Social Uses and Gratifications

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Default-person Joe Wasserman (Author)



Tagged by Joe Wasserman 3 months ago

media consumption* 

Tagged by Joe Wasserman 3 months ago


Tagged by Joe Wasserman 3 months ago

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#### This model is part of this interactive essay on integrating classic media theory with agent based modeling:


This model is an exploration of the suitability of a classic media theory, uses and gratifications (U&G), for agent-based modeling as a demonstration of the promise and challenges of moving from traditional approaches in communication and media studies to complexity science and agent-based modeling.

#### What is uses and gratifications theory?

U&G is “concerned with: (1) the social and psychological origins of (2) needs, which generate (3) expectations of (4) the mass media or other sources, which lead to (5) differential patterns of media exposure (or engagement in other activities), resulting in (6) need gratifications and (7) other consequences, perhaps mostly unintended ones” (Katz et al., 1973, p. 510). The majority of U&G research has focused on (a) typologies of media gratifications, or the expected need satisfaction to be experienced via media usage, and (b) the relationship between those expectations and differential patterns of media consumption (Rubin, 2009). U&G research has also addressed social, psychological, and contextual influences on media consumption, as well as how media usage motivations influence media effects (Rubin, 2009). However, nearly all U&G research has neglected the reciprocal manner in which the consequences of media usage might reciprocally influence individuals’ attitudes, behaviors, and social relationships—and thereby the very social and environmental contexts that are thought to produce needs in the first place. U&G theory and research has not been immune to critique (for a review, see Ruggiero, 2000), including that it is overly individualistic. While this critique may be fair for the majority of U&G research, U&G theorizing includes supra-individual elements. Thus, individual-focused U&G research has not fully captured all of the dimensions of U&G theory.

Rosengren’s (1974) formulation of the U&G paradigm provides one of the most nuanced treatments of U&G theory that includes supra-individual elements. For the most part, advances in U&G have involved more precisely specifying the nature of and relationships among theoretical constructs, rather than substantially altering the proposed theory (see Rubin, 2009). It specified a process in which individuals’ needs produce perceived problems and solutions to those problems, which in turn produce motives for consuming media and engaging other behaviors. These motives drive media consumption that yield gratifications (or non-gratifications) related to prior needs. Media consumption, other behavior, and gratification/non-gratification in turn influence individuals’ characteristics as well as social structures. Crucially, social structures and individual characteristics were specified as influencing all of these processes, thus incorporating feedback loops and scale-crossing phenomena into the model.


### Defining the Agents and Their Properties

**The basic unit of U&G is an individual human. In this ABM, each individual is represented as a square.** U&G proposes a large variety of properties an individual could have. Only individuals’ characteristics that are either (a) relevant to the ABM or (b) outcomes of interest should be included in the model. Drawing directly from Rosengren (1974), some candidate characteristics include needs, perceived problems, perceived solutions to problems, motivations for action, history of past media usage and other behavior, and the gratification or non-gratification associated with those past behaviors. In this model, individuals only have one property: their current level of social need, which is modeled as a continuous variable that can range from zero to one. Gratifications related to social interaction have been one of the most commonly-identified in gratification typologies (Sundar & Limperos, 2013). Indeed, the need for social relatedness has been identified as a basic, universal psychological need (Deci & Ryan, 2000). **Instead of explicitly modeling the antecedents of needs in this model, origins of needs are modeled as a random variable such that at each time point, or tick, individuals’ social need increases between zero and a maximum value set by the “need-increase-max” slider.**

### Defining Agents’ Behaviors

Agent behaviors can be fully deterministic or involve randomness. Important agent behaviors to include in an ABM of U&G include at minimum the selection of media to consume. Drawing directly from Rosengren (1974), media consumption behavior can be characterized in terms of time spent and types of media consumed. In this simple model, distinctions are not made among different kinds of media. Instead, **agents have only two possible behaviors: either consume media or interact socially.** Thus, this model includes both media consumption and non-media behavior that are relevant to gratifying a single need (see Rosengren, 1974). **The ability of media consumption to gratify individuals’ social needs is determined by the “media-social-grat” slider, which can range from 0 to 1.** Similarly, **the ability of social interaction to gratify individuals’ social needs is influenced by the “ix-social-grat” slider, which ranges from .01 to 1.** The rules determining how agents choose whether to consume media or interact socially is described below in “Defining Interactions and Cognition.”

