Social Media andPolitical Agenda Setting

Fabrizio Gilardi1 Theresa Gessler1 Maël Kubli1 Stefan Müller2

1University of Zurich2University College Dublin

CAP Virtual ConferenceJuly 1, 2020

(Updated on June 30, 2020)

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Motivation

≻ Agenda setting matters, a lot≻ Who leads, who follows?

· Politicians, parties, traditional media, the public?· Still unclear; complex relationships· Social media and “hybrid media systems”→ new, additional layerof complexity

≻ Few studies take into account multiple types of actors, traditionaland social media, mutual influences, long periods

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Motivation

≻ Agenda setting matters, a lot

≻ Who leads, who follows?· Politicians, parties, traditional media, the public?· Still unclear; complex relationships· Social media and “hybrid media systems”→ new, additional layerof complexity

≻ Few studies take into account multiple types of actors, traditionaland social media, mutual influences, long periods

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Motivation

≻ Agenda setting matters, a lot≻ Who leads, who follows?

· Politicians, parties, traditional media, the public?· Still unclear; complex relationships· Social media and “hybrid media systems”→ new, additional layerof complexity

≻ Few studies take into account multiple types of actors, traditionaland social media, mutual influences, long periods

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Motivation

≻ Agenda setting matters, a lot≻ Who leads, who follows?

· Politicians, parties, traditional media, the public?· Still unclear; complex relationships· Social media and “hybrid media systems”→ new, additional layerof complexity

≻ Few studies take into account multiple types of actors, traditionaland social media, mutual influences, long periods

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Research question

We examine the connections between three agendas:1. Traditional media agenda2. Social media agenda of parties3. Social media agenda of politicians

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Research question

We examine the connections between three agendas:

1. Traditional media agenda2. Social media agenda of parties3. Social media agenda of politicians

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Research question

We examine the connections between three agendas:1. Traditional media agenda

2. Social media agenda of parties3. Social media agenda of politicians

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Research question

We examine the connections between three agendas:1. Traditional media agenda2. Social media agenda of parties

3. Social media agenda of politicians

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Research question

We examine the connections between three agendas:1. Traditional media agenda2. Social media agenda of parties3. Social media agenda of politicians

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Expectations

Traditional mediaagenda

Social mediaagenda (parties)

Social mediaagenda (candidates)

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Research design

≻ Switzerland, January 2018–December 2019≻ Period includes national elections and multiple referenda≻ 2.78 million articles published in 84 newspapers (full text)≻ 6,500 tweets posted by parties on their official accounts

(excluding retweets)≻ 210,000 tweets posted by politicians on on their own accounts≻ Machine-learning classifiers for four issues: environment, gender,

Europe, immigration≻ Vector autoregression (VAR) models (Barberá et al., 2019),

controlling for press releases of parties and many organizations

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Research design

≻ Switzerland, January 2018–December 2019

≻ Period includes national elections and multiple referenda≻ 2.78 million articles published in 84 newspapers (full text)≻ 6,500 tweets posted by parties on their official accounts

(excluding retweets)≻ 210,000 tweets posted by politicians on on their own accounts≻ Machine-learning classifiers for four issues: environment, gender,

Europe, immigration≻ Vector autoregression (VAR) models (Barberá et al., 2019),

controlling for press releases of parties and many organizations

5 / 9

Research design

≻ Switzerland, January 2018–December 2019≻ Period includes national elections and multiple referenda

≻ 2.78 million articles published in 84 newspapers (full text)≻ 6,500 tweets posted by parties on their official accounts

(excluding retweets)≻ 210,000 tweets posted by politicians on on their own accounts≻ Machine-learning classifiers for four issues: environment, gender,

Europe, immigration≻ Vector autoregression (VAR) models (Barberá et al., 2019),

controlling for press releases of parties and many organizations

5 / 9

Research design

≻ Switzerland, January 2018–December 2019≻ Period includes national elections and multiple referenda≻ 2.78 million articles published in 84 newspapers (full text)

≻ 6,500 tweets posted by parties on their official accounts(excluding retweets)

≻ 210,000 tweets posted by politicians on on their own accounts≻ Machine-learning classifiers for four issues: environment, gender,

Europe, immigration≻ Vector autoregression (VAR) models (Barberá et al., 2019),

controlling for press releases of parties and many organizations

5 / 9

Research design

≻ Switzerland, January 2018–December 2019≻ Period includes national elections and multiple referenda≻ 2.78 million articles published in 84 newspapers (full text)≻ 6,500 tweets posted by parties on their official accounts

(excluding retweets)

≻ 210,000 tweets posted by politicians on on their own accounts≻ Machine-learning classifiers for four issues: environment, gender,

