Social media as an informational force multiplier that favours individual informational actors

Research Question:

Do individual actors get a relatively larger benefit from social media tools compared to group actors and large institutional actors, and what are the interdependencies and interactions involved?

Abstract (Draft): We are interested in the hypothesis that social media systems like Twitter are informational force multipliers for interpersonal and social conflict. We further hypothesise that there is a significant damping or mitigating effect for this outcome for large groups and institutions versus motivated individuals. In other words, we hypothesise that the net increase in informational power for an individual is far greater relative to their effort than it is for a group actor or agent. Moreover that, dependent upon certain actor-intrinsic factors, for an individual actor the benefit is sustained for longer or has greater scope/extent relative to effort applied or energy put into the social media network as a communication and information broadcast system. The degree to which this outcome (dependent variable) occurs and is sustained is likely dependent on a set of other significant factors including, but not limited to actor intelligence, actor emotional intelligence, actor education level, actor technological capability and competence, and the access to technology and network infrastructure that is available to the individual informational agent/actor.

Definitions (Draft):

Informational Force:

The ability of any particular item, or else quantity, of information (including semantic information), quantified using any formal method (including Shannon’s theory, infonic situation theory, control systems theory, and general systems theory) to effect change in a system to which it is causally input (via transmission and signalling, for example).

Pre-theoretically:

IF = (quantity of information) x (net change in system dynamics and behaviour).

There is a qualitative variant that takes into account the relative potency of discrete items of information. Two different infons (items of information) might have the same quantitative measure of, say, Shannon entropy, but not have equivalent effects in terms of change on the dynamics of a given system.

QIF = Qx(quantity of information) x (net change in system dynamics and behaviour).

Where Q is the qualitative and contextual potency co-efficient.

Informational Actor/Agent:

An individual or group that uses information to effect changes in a system. The system in question could be any human system – including, but not limited to, environmental and ecological systems, the human body and other biological systems, political systems, social systems, administrative and government systems, communication systems, command and control systems, and almost any kind of communication system. In other words, any complex causal system with complex dynamics, processes, and structure, including stochastic and non-linear dynamics.

Informational Force Multiplier:

Any device, tool, system, or process that multiplies the effect of the energy and informational force input by an agent or other process or system.

Method (Draft):

A suite of factorial ANOVA and/or regression analysis full experiments. An additional naturalistic study involving the mining of social media data is likely to be necessary for increasing the overall power of the experiment suite and to provide further insight into interactions between independent variables.

---