Meme Theory
The "Meme Theory" is set of concepts to understand, predict and control how memes travel through an organization or network. The graphs here are a product of this theory. Keywords are a proxy for a meme and measuring the rate-of-change of keyword frequency in a datasource like the Internet is predictive. For instance, if the keyphrase "Adobe Flex" has an increasing rate-of-change, its propagation across the Internet is increasing, which implies a growing demand for that technology. For a deeper understanding of meme manipulation, the Schramm Communication Model is the basis of Meme Theory.
Here is the flea design pattern for memetic manipulation, empirically tested on this website.
Here is the venturi design pattern for memetic manipulation.
Here is a more complicated design pattern for manipulation that I've tested empirically.
Assumptions
♥ A human being has finite capacity (bandwidth) to accept or send information.
♥; The sum of bandwidth for all people is finite
♥ Bandwidth is composed of many channels, slices of bandwidth
♥ Each channel is a proxy for a meme.
♥ Channel width is roughly stable over time, all other things being equal
♥ This bandwidth has quality of service and allocates bandwidth to escalated memes as necessary.
♥ Long-term competition for finite bandwidth creates hardy memes, resistant to displacement.
Measurements
Bandwidth allocation and memetic resistance can be determined through empirical time-series measurements.
Define a set of quantitative measurements:
Strength of correlation between stock prices and word counts.
Impedance - the speed of propagation of a meme.
Sentiment - how a meme affects the population.
Diffraction - how well a meme spawns derivative memes.
Correlation
Correlation of stock prices to Dejanews word counts over a ten-year period.
Impedance
Latency and magnitude are components of cultural impedance. Latency shows how quickly a meme is absorbed by a culture. These examples show that Pope's death is quickly accepted but SARS virus is resisted -
Sentiment
Run two time-series queries such as "company name + cool" and "company name + evil", and two graphs are produced, one of mostly positive comments, the second is mostly negative comments.
The difference of the two graphs, a sentiment map, describes net popular sentiment about the subject over time.
Diffraction
As a new meme appears, it absorbs bandwidth from existing memes. If it spawns new memes, the network allocates even more bandwidth to those and there is a measurable diffraction. Diffraction is directly proportional to cultural acceptance. Conversely, if a meme encounters rejection, it sinks like a stone into an icy, black pool of water, barely raising a ripple. We can measure diffraction by comparing two time-series queries. The first graph is new meme, i.e. "Pope + Death" while the second graph shows the existing meme "Pope", minus the elements of the first series. The second series shows us diffraction ( new "non-Death" Pope-related memes ) such as:
The new pope should be Cardinal Jones
Do you remember the pope's last speech?
This pope was an effective leader
High diffraction shows the new meme triggered re-direction of bandwidth to an associated child meme. Diffraction is the mechanism behind Naomi Klein's Shock Doctrine theory.
By contrast, the SARS meme used no new bandwidth but drew from the existing bandwidth allocated to "VIRUS". Although SARS used bandwidth, the graph for "VIRUS" remains almost constant.
