Zurich Open Repository andArchive
University of Zurich
University Library
Strickhofstrasse 39
CH-8057 Zurichwww.zora.uzh.ch
Year: 2022

Social media and political agenda setting
Gilardi, Fabrizio ; Gessler, Theresa ; Kubli, Mael ; Müller, Stefan

DOI: https://doi.org/10.1080/10584609.2021.1910390

Posted at the Zurich Open Repository and Archive, University of Zurich
ZORA URL: https://doi.org/10.5167/uzh-210376
Journal Article
Published Version

The following work is licensed under a Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License.
Originally published at:
Gilardi, Fabrizio; Gessler, Theresa; Kubli, Mael; Müller, Stefan (2022). Social media and political agenda setting.
Political Communication, 39(1):39-60.
DOI: https://doi.org/10.1080/10584609.2021.1910390

POLITICAL COMMUNICATION
https://doi.org/10.1080/10584609.2021.1910390

Social Media and Political Agenda Setting
Fabrizio Gilardia, Theresa Gesslera, Maël Kublia, and Stefan Müller

ba
Department of Political Science, University of Zurich, Zurich, Switzerland; bSchool of Politics and International
Relations, University College Dublin, Dublin, Ireland

ABSTRACTKEYWORDS

What is the role of social media in political agenda setting? Digitalplatforms have reduced the gatekeeping power of traditional media
and, potentially, they have increased the capacity of various kinds ofactors to shape the agenda. We study this question in the Swisscontext by examining the connections between three agendas: thetraditional media agenda, the social media agenda of parties, and thesocial media agenda of politicians. Specifically, we validate and apply
supervised machine learning classifiers to categorize 2.78 million articles published in 84 newspapers, 6,500 tweets posted on official party
accounts, and 210,000 tweets posted by politicians on their own
accounts from January 2018 until December 2019. We first use theclassifier to measure the salience of the four most relevant issues of theperiod: the environment, Europe, gender equality, and immigration.
Then, using a vector autoregression (VAR) approach, we analyze therelationship between the three agendas. Results show that not only do
the traditional media agenda, the social media agenda of parties, andthe social media agenda of politicians influence one another but,overall, no agenda leads the others more than it is led by them.
There is one important exception: for the environment issue, the social
media agenda of parties is more predictive of the traditional media
agenda than vice-versa. These findings underscore how closely different agendas are tied together, but also show that advocacy campaigns
may play an important role in both constraining and enabling parties
to push their specific agendas.

Agenda setting; social media;
newspapers; supervisedmachine learning

Introduction
Influence over the political agenda, and political discourse more broadly, is one of the mostimportant sources of power (Bachrach & Baratz, 1962; Schattschneider, 1960). The fundamental question who gets to set this agenda has been studied from several perspectives,
including media studies, policy studies, and political communication (Wolfe et al., 2013).
A central argument is that of the agenda-setting function of the mass media (McCombs &Shaw, 1972, 1993), which posits that newspapers, television, and radio influence what
political actors think and care about – that is, the content of the political agenda. The
literature has long debated the direction of agenda-setting dynamics, without fully resolving
the question of whether the media agenda shapes the political agenda or vice-versa, and

CONTACT Fabrizio Gilardi
gilardi@ipz.uzh.ch
Department of Political Science, University of Zurich, Affolternstrasse
56, CH-8050 Zurich
Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/10584609.2021.
1910390
Copyright © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://
creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided
the original work is properly cited, and is not altered, transformed, or built upon in any way.

