Social Influence vs. Selection

First, a couple of definitions:
* Selection is a person's characteristics (mutable or immutable) that drive link (friendship) creation.
* Social Influence is the propensity of a person's friendships to drive characteristics.

The first, for example, would be an ethnic group finding a neighborhood where members of the same group live. The second would be how the discovery of a new music act by one friend drives the adoption of the same act by their friends.

I came across some research that looks at selection vs social influence in the context of page editors of wikipedia. The question posed is, how does friendship influence the pages editors work on? How Crandall et al approached this was to look at the similarity (ie the number of common articles they edited) between two editors pre- and post- meeting.

The following graph is an aggregate/ average of many pairs of editors, but the surprising conclusion is the positive-linear nature of similarity and the ramp up/down surrounding when the meet. Of course, there's a level of historical retrospective going on here (looking at behaviors of people that met in the past), but it's interesting to see the build up pre-meeting (selection) and the continued ramp post-meeting (social influence).


Here's the full presentation from "Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining":

Feedback Effects Between Similarity And Social Influence In Online Communities

View more presentations from Paolo Massa.

Representations of Social Networks

In my study of social networks, I keep asking myself why they are commonly represented so simply? The concept of a "graph" is simple enough, and many of the natural extensions I'd like to see never seem to come up.

An artificial graph, below, contains 3 nodes (people), 2 edges (friendships), and 1 "non friendship).



Let's assume you're trying to assess triadic closure (the propensity for B-C to become friends if A-B and A-C are friends). What would be helpful for this graph would be:
1. The nature of the edge between A-B and A-C.
- Are the edges representing true friendships?
- Are the edges actually a blend of two types of edges (professional/ affiliate and personal)? "A" may be great personal friends with "B", but belong to the same club as "C". This is unlikely to drive triadic closure.
2. The weight of the edge between A-B and A-C. Something that has always made me uncomfortable is the qualitative nature of how edges are described. Perhaps because this is, historically, difficult to quantify. Even so, a 1= acquaintence, 2=best friend would add a more comfortable quantitative layer.
3. The nature of the edges between pairs are unidirectional, when stated/ perceived relationships by the individuals within the pairs may not be reciprocal. I'd like to see every edge actually be made up of 2 edges: one for the nature perceived by each node.

Lastly, flying in the face of the triadic closure concept, I'd like to see ties with a weight ranging from -1 (avoidance) to +1 (closeness); 0 would represent non-friendship/ non-tie. If A-B are friends and B-C are friends, and A is a drug dealer, and C is a recovering addict, triadic closure likely won't result here.

I'd think adding these details would provide a more nuanced analysis. Perhaps as I dig a bit deeper, these practices will surface.

More on contagions...

Just summarizing a great paper that combines social network data and co-location data: "Distinguishing between Drivers of Social Contagion: Insights from Combining Social Network and Co-location Data".

The field now seems ready to move from investigating whether contagion is really at work to why it occurs (Aral 2011; Godes 2011; Iyengar et al. 2011b).

Social contagion may occur for at least five reasons:
1. The process may operate through spreading awareness and interest,
2. Through social learning about the new product’s risks and benefits,
3. Through social-normative influence increasing the legitimacy of the new product,
4. Through concerns that not adopting may result in a competitive or status disadvantage, or
5. Through direct and indirect “network” or installed base effects (Van den Bulte and Lilien 2001).

Who is prone to influence: "Physicians who perceive themselves to be opinion leaders are less sensitive to peer behavior whereas true sociometric leaders are not. This finding indicates that self-confidence rather than true expertise moderates sensitivity to contagion, which is consistent with risk reduction as well as status maintenance mechanisms but not with awareness (e.g., Berger and Heath 2008; Van den Bulte and Stremersch 2004)."

Product-type-specific influence: "what drives contagion is to consider characteristics of the product, and possibly also the influencers. For instance, for products that do not benefit from standard marketing communication and present little perceived risk, contagion may foster adoption by operating at the awareness stage. In such cases, occasional users may be more effective in creating additional awareness than regular users. This is because the latter are more likely to be connected to other regular users and others who are already aware of the product, as noted in a study by Godes and Mayzlin (2009) of stimulated word of mouth for a restaurant chain"

Paper conclusion: "spatial structure overlaps little with network structure, which is why contagion from co-located peers can provide information over and above what can be gleaned from contagion from network peers"

Information-type-specific influence: "Some information and knowledge is quite complex and possibly even tacit. It is hard to convey through “lean” channels such as written documents and presentations at conferences by high-profile speakers, and typically requires “richer” channels, esp. face-to-face interaction (Daft and Lengel 1986)"