Provider and Visualization toolkit

My colleague at the Oxford Internet Institute, Bernie Hogan, is working on tools that collect personal Facebook network data and visualize the connections among your friends.  These tools now interoperate with NodeXL through the GraphML XML file format. Here is the new link: http://namegen.oii.ox.ac.uk/fb/downloadNet.php?type=graphml

Here is an example: http://twitpic.com/9rvfq

2009 - September - Bernie Hogan - Facebook Network Visualization

It provides a good illustration of the ways a person’s social network is clumped into clusters built around life phases, workplaces, educational institutions, teams and locations.  As people move through more of these stages of life during the Facebook era (and often before) they accumulate these clusters.

Facebook or other contact and friend management systems might could leverage this clustering to organize the presentation of contact information streams.

Bernie recently announced on the SOCNET list that he has updated his script for downloading your Facebook network.

“Features:

1. Its faster. (Presently orders of magnitude faster than Nexus, Touchgraph or ORA).
2. It gives nice feedback during the download.
3. It has less bugs!
4. It gives you the output as a file you can right-click and save rather than copy-paste.
5. IDs are names.”

Bernie writes that phase two of his project is underway.

Bernie is planning a demo at the Sunbelt social network analysis conference in Italy in 2010.

Bernie is the author of the Facebook chapter in our forthcoming book Analyzing Social Media Networks with NodeXL: Insights from a connected world available from Morgan-Kaufmann in July 2010.

Social Networks in the News at NYT

My colleague Scott Sargent at Telligent notes that there are two sections of the March 29th Sunday New York Times feature articles illustrated with network graphs.  The Business section runs an article “Is Facebook Growing Up Too Fast? (http://www.nytimes.com/2009/03/29/technology/internet/29face.html) and the Style Section has an article on The Celebrity Twitter Ecosystem.

20090329 NYT Facebook Ego Networks

20090329 NYT Facebook Ego Networks

My colleague Prof. Ben Shniederman is positively impressed by these images.  He writes, “Notice how the node layout remains stable as edges are removed, so by the 4th figure the edges can all be followed easily….”.  This is one of the themes he highlights in his paper and presentations about problems and improvements in network graph drawing (see: http://www.cs.umd.edu/hcil/nvss/and in particular http://www.cs.umd.edu/hcil/pubs/presentations/NVSS-3.ppt). Prof. Shniederman’s  5th edition of Designing the User Interface is now available with two full chapters on the website with wordles to open each chapter.

A somewhat related article ran the same day in the Style section on The Celebrity Twitter Ecosystem (http://www.nytimes.com/2009/03/29/fashion/29twitter.html). This image focused on the linkages between well known people using Twitter and, by extension, revealing who they follow and who follows them in the social network.

2009 -03- 29 - NYT - Twitter Ecosystem

In the first image no names are associated with the nodes, in the second the names are the major point of the diagram.

The practice of “anonymization” of network graphs may be moot in light of a recent publication mentioned on the Social Network Analysis email list (SOCNET) by Mark Round from QinetiQ of a paper:

Deanonymizing Social Networks – Arvind Narayanan & Vitaly Shmatikov
(http://33bits.org/2009/03/19/deanonymizing-social-networks/)

which suggests that just publishing the unique pattern of links around an individual is sufficient to identify them in an otherwise anonymized data base.

Abstract:
Operators of online social networks are increasingly sharing
potentially sensitive information about users and their relationships
with advertisers, application developers, and data-mining researchers.
Privacy is typically protected by anonymization, i.e., removing names,
addresses, etc.
We present a framework for analyzing privacy and anonymity in social
networks and develop a new re-identification algorithm targeting
anonymized social-network graphs. To demonstrate its effectiveness on
real-world networks, we show that a third of the users who can be
verified to have accounts on both Twitter, a popular microblogging
service, and Flickr, an online photo-sharing site, can be re-identified
in the anonymous Twitter graph with only a 12% error rate.
Our de-anonymization algorithm is based
purely on the network topology, does not require creation of a large
number of dummy “sybil” nodes, is robust to noise and all existing
defenses, and works even when the overlap between the target network
and the adversary’s auxiliary information is small.