Europe, immigration≻ Vector autoregression (VAR) models (Barberá et al., 2019),

controlling for press releases of parties and many organizations

5 / 9

Research design

≻ Switzerland, January 2018–December 2019≻ Period includes national elections and multiple referenda≻ 2.78 million articles published in 84 newspapers (full text)≻ 6,500 tweets posted by parties on their official accounts

(excluding retweets)≻ 210,000 tweets posted by politicians on on their own accounts

≻ Machine-learning classifiers for four issues: environment, gender,Europe, immigration

≻ Vector autoregression (VAR) models (Barberá et al., 2019),controlling for press releases of parties and many organizations

5 / 9

Research design

≻ Switzerland, January 2018–December 2019≻ Period includes national elections and multiple referenda≻ 2.78 million articles published in 84 newspapers (full text)≻ 6,500 tweets posted by parties on their official accounts

(excluding retweets)≻ 210,000 tweets posted by politicians on on their own accounts≻ Machine-learning classifiers for four issues: environment, gender,

Europe, immigration

≻ Vector autoregression (VAR) models (Barberá et al., 2019),controlling for press releases of parties and many organizations

5 / 9

Research design

≻ Switzerland, January 2018–December 2019≻ Period includes national elections and multiple referenda≻ 2.78 million articles published in 84 newspapers (full text)≻ 6,500 tweets posted by parties on their official accounts

(excluding retweets)≻ 210,000 tweets posted by politicians on on their own accounts≻ Machine-learning classifiers for four issues: environment, gender,

Europe, immigration≻ Vector autoregression (VAR) models (Barberá et al., 2019),

controlling for press releases of parties and many organizations

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Issue emphasis over time

Tweets by Politicians

Tweets by Parties

Newspapers

Jan 2018 Apr 2018 Jul 2018 Oct 2018 Jan 2019 Apr 2019 Jul 2019 Oct 2019 Jan 2020

0

500

1000

1500

0

20

40

0

100

200

300

400

500Num

ber

of

Docu

ments

per

Week

Environment Gender Europe Immigration

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Effect of 10pp increase over next week (VAR)

Newspapers -> Parties

Newspapers -> Politicians

Parties -> Newspapers

Parties -> Politicians

Politicians -> Newspapers

Politicians -> Parties

0 2 4Percentage points

Environment Gender Europe Immigration7 / 9

Net effect of 10pp increase over next week (VAR)

Parties -> Newspapersvs.

Newspapers -> Parties

Politicians -> Newspapersvs.

Newspapers -> Politicians

Politicians -> Partiesvs.

Parties -> Politicians

-2 0 2Percentage points

Environment Gender Europe Immigration8 / 9

Summary

≻ Relationship between three agendas:· Traditional media, parties, politicians

≻ We consider:· Multiple types of actors· Traditional and social media· Mutual influences between the agendas· Long period (two years, including elections and referenda)

≻ Results:· For most issues, the agendas counterbalance each other· Important exception: environment issue (parties→ newspapers)

≻ Next steps:· Facebook· Heterogeneity: across parties, over time

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Summary

≻ Relationship between three agendas:· Traditional media, parties, politicians

≻ We consider:· Multiple types of actors· Traditional and social media· Mutual influences between the agendas· Long period (two years, including elections and referenda)

≻ Results:· For most issues, the agendas counterbalance each other· Important exception: environment issue (parties→ newspapers)

≻ Next steps:· Facebook· Heterogeneity: across parties, over time

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Summary

≻ Relationship between three agendas:· Traditional media, parties, politicians

≻ We consider:· Multiple types of actors· Traditional and social media· Mutual influences between the agendas· Long period (two years, including elections and referenda)

≻ Results:· For most issues, the agendas counterbalance each other· Important exception: environment issue (parties→ newspapers)

≻ Next steps:· Facebook· Heterogeneity: across parties, over time

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Summary

≻ Relationship between three agendas:· Traditional media, parties, politicians

≻ We consider:· Multiple types of actors· Traditional and social media· Mutual influences between the agendas· Long period (two years, including elections and referenda)

≻ Results:· For most issues, the agendas counterbalance each other· Important exception: environment issue (parties→ newspapers)

≻ Next steps:· Facebook· Heterogeneity: across parties, over time

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Summary

≻ Relationship between three agendas:· Traditional media, parties, politicians

≻ We consider:· Multiple types of actors· Traditional and social media· Mutual influences between the agendas· Long period (two years, including elections and referenda)

≻ Results:· For most issues, the agendas counterbalance each other· Important exception: environment issue (parties→ newspapers)

≻ Next steps:· Facebook· Heterogeneity: across parties, over time

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References

Barberá, P., A. Casas, J. Nagler, P. J. Egan, R. Bonneau, J. T. Jost, and J. A. Tucker (2019). Who Leads? WhoFollows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public UsingSocial Media Data. American Political Science Review 113(4), 883–901.

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