2

F. GILARDI ET AL.

under which conditions (Walgrave & Van Aelst, 2006). We even “know relatively little
about the forces that drive media attention” (Boydstun, 2013, p. 1). Moreover, the internet
and social media have created new “hybrid media systems” that have expanded the number
and types of actors potentially able to shape the political discourse and agenda (Chadwick,
2017; Jungherr et al., 2019). Therefore, the rise of digital platforms brings up the question of
who sets the political agenda in these kinds of environments.
Despite a large and increasing number of studies (Jungherr, 2016), a clear answer
regarding the role of social media in political agenda setting is still missing. Previous
work has studied important questions such as the perception of social media messages(Chen et al., 2019; Feezell, 2018), the effects of exposure to news media on social media
discussions (King et al., 2017), and the interaction between politicians and the public onsocial media (Fazekas et al., 2021). Shapiro and Hemphill (2017) study the congruence
between the New York Times and Congressional Twitter, finding variation across policy
areas. Peeters et al. (2019) compare the issue profile of politicians in parliament, in the
media, and on Twitter, finding a high degree of congruence. The study that is closest to ours
is Barberá et al. (2019), which focuses explicitly on agenda setting dynamics of Twitter andis interested primarily in the connection between the issues discussed by legislators and the
public, finding that legislators are more likely to follow than to lead the agenda. Relatedly,
James et al. (2019) study information flows during election campaigns. They find that
although social media allows the public and parties to share information, traditional
media still maintains a mediating gatekeeper position.
In contrast to these studies, we examine the connections between three agendas in the
Swiss case: the traditional media agenda, the social media agenda of parties, and the social
media agenda of politicians. Our theoretical expectations are based on the traditional
agenda-setting literature in policy and media studies as well as the more recent literature
on social media. First, we expect that the social media agenda of parties is more predictive ofthe traditional media agenda than vice-versa. This hypothesis is based on the (pre-social
media) consensus that “during campaigns, the media’s impact on politicians’ and parties’
agendas is limited or even absent” (Walgrave & Van Aelst, 2006, p. 96). Our observation
period includes the Swiss national elections held in October 2019 as well as several
referenda. Second, we expect that the traditional media agenda is more predictive of thesocial media agenda of individual politicians than vice-versa. This hypothesis is based on
the argument that “legislators are more likely to follow, than to lead, discussion of public
issues” (Barberá et al., 2019, p. 883). Third, we expect that the social media agenda of parties
is more predictive of the social media agenda of politicians than vice-versa. We argue that insystems with distributed power, individual politicians, especially political candidates, may
have a harder time shaping the parties’ agenda. However, we are open to the argument thatsocial media might shift the balance among parties and politicians. We acknowledge that foreach of the expectations, the theoretical arguments are less straightforward than in this briefsummary. We discuss them more in depth in Section 2.
Our empirical analysis compares daily issue salience in the Swiss media and on Twitterover two years, 2018 and 2019. First, we apply and validate supervised machine-learningclassifiers and use them to classify about 2.78 million articles published in 84 newspapers,
6,500 tweets posted on official party accounts, and 210,000 tweets posted by politicians on
their own accounts from January 2018 until December 2019. Then, following Barberá et al.
(2019), we use vector autoregression (VAR) models to determine which agenda (media,

POLITICAL COMMUNICATION

3

parties, politicians) is more predictive of which other agendas. In other words, we analyze
whether the media are more likely to report on a given issue after parties or politicians
discuss it on social media or, on the contrary, parties’ or politicians’ social media activity
reflects the issues that were previously reported in the media. Moreover, we consider the
relationship between parties and politicians.
Specifically, we concentrate on four sets of issues. First, we focus on two issues thatemerged prominently on the Swiss political agenda during our observation period, and inparticular during the campaign leading to the Swiss national elections in October 2019:
climate change and gender equality. Both issues were widely perceived to prompt someparties to redefine their positions facing the national elections in 2019 (Gilardi et al., 2020).
Second, as a contrast, we also consider two issues with longstanding salience in Swiss
politics (as well as other European countries): immigration and the relationship with theEuropean Union. In contrast to climate change and gender equality, these two issues
remained in the background during the 2019 elections. The focus on four issues withdifferent levels of salience allows us to test dynamics of agenda setting across different
types of issues.
Results show that not only do the traditional media agenda, the social media agenda ofparties, and the social media agenda of politicians influence one another but, overall, noagenda leads the others more than it is led by them. There is one important exception,
which we did not anticipate: for the environment issue, the social media agenda of parties ismore predictive of the traditional media agenda than vice-versa, by a significant differenceof two percentage points. These findings underscore how closely different agendas are tied
together, but also show that advocacy campaigns may play an important role in bothconstraining and enabling parties to push their specific agendas.

