Social Contagions and Social Media Marketing Effectiveness

A little background terminology here: things that transmit between nodes in a social network are known as contagions. The most simple real-life contagion example is, of course, diseases, but intuitively we also know that attitudes (eg. product preference) are influenced by who our friends are.

Further, as with disease, in order for contagions to effectively spread, they must be in an appropriate environment. In the case of diseases, not only must the shape of the network be appropriate, but varying degrees of physical contact (it might be as simple as a handshake, or intimate as sexual contact, and genetic predisposition) may be a factor. For product preference, as an example, the degree of susceptibility to a contagion is similarly nuanced. Of course, centrality and number of connections may be large factors, but there are others.

Companies are spending a lot of money promoting their products on Facebook. Estimates have Facebook's advertising revenue at ~$1.86B dollars for 2010 (not inconsequential vs. competitive "portals"). Much of the advertiser interest in advertising on Facebook stems from the belief that "social ads" are more effective than are non-social standard banner ads due to the influence our contacts have over us.



Much of the research by mainstream analysts (1, 2) frame the impact of social media in the form of "influence". This has an implication of a sort of cognitive awareness and formality by consumers; that they make their purchase decisions rationally based on friend's behaviors (eg. "ah, I see Jim has bought , so I will also get one.". Though there may be some contribution by rational decision making processes, I hypothesize that modeling influence as a contagion (eg disease) is a more effective way to measure impact. In other words, you can't control your desire for a product anymore so than you can control your ability to catch a cold.

Google is often criticized for the lack of transparency in their advertising marketplace. Advertisers don't know the publishers, targeting and ad rotation is opaque, and the true price is unclear. Facebook has a similar problem, but it's not as direct or obvious as Google's.

Here's the scenario: An advertiser creates a facebook page and buys ads to promote it. Nested in the ad is a "like" button that, when pressed, acts like other like buttons on the site: for some of your contacts, it inserts an item into their newsfeed. Here's the key problem: Facebook doesn't permit the advertiser visibility into who and where they surface these "likes". For brand advertisers, not all impressions are created equal.
* People are not monolithic influences or non-influencers. My mom might influence cold remedies, but not music taste, for example.

While tastes do signal social identity, what others infer from one’s choice depends upon group membership (Berger and Heath 2007; McCracken 1988; Muniz and O’Guinn 2001). For example, Berger and Heath (2007) find that people
may converge or diverge in their tastes based on how much their choice in a given context signals their social identity. [Do Friends Influence Purchases in a Social Network? Raghuram Iyengar]

* People that I have strong ties with, for some types of products, have already influenced me offline resulting in a wasted cost of an impression. For example, don't bother showing my closest friends that I liked "Against Me's" latest album, we all already have it. That said, if I liked a car brand, it's probably worth showing them my "Like".

With Facebook, as with google, this targeting is done algorithmically. You may get a lot of impressions, and people may say that they're influenced by social media, but are they actually being influenced?

Some further, research from Harvard Business School talks about how relative social standing may paradoxically reduce influence:

Our results show that there are three distinct groups of users with very different behavior.
The low-status group (48% of users) are not well connected, show limited interaction with other members and are unaffected by social pressure. The middle-status group (40% users) is moderately connected, show reasonable non-purchase activity on the site and have a strong and positive effect due to friends’ purchases. In other words, this group exhibits “keeping up with the Joneses” behavior. On average, their revenue increases by 5% due to this social influence. The high-status group (12% users) is well connected and very active on the site, and shows a significant negative effect due to friends’ purchases. In other words, this group differentiates itself from others by lowering their purchase and strongly pursuing non-purchase related activities. This social influence leads to almost 14% drop in the revenue of this group. We discuss the theoretical and managerial implications of our results.

This is consistent with what's known as the middle status conformity thesis. Detailed here (Philips and Zuckerman 2001). Not to put to fine a point on this implication, but if 48% of the population is a "low-class", and these people are not influenced socially, then social advertising to them is ineffective.

Other background reading:
* Impact of Social Influence in E-Commerce Decision Making
* Distinguishing between Drivers of Social Contagion: Insights from Combining Social Network and Co-location Data

"Giant Components" Implies WInner-Takes-All in the Social Network Race

In graph theoretic social networking analysis, there's a concept known as "Giant Components". As the name implies, in any given human social network, there exists one main, extremely large, set of connected "nodes" (people) surrounded by significantly smaller, disconnected from the giant component, peripheral clusters of social networks.

This is illustrated qualitatively in "Networks, Crowds, and Markets" (free version here) by given the example: consider your current friend group, and who they're connected to, and so on. Ultimately, you'll find you're indirected connected to people from other countries. Another way to put it, if everyone has 100 (unique) friends, you very quickly get to large numbers of connected nodes (100 of your friends x (have) 100 friends x (who have) 100 friends x (who have) 100 friends x (who have) 100 friends = 10B people. However, there will be people, isolated on an island somewhere, that is not connected to the giant component.

Random Example (from here). You can see that a high proportion of nodes below to one connected cluster.

If any one person, in any one of the smaller clusters, becomes connected to the "Giant Component", the entire cluster is then considered part of the "Giant Component". So, it's reasonable to assume that, at some point, the desert island person will eventually meet one person in the giant component. It seems, in this connected world, we're almost fatalistically destined to be part of the giant component.

It is inevitable then, that we become part of the Facebook giant component, right? They're nearing 600 millions users, and check out this giant component.

In reality, things aren't as inevitable. It's not obvious initially, but a few things to consider:

• The definition of the edges (connections between people) are a little more nuanced than simply "knowing" someone. What if you, instead of drawing a social graph based on Facebook-stated friendships, you drew it based on spending greater than 10 hours a day together? The graph would become much more fragmented.
• Graphs can be used to represent different classes of social graphs. For example, and Facebook even does this, my family, and my coworkers could be represented as separate graphs. In other words, people are capable of belonging to multiple networks.

Both of these facts create an opportunity for emerging or niche social networks to evolve and grow -- and not necessarily at the expense of Facebook either! In retrospect, Livemocha, an interest-based social network, benefited from this.

Another example (or maybe a 3rd bullet is required above stating "cultural norms") is Mixi, a Japanese social network. Recently featured in the NYTimes, Facebook has been relatively unsuccessful in Japan. Some speculate it is cultural in nature; that the Japanese are more private and that Facebook's religious-like fervor towards unfettered openness doesn't resonate there. Allegedly, on Mixi only 5% of users use their real picture as an avatar.

The "giant component" question seems to simply be one of definition. Existentially, or environmentally, aren't we all connected?

As an aside, I'm taking a Social Media Analysis reading course this semester (similar to this one at Carnegie Melon). I have a weekly blog-writing assignment - this is the first post of many.