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Model 1 - Spread of content in a network

by Noam Drory (Submitted: 08/09/2017)

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This model studies how a content is spread in a network with regard to its uniqueness and offensiveness. once the initial parameters were set, the centered member of the graph starts spreading the content (if he isn't indifferent to it) to one of his neighbors , the neighbor then spreads it to one of his neighbors, and so on. If the agent has either a positive or negative opinion on the content, his or hers opinion will be taken to account (meaning, it will be counted).
Thus we can simulate content spreading and the reactions of the network to it.


After initial parameters, namely, the offensiveness and the uniqueness of the content are set, the network can be covered by agents that holds one of 4 opinions regarding the content - Indifferent, Distributer, Positive or Negative (correlates to the colors white, blue, green and red respectively). the percentage of each opinion is in correlation with the offensiveness factor according to model of "Social conception of content" (a full credit will appear in next sections). in addition to the offensiveness factor, the indifference of the agents is also correlated with the uniqueness factor by the function P(indifferent)=0.012U+0.01 (this function was made up and could be revisited) - the function's purpose is to define a more subtle connection between uniqueness of content and the probability of an agent being indifferent to it (even "boring" content isn't totally ignored sometimes).

Once the graph was setup, we can run the model. on the first tick, the centered agent (in case he isn't indifferent) is choosing one of 4 or 8 (depends on the user's choice) neighbors and spreads the content to him/her. if the agent isn't indifferent he then will also choose a neighbor to spread the content to, and so will the other neighbors. if the agent is just a distributer, the content will simply spread, if, the agent has a positive or negative opinion on the content, his/hers opinion will be counted.

Once the content stopped spreading, we could see how the network responded to it by the Positive/Negative counters.


First, one must define the initial parameters - offensiveness, uniqueness and eight-mode.
Once defined, the user can hit "Setup" to create the initial graph of the network. at all times of the simulation, the user can manually turn agents which didn't receive the content to indifferent using the "block-rumor-with-mouse" button, or spread the content to specific agents using the "spread-rumor-with-mouse" button.

Each non-indifferent agent that hears the rumor turns black on the graph (looks like he disappeared), and each agent that didn't receive the content remains with its color. indifferent agents stays white all along.

The simulation never stops, but the progress is visualized both on the Network-graph with the agents and on the grapth describing the rumor spread as a function of time. Monitors that shows the number of agents that already recieved the content, their precentage, and the number of positive and negative responses appear as well.

EIGHT-MODE? is a switch that determines whether at each time step the rumor spreads to one of four randomly chosen neighbors, or one of eight such neighbors.


Here are some suggestions for ways to extend the model.

1. A more scholarred functions can desribe the ration between offensiveness and uniqueness and the precentage of each opinion.
2. A stability factor could be added to the system the stops the simulation.
3. Probabilities could be assigned to each tick of the simulation depending on the opinion of the agent.
4. An idea of the RumorMill model that could be taken into account here: Can you make a rumor model that does not spread the rumor through spatial proximity?


Note the use of the `neighbors` and `neighbors4` primitives to implement 8-mode and 4-mode, respectively.


RumorMill by Uri Wilensk (Wilensky, U. (1997). NetLogo Rumor Mill model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.)

Social conception of content by Noam Drory

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