Theory and Hypotheses
Social Media and Political Agenda Setting
Following Kingdon’s classic definition,1 the political agenda has been understood as “the listof issues to which political actors pay attention” (Walgrave et al., 2008, p. 815). Therefore,
agenda-setting is the process by which some issues, but not others, attract political attention. We argue that social media have become an important part of this process. Agenda
setting has been studied in several literatures across several subfields in communication andpolitical science, which have not always been strongly integrated (Wolfe et al., 2013). A key,uncontroversial argument in all strands of the literature is that the media are an importantcomponent of political agenda setting (McCombs & Shaw, 1972, 1993). As Wolfe et al.
(2013, p. 179) state, “[a]genda setting from the policy processes approach is fundamentally
about the politics of attention and attention dynamics at the level of the political system. As
a consequence of this focus on information processing, media dynamics are intimately
bound up with policymaking.” While the literature shares this core premise, the empiricalfindings do not paint an unambiguous picture. As Sciarini and Tresch (2019, p. 734) write,“[a] number of studies have demonstrated that media coverage influences the issue priorities of a variety of political agendas, but that the media’s agenda-setting power is
contingent on a series of factors.” However, the specific conditions under which themedia affect the political agenda tend to vary depending on the specific study. Walgrave

4

F. GILARDI ET AL.

and Van Aelst (2006, p. 89), for example, concluded: “we still cannot answer the basic
question whether the mass media determine the political agenda or, put more precisely,
under what specific circumstances the mass media are able to boost political attention for
issues.” One reason might be that “only a few studies have actually compared the mutual
influence of media and politics” (Vliegenthart et al., 2016, p. 285). As Boydstun (2013,
p. 196) argues, “[i]t is extraordinarily difficult to get a theoretical or empirical handle on
how the media affects and simultaneously is affected by other political forces.” Making
progress on this front is one of the goals of this article.
We argue that social media as a communication platform creates new challenges andopportunities for political agenda setting. Social media do not just add a layer of complexity
to agenda-setting dynamics. Potentially, they change their nature. While these changes havebeen recognized in the literature, their implications are not fully understood. Our argument
proceeds in three steps.
First, social media have become a relevant channel for political communication (Chenet al., 2019; Feezell, 2018; Harder et al., 2017; Jungherr, 2014, 2016; King et al., 2017; Popaet al., 2020; Shapiro & Hemphill, 2017). Candidates and legislators use social media tocommunicate with journalists and the public (Barberá & Zeitzoff, 2017) and to engage with(or even attack) their political opponents (Russell, 2018). Politicians are less restricted in
expressing their opinions, compared to, for instance, parliamentary speech or parliamentaryquestions (Proksch & Slapin, 2015). Legislative activities are often regulated, for instance, due
to speaker selection rules (Herzog & Benoit, 2015), limited speaking time, or top-down control
by party leaders. On social media, however, candidates do not face these restrictions.
Therefore, social media channels are an ideal tool for politicians to shape their own profileand display expertise in certain areas they are highly interested in (Enli & Skogerbø, 2013).
Thus, we regard social media messages as a suitable proxy of politicians’ issue emphasis during
the legislative cycle and during election campaigns (Barberá et al., 2019, p. 884).
Second, social media are relevant not only for political communication in general, but foragenda setting specifically: “the rapid rise of social media, including the microblogging
platform Twitter, has provided new avenues for political agenda setting that have increasingly discernible impact” (Lewandowsky et al., 2020, p. 2). As Langer and Gruber (2021,
p. 3) argue, “a thorough understanding of agenda setting necessitates a broadening of
focus,” including both traditional and social media. Several studies document the agendasetting effects of social media: King et al. (2017) show that the media affect what people
tweet about; Feezell (2018) finds that people perceive issues to be more salient if they are
exposed to them in Facebook; Shapiro and Hemphill (2017) find that the New York Times is
responsive to congressional Twitter posts; Barberá et al. (2019) come to the opposite
conclusion, namely, that the media have a stronger influence on politicians than viceversa; James et al. (2019) argue that social media has not altered the gate-keeping role oftraditional media; finally, Fazekas et al. (2021) argue that politicians use Twitter to expand
issues from elites to the public. Importantly, social media have engendered “[p]oliticalinformation cycles [that] may involve greater numbers and a more diverse range of actors
and interactions than news cycles as they have been traditionally understood” (Chadwick,
2011, p. 8). Social media have reduced the gatekeeping power of traditional media, leading
to “hybrid media systems” (Chadwick, 2017) that have expanded the number and types ofactors who potentially have the ability to “introduce, amplify, and maintain topics, frames,and speakers that come to dominate political discourse” (Jungherr et al., 2019, p. 17).