### Defining Agents’ Environments

Environments in an ABM take spatial or network forms. In this model, **agents’ environment is spatial: their four immediately adjacent orthogonal neighbors.** So that all agents have the same number of neighbors, the top and bottom edges of the world wrap to connect to each other, as do the left and right edges. In this way, agents’ environments are each other. Collectively, this grid of agents could be considered a simulated microcosmic society.

### Defining Interactions and Cognition

Like agent behaviors, interactions can be deterministic or include randomness. Agents’ interactions can occur within themselves, with their environment, and/or with other agents. Using “cognition” to describe what agents in an ABM do can be metaphorical, as it describes processes that are not necessarily mental. Cognition simply refers to the manner in which agents decide what actions to perform. Aside from potentially selecting media to consume from their environments, how individuals interact with each other and how they interact with their environments are the most underspecified part of Rosengren’s (1974) model of U&G. In this ABM, **agent cognition is primarily concerned with the decision whether to consume media or socially interact on a given tick. To make this decision, agents compare (a) the amount of social need gratification they would obtain by consuming media to (b) the amount of social need gratification they expect to obtain by interacting socially assuming that none of their neighbors decide to consume media.** Because the latter is not always an accurate assumption, agents sometimes overestimate the amount of social need gratification they will obtain via social interaction.

After making this decision, **agents who decided to consume media reduce their social need by the value determined by the “media-social-grat” slider.** In this ABM, agents interact with each other when they choose to socially interact. **The amount by which social interaction reduces an agent’s social need is determined by a combination of their four neighbors’ availability and the “ix-social-grat” slider. Agents’ availability for those consuming media is 0, and for all others is equal to their current social need, reflecting an assumption that those with greater social needs will be willing to devote more time to social interaction. Gratification obtained via social interaction is the sum of neighbors’ availability times the value of the “ix-social-grat” slider.**


The specifics of both micro and macro behaviors in this ABM depend on the values of model parameters set by sliders. Generally, as both “need-increase-max” and “ix-social-grat” increase, agents are more likely to decide to socially interact over consuming media. As “media-social-grat” increases, agents are more likely decide to consume media instead. At most parameters, agents’ social need and the decision whether to consume media or socially interact are fairly homogeneous over time. At low values of “media-social-grat” and “ix-social-grat” and moderate values of “need-increase-max,” however, a checkerboard pattern emerges in which every other agent maintains a consistently high (or low) level of social need. In this arrangement, those with consistently low social need almost always decide to socially interact, while their neighbors with consistently high social need usually socially interact but sometimes decide to consume media. This checkerboard pattern emerges from agents’ initially random social need, as the neighbors of those with high social need are able to gratify all (or almost all) of their initial social need—thus “starving” their own neighbors of social gratifications due to subsequent low availability. This emergent pattern could be validated against observations of groups’ social interaction dynamics.

### Information displays and reporters

This ABM includes several quantitative visualizations. Depending on the selection in the “Patch-display” menu, individual agents visually display either (a) their current social need, (b) their average social need over the duration of the simulation, or (c) the consistency of their decision to either consume media or socially interact. Furthermore, three graphs depict (a) the number of agents who decided to socially interact or consume media on each tick, (b) the mean and standard deviation of all agents’ social need at each tick, and (c) the mean and standard deviation of all agents’ variability in their decision to socially interact or consume media over the duration of the simulation.


If you mention this model in a publication, we ask that you include the citation below.

