Research using empirical data to investigate the effects of today’s news

on young citizens:

Effects of Social Media News Recommendations on Media Trustand

Effects of Childhood News Media Habits on Young Adult Participation

Rosanne SchollDownload this presentation at tinyurl.com/datajourn

About Me

• Data analyst with economics background• Classes focus on data methods, mass

communication theory, digital skills acquisition, and critical thinking

• Research focus on effects of news on democratically important outcomes

• AEJMC division head, head of one of School’s 4 areas of study

Media Effects

• Most of what we know about the effects of journalism regards the content of the news– Framing, bias, credibility, frequency

• Two papers today: effects of news delivery– From friends on social media– From parents during your adolescence

NEWS RECOMMENDATIONS ON SOCIAL MEDIA: EFFECTS ON MEDIA TRUST

Study 1: Political Communication Research Group

With Jason Turcotte, Chance York, Jacob Irving, and Ray Pingree

Use own facebook page

Media Trust

• Will we trust media that we get this way?

• Will we seek out more political information when introduced to it via social media?

• Does who the recommendation comes from matter?

Two Step Flow of Communication

Opinion Leadership

• Please answer the following questions about Al Stavitsky, your Facebook friend:

[7 point Likert scale for agree/disagree with the following statements:]

– This person is well informed about politics and current events

– This person is honest about politics and current events

Cues and Motivated Reasoning

• The recommendation of an opinion leader can act as a cue for the quality of the news source

• Do we interpret the political content to make it consistent with opinions about the friend’s politics?

An experiment

• Manipulate whether participant reads a story that has been shared by a social media contact– Or the same story with no social recommendation

cue

Choosing the friend

• Facebook API pulls 100 friends, picks one with recent interactions with the participant

Stimulus

[Example of Social Recommendation Manipulation]

Procedure

• Online experiment with undergraduates receiving extra credit for their participation

• 1) Facebook sign in• 2) Stimulus• 3) Outcome measures• 4) Opinion leader measures

Trust in News Outlet

Model 1

Model 2 b (SE) b (SE)

Adjudication -0.03 (0.06) -0.02 (0.06) Polarization 0.06 (0.10) 0.07 (0.09) Story Context 0.08 (0.10) 0.08 (0.09) Recommendation 0.03 (0.10) -0.84* (0.35) Opinion Leader 0.13*** (0.04) 0.05 (0.05) Recommendation X Opinion Leader --- 0.19** (0.07) Constant 4.09*** (0.19) 4.43*** (0.23) N 364 364 R2 0.038 0.056 Note: ***p<.001, **p<.01, *p<.05

Size of Effect of Social Recommend-ation on Trust in News Outlet

Findings Summary

• When a quality opinion leader recommends an article, we trust the outlet more and look to it more in the future

• When a poor opinion leader recommends an article, we trust the outlet less and look to it less in the future

Implications

• Social media amplifies the two step flow• Political news can be credited or discredited

by perceptions about the person sharing it

Implications

• Social media enriches marketplace of ideas– Get political information from trusted sources

• Social media constricts diverse media content– Most friends similar, plus platforms further filter

dissonant news– Rare cross-cutting voices may be labeled “poor

opinion leaders”

Implications

• More social media reach is not necessarily better

• Many sources, some are more deserving, people are doing something rational to use friends to figure out which to trust

Ongoing

• Gathering more data: currently in the field• Effect of partisanship match between

participant and friend• Effects on other outcomes• Interaction with other manipulations

EFFECTS OF CHILDHOOD NEWS MEDIA HABITS ON YOUNG ADULT PARTICIPATION

Study 2: Funded Research on Multigenerational Panel Data

With Chance York

Socialization

Socialization mechanisms

• Modelling• Defining• Other possibilities:

– Causal schema formation– Status inheritance– Genetic heritability

• “Impressionable years” of adolescence

Youth Voting

Expectations

Data

University of Michigan’s Panel Study of Income Dynamics (PSID)

• 1997 and 2003 PSID main interviews: parent and child demographic characteristics

• 2002 Child Development Supplement: parent and adolescent behavioral and media use variables

• 2009 Transition to Adulthood Supplement: young adult measures

• N = 903 young adult respondents

Modelling Youth Voter Turnout

• Weighted for national representation using CDS weights

• Logistic regression with MLE estimator • Control for demographics, student status,

employment status, children, 2009 media use

  B ZParent Characteristics Education 0.040 2.40* Income -0.001 -1.88† Volunteering 0.007 1.35 Donations 0.043 0.72 Newspaper Use 0.078 2.22* Talk About News with Child 0.206 2.21* Television Use 0.013 0.65 Talk About TV with Child 0.175 0.78Adolescent Media Use Newspaper 1.369 1.19 Television -0.034 -1.23 Internet -0.093 -1.28 Videogames -0.042 -0.84Young Adult Characteristics Female 0.197 0.92 Age 0.069 1.21 Black 1.152 3.63*** Hispanic -0.445 -1.24 Education 0.429 5.08*** Income 0.003 0.36 College Student 0.260 1.09 Employed 0.241 1.14 Children -0.248 -1.73 Read/Watch News 0.160 2.07* Entertainment TV 0.013 0.17Constant -9.477 -6.24***N 903Model Chi-Square 132.97***Pseudo R2 18.29%

† = p<.10, * = p<.05, ** p<.01, *** p<.001 (two-tailed)

Note: This analysis was weighted using PSID weights for national representativeness. A Hosmer and Lemeshow goodness of fit statistic (898.77, p = .32) based on unweighted estimates was not significant, suggesting the logistic regression model fit these data well.

Multivariate Logistic Regression Predicting Young Adult Voter Turnout

Expectations

Implications

• Parents can help train young people to vote, but not via regulating the behavior of teenagers

• Adolescents may be forming civic orientations that lie dormant until early adulthood, even while ignoring news for entertainment TV and video games

THANK YOU!

@Rosanne Scholl or rmscholl@gmail.comDownload this presentation: tinyurl.com/datajourn

Some visuals in this presentation created with draw.io.Photos tagged as licensed for reuse via Google Image search.