POLITICAL COMMUNICATION

5

Third, via social media political actors can potentially reach an audience that goes wellbeyond social media users.2 Journalists monitor social media activity closely and use it in theirreporting: “tweets become public record and are increasingly incorporated into traditional
journalistic coverage of political events” (Jungherr, 2014, p. 2). The fact that journalists rely on
Twitter to decide which events and voices are newsworthy is well established in the literature.
As McGregor (2019, p. 1071) writes, “[j]ournalists draw on social media in various ways in thecourse of their reporting on political contests, from documenting public reaction to media
events to evaluating the performances of candidates.” In an experiment, McGregor and
Molyneux (2020) find that journalists evaluate the news-worthiness of tweets on par withheadlines from the Associated Press wire. Therefore, political actors can realistically hope to
influence the traditional media agenda using social media.
In sum, social media change political agenda setting dynamics for three reasons: first,
they are a relevant channel for political communication; second, they expand the number
and types of actors who can potentially shape the agenda; third, using social media, politicalactors can potentially reach the broader public via traditional media. In the next sections, we
discuss our expectations regarding the connections between the different agendas, taking
the role of social media into account.
Traditional Media Agenda and Social Media Agenda of Parties
To explain the extent to which parties set the media agenda or respond to it, we rely on
theories of issue ownership and agenda setting as well as responsiveness. On the one hand,previous research suggests that government parties often respond to changes agenda settingby the media and other parties (Green-Pedersen, 2019; Green-Pedersen & Mortensen,
2010). For example, literature on election campaigns has argued that “riding the wave,”i.e. campaigning on issues that dominate the news cycle, provides politicians with anopportunity to appear concerned and responsive (Ansolabehere & Iyengar, 1994). Whilemost of the literature studies parties’ responsiveness to the priorities of voters (Barberá
et al., 2019; Jones & Baumgartner, 2004; Klüver & Spoon, 2016; Neundorf & Adams, 2018;
O’Grady & Abou-Chadi, 2019), media reports are also an important source of information
for parties about public priorities.
In contrast, the question of whether and when parties can influence the media agenda hasreceived less attention from a party-competition perspective, even though parties face large
incentives to exert influence over the political agenda. As Schattschneider (1960, p. 69)
argued, “who determines what politics is about runs the country.” Setting the media agenda
allows parties to gain an electoral advantage by focusing public debate on issues that areelectorally favorable to them. To determine whether the media or parties lead in setting the
public agenda, it is crucial to consider the context of our study: while research on parties’
agenda-setting power has led to conflicting results (Vliegenthart et al., 2016; Walgrave &Van Aelst, 2006, pp. 95–98), there is a consensus that parties are able to set the agenda
during campaign times (Brandenburg, 2002; Hopmann et al., 2012; Walgrave & Van Aelst,2006). On the one hand, parties increase their efforts to influence the public agenda during
campaign times (Dalton et al., 1998, p. 476). On the other hand, media also simply focus onpolitical news more in those periods (Walgrave & Van Aelst, 2006).
In contrast to most of the literature, we consider the parties agenda expressed on social
media, specifically Twitter. Although Twitter is much less popular among the public than6