For the model itself:

Wasserman, Joe A. (2018). NetLogo Social Uses and Gratifications model [version 1.0]. Retrieved from

If you are interested in extending this model and/or co-authoring a paper on it, please contact Joe A. Wasserman through his website,


Copyright 2018 Joe A. Wasserman.

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This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. To view a copy of this license, visit or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

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Click to Run Model

  sneed  ;; magnitude of current social need (0.00–1.00)
  run-tot-sneed ;; running total of social need to calculate average sneed for viz
  avail  ;; availability for social interaction (0.00-1.00, currently equal to sneed)
  total-soc ;; total of neighbors' availability * ix-social-grat
  social-interact? ;; T/F flag for whether a patch decides to consume media () or interact socially ()
  cnt-media ;; number of ticks on which patch decided to consume media
  cnt-soc ;; number of ticks on which patch decided to interact socially
  H ;; binary entropy function as a measure of consistency/variability in choice: higher H = more variable

to setup
  ask patches
  [ set sneed random-float 1
    set run-tot-sneed sneed
    set avail sneed
    set cnt-media 0
    set cnt-soc 0
    set H 0

to go
  ;; increase social need
  ;; decide whether to consume media or socially interact
  ;; obtain social gratification
  ;; calculate entropy as a measure of variability of selection
  ;; recolor patches for visualization
  ;; advance time

to recolor-patch  ;; patch procedure: visualize depending on selected visualization
  ask patches

  [ if Patch-display = "Current-need" ;; visualize current social need: white = least, black = most
    [ set pcolor scale-color red sneed 1 0 ]

    if Patch-display = "Choice-consistency" ;; visualize consistency of choice between media and social interaction over all ticks:
    [ ifelse cnt-media > cnt-soc            ;; white = 50:50 split, darker = most consistent, blue = favoring media, green = favoring social interaction
      [ set pcolor scale-color blue (H + .1) 0 1]
      [ set pcolor scale-color green (H + .1) 0 1]
      if cnt-media = cnt-soc
      [ set pcolor white ] ]

    if Patch-display = "Average-need" ;; visualize average social need over all ticks
    [ set pcolor scale-color red (run-tot-sneed / (ticks + 2)) 1 0 ]

to increase-need ;; patch procedure: increase social need (sneed) up to a maximum of 1
  ask patches
  [ set sneed min list (sneed + random-float need-increase-max) 1 ;; random increase 0-(need-increase-max) (slider)
    set avail sneed ;; set availability for social interaction with neighbors equal to current social need

to decide-social ;; patch procedure: decide whether to consume media or socially interact based on comparison of media to neighbors
  ask patches
  [ set total-soc min list ((sum [avail] of neighbors4) * ix-social-grat) 1 ;; calculate total social gratification of interacting with 4 neighbors assuming none consume media
    ifelse total-soc > media-social-grat ;; compare social gratification of neighbors to media social gratification
    [ set social-interact? 1 ]
    [ set social-interact? 0 ]
    ifelse social-interact? = 0 ;; if not socially interacting but consuming media, set availability for social interaction to 0
    [ set avail 0
      set cnt-media (cnt-media + 1) ]
    [ set cnt-soc (cnt-soc + 1) ]

to social-grat ;; obtain social gratification (social need reduction) via social interaction or media consumption depending on choice in decide-social
  ask patches
  [ ifelse social-interact? = 1
    [ set sneed (sneed - ((sum [avail] of neighbors4) * ix-social-grat)) ]
    [ set sneed (sneed - media-social-grat) ]
    set sneed max list 0 sneed
    set run-tot-sneed (run-tot-sneed + sneed)

to calc-H ;; binary entropy function as a measure of consistency/variability in choice: higher H = more variable
  ask patches
  [ let sum-cnt (cnt-soc + cnt-media)
    let p ( (max list cnt-soc cnt-media) / sum-cnt)
    ifelse p = 1
    [ set H 0 ]
    [ set H ( ( (-1 * p) * (log p 2) * p) - ((1 - p) * log (1 - p) 2)) ]

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