5/26/2017

Partisan media

Implicated as potential cause of:

  • Ideological polarization (Levendusky, 2013)
  • Affective polarization (Garrett et al., 2014)
  • Participation (Dilliplane, 2011; Wojcieszak, Bimber, Feldman, & Stroud, 2016)
  • Perceptions of media bias (Arceneaux, Johnson, & Murphy, 2012)
  • Uncivil political discussion (Gervais, 2014)

Selective exposure

  • Selective approach (I choose my side's programming)
    • Seems more prevalent than active avoidance of the other side (Garrett, 2009)
  • But what causes it?
    • Partisanship (Iyengar & Hahn, 2009; Johnson, Zhang, & Bichard, 2011; Stroud, 2008; Wicks, Wicks, & Morimoto, 2014)
    • Other leads: Emotional motivations (e.g., Valentino, Banks, Hutchings, & Davis, 2009)
  • Let's take a closer look at partisanship

Partisanship as a social identity

  • Party affiliation as a group membership rather than symbolic statement of issue positions or rational choice (Huddy, Mason, & Aar√łe, 2015; Iyengar, Sood, & Lelkes, 2012)
  • We can apply social identity theory to partisanship (Green, Palmquist, & Shickler, 2004)

Reinforcing Spirals Model (Slater, 2007; 2014)

  • Media are used to maintain social identities
    • Only use as much media as needed for homeostasis
    • Different identities may require different media sources
  • Threatened identity = more identity-relevant media use
    • Necessary maintenance
    • Threat removed = less identity-relevant media use

Reinforcing Spirals Model (Slater, 2007; 2014)

  • Media use is a response to the environment rather than perpetually reinforcing
    • Motivation is only for maintenance
    • Competing identities and diverse systems prevent positive feedback loops

Identity threat

  • Numerical distinctiveness
    • Being outnumbered raises identity salience and affiliation (Bettencourt, Miller, & Hume, 1999; McGuire, McGuire, Child, & Fujioka, 1978)
  • Threats to value (Branscombe, Ellemers, Spears, & Doosje, 1999)
    • Questioning the merit of the group's practices or morality
  • Generally, stronger identification = more entrenchment in the face of threat

Identity threat in politics

  • Red states and blue states
    • Urban-rural divide
    • E.g., are you outnumbered politically in your local area?
  • Questioning the beliefs and moral status of opposing parties is normatively acceptable
    • E.g., discussions with those who disagree

This study

H1a: Being a member of the losing party locally will be associated with greater partisan media selectivity.

H1b: Discussing politics with members of opposing parties will be associated with greater partisan media selectivity.

H2a: The effect of being a member of the losing party locally will be amplified by stronger partisan identity.

H2b: The effect of discussing politics with members of opposing parties will be amplified by stronger partisan identity.

Methods

  • N = 1000 sample from YouGov
    • 800 general population sample, 200 black oversample
  • Party ID measured on 7-point scale from two questions
  • Do you identify as…Republican, Democrat, or Independent
  • If Independent, asked if lean toward one or other party
  • If one of the parties, asked if "strong" or "not strong"

  • Those who provided no party or party lean were excluded

Measures

  • Used program list technique (Dilliplane, Goldman, & Mutz, 2013) to measure media use
    • 67 sources from TV, radio, and websites
    • Yes or no response to watch/listen to/visit source "regularly"
  • Sources categorized as left-leaning, right-leaning, or non-partisan
    • 5 political comm. researchers rated each source with option to say "don't know"
    • If majority agreed with identification, then it is used
    • Otherwise, used Lexis-Nexis technique described in Dilliplane (2011)

Measures

  • Respondent ZIP code -> county
    • County-level identity threat = average presidential margin of loss/victory in 2008 and 2012 elections
    • Threat = R margin of victory for Ds, D margin of victory for Rs
  • What portion of the people you talk politics with are Republicans, Democrats, members of other parties, and non-party identifiers?
    • Forced sum to 100%
    • Network threat = % out-parties

Models

Linear regression predicting % of in-party partisan sources out of total number

Model 1: H1a, H1b

Model 2: H2a (county by identity interaction)

Model 3: H2b (discussion by identity interaction)

Models

Focal predictors:

  • County-level identity threat (out-party POTUS vote share)
  • Network-level identity threat (% out-party discussants)
  • Strength of political identity constructed measure
    • 0 = nonpartisan, 1 = lean/not strong partisan, 2 = strong partisan
    • 0 = moderate, 1 = liberal/conservative, 2 = very liberal/conservative
    • Very liberal strong Democrat/very conservative strong Republican = 4
    • Moderate not strong partisan = 1

Models

Controls:

  • Party (1 = Republican)
  • Indicator for whether R discusses politics with others
  • Number of people with whom R discusses politics with
  • Whether any media sources used
  • Number of media sources used
  • Age, race (black, Hispanic), gender, education, Christian religion

Results

Model 1

Est. SE p
County-level threat 4.22 2.06 0.04
Network-level threat 1.54 2.39 0.52
Identity strength 10.24 2.27 0.00
Republican 8.52 2.64 0.00
# sources 6.12 2.36 0.01
# discussants 4.54 2.32 0.05

Results

Model 2

Est. SE p
County-level threat 4.55 2.08 0.03
Network-level threat 1.46 2.39 0.54
Identity strength 10.11 2.25 0.00
Republican 8.42 2.63 0.00
# sources 5.81 2.35 0.01
# discussants 4.65 2.23 0.04
County threat x Identity strength 6.88 3.72 0.06

Results

Results

Model 3

Est. SE p
County-level threat 4.13 2.04 0.04
Network-level threat 1.84 2.40 0.44
Identity strength 10.35 2.24 0.00
Republican 8.53 2.63 0.00
# sources 6.02 2.34 0.01
# discussants 4.05 2.28 0.08
Network threat x Identity strength 9.13 4.19 0.03

Results

Recap

H1a (county-level threat): Supported

H1b (network-level threat): Not supported

H2a (county threat by identity strength interaction): Supported

H2b (network threat by identity strength interaction): Supported

Overall:

  • Heterogeneous networks associated with more selectivity
    • Especially for stronger partisans
  • RSM is a theory invested in over-time effects
    • Those aren't measured here