F. GILARDI ET AL.

Facebook, it matters for political communication: “especially what parties emphasize or
decide to talk about on Twitter contributes to what their supporters will know” (Popa et al.,2020, p. 329). The Twitter agenda can be considered “symbolic,” similar to parliamentaryquestions, although politicians can express themselves much more freely on social media,
and with a much broader potential audience. Like for parliamentary questions, however, we
expect the social media agenda to be more subject to media influences than the “substantial”
agenda, which comprises actual decisions. Therefore, the focus on the social media agenda
raises additional reasons to think that the media agenda might lead the parties’ agenda.
As we see, the literature is not entirely conclusive regarding the direction of agenda
setting influences between parties and the media, especially when considering parties
agendas on social media. It is clearly a question where more research is needed andwhere, hopefully, our study can make a useful contribution. Overall, given that we study
a period that includes a national election campaign and several referenda, we expect that, onbalance, the parties’ agenda leads the media agenda:
H1: The social media agenda of parties is more predictive of the traditional media agenda
than vice-versa.
Traditional Media Agenda and Social Media Agenda of Politicians
Our second hypothesis relates to the interactions between the media and politicians. Classic
behavioral theories of political parties assume that politicians are vote-seeking, officeseeking, and policy-seeking (Pedersen, 2012; Strom, 1990). Politicians aim to maximize
votes in elections to get elected or remain in parliament. Vote seeking has several observable
implications. Politicians put a lot of time and resources into campaigns (Sudulich &Trumm, 2019). Engaging in more campaign activities correlates with the probability ofwinning a seat (Bowler et al., 2020). Politicians appear to act strategically during a campaign
and emphasize policy issues that are perceived as important by the public. Research showsthat, at the individual level, political information seen on social media affects perceptions of
the salience of issues (Feezell, 2018).
We posit that vote-seeking behavior also influences the relationship between politicians
and the media. Politicians face strong incentives to profile themselves in the media.
Politicians are responsive to the electorate and to topics that dominate public discussions.
For instance, Barberá et al. (2019, p. 883) demonstrate that “legislators are more likely tofollow, than to lead, discussion of public issues.” A comparative study of media coverageand the content of parliamentary questions conclude that “the media matter more forpolitics than vice versa” (Vliegenthart et al., 2016, p. 284). These findings hold also in the
Swiss context (Sciarini et al., 2020). Besides increasing name recognition and presenting
their personality or personal life, politicians use social media to declare policy positions(Kobayashi & Ichifuji, 2015). When talking about policy issues, politicians can use social
media to “expand” issues from elites to the public (Fazekas et al., 2021). This expansion canoccur directly, when voters see and engage with social media posts by politicians, but also(and maybe especially) indirectly, when politicians and their messages make it into traditional media. Journalists frequently cite social media messages by politicians, and politicians
engage with social media with the goal of getting their message out (Jungherr, 2014). Tweets
are suitable sources for direct quotes since these messages are short, pointed, publicly

POLITICAL COMMUNICATION

7

available, and easily accessible. To make it into the news – a central vote-seeking strategy – itmakes sense for politicians to mention topics that are already salient in the media. In theirstudy of party press releases, Meyer et al. (2020, p. 281) show that “party messages are more
likely to make it into the news if they address concerns that are already important to themedia.” Such a logic may well apply to politicians and social media, and suggests that thedirection of agenda setting is from traditional media to politicians.
We see that the literature is not entirely clear regarding the direction of agenda setting
between traditional media and politicians, especially when considering social media. This is
another point where our study can make a valuable contribution. Overall, we expect thatissue emphasis in the media leads that of politicians on social media.
H2: The traditional media agenda is more predictive of the social media agenda of politicians
than vice-versa.
Social Media Agendas of Parties and Politicians
Our third expectation concerns the relationship between parties and politicians, which is
not straightforward. We argue that there is a reciprocal influence between parties andpoliticians, both within and across parties. As Polk and Kölln (2017, p. 5) put it, “parties
may be collective entities but internal factions, groups and divisions structure thoseentities.” Such divisions are particularly likely to appear on social media: “Social media is
comparatively free from agenda setting or selection power by political parties. An arena, inwhich personal preferences and individual strategic considerations dominate position
taking” (Sältzer, 2020, p. 3). From this perspective, we argue parties and candidates canbe considered as two separate entities. However, there has been little research on how
parties’ central communication interacts with communication by politicians. We suspectthat this partly depends on the character of the political system. In systems with distributed
power, such as the Swiss system, individual politicians may have a harder time shaping theparties agenda with their social media activity than in systems where individual officeholders yield significant power. This holds all the more given we look at political candidates,
some of which do not hold office and may lack the resources to get significant publicattention. What is more, individual politicians may want to respond to the larger agenda toreceive attention and to increase their profile within the political sphere. Hence, we expect
parties to yield more influence than individual politicians. In a narrower sense, much of theinfluence of parties may be on their own politicians. Based on research on intra-party
politics, we may expect that parties lead the candidate agenda. Internal coherence anddiscipline is important to parties (Greene & Haber, 2016), as intra-party conflict can harm
public approval. Parties that are perceived as internally divided and ambiguous in theirpolicy statements enjoy considerably lower degrees of public support than internallycoherent and broad-appealing strategies (Lehrer & Lin, 2020; Lin & Lehrer, 2020).
However, broad-appeal strategies can be electorally successful (Somer-Topcu, 2015).
Therefore, parties may encourage their politicians to focus on many different issues andnot necessarily stick to the issues that the party focuses on during an election campaign.
Social media is highly relevant in this context because it makes it very easy for politicians to
conduct their own communication fully autonomously, engaging in exchanges both within
and across parties, as well as with journalists and the broader public.

8

F. GILARDI ET AL.

Overall, we posit that politicians respond to parties rather than the other way around,and candidates do not go too far beyond the wider party issue agenda, given the asymmetry
in their prominence. Instead, politicians might follow the agenda set by their party specifically in order to signal responsiveness and internal coherence. They may equally follow theagenda of other parties as “first responders” to the agenda of competitors and to raise theirpolitical profile. This assumption does not imply that politicians always stick to the party
agenda. For vote-seeking and policy-seeking reasons, it may well make sense for politicians
to put their own issues forward at times, especially when there is a favorable opportunity
structure for a topic. Yet, on balance, we expect that the parties agenda leads the politicians
agenda on social media:3
H3: The social media agenda of parties is more predictive of the social media agenda ofpoliticians than vice-versa.

Data and Methods
Data
We draw the data for this study from three sources: First, Twitter data from Swiss parties,
politicians, organizations, administrations and newspapers; second, newspaper articles
from Swiss newspapers; and third, official press releases published by the parties andorganizations in the Twitter sample. For all three sources, we gather documents publishedbetween January 1st, 2018 and December 31st, 2019. For the three data sets, this results in
330,375 tweets (excluding retweets) from more than 1,161 accounts, 2,786,159 articles from
83 different newspapers and 6,477 press releases.4 We outline the process of data collection
and analysis below. All data was collected daily through an ingestion system distributed
over multiple machines which collects the data from the different sources and immediately
stores it in a database.
Twitter Data
The collection of tweets includes all tweets from the six largest parties in the national
council of Switzerland as well as all politicians5 of these parties who had a Twitter accountbefore the national election in 2019 and tweets from newspapers, organizations andadministrations.6 The number of tweets for each actor is summarized in Table A4. The
parties alone published 6,548 tweets over the course the two years. The politicians of theseparties are responsible for 211,790 additional tweets.
Newspaper Articles
In addition to the news outlets’ Twitter accounts, we also collected and classified allnewspaper articles published by 84 Swiss newspapers. The articles are available throughthe swissdox database. The text corpus of consists of 2,786,159 articles. In Table A5, wereport the average number of articles each paper published at a given day over the full
two year period along with the maximum and minimum number of articles.

POLITICAL COMMUNICATION

9

Press Releases
Finally, we collected all official press releases published by Swiss political parties and manyorganizations. While we are not primarily interested in press releases, they are an important
medium for parties to communicate their policy positions and issue emphasis (Haselmayer
et al., 2017). Hence, we include them in our model to assess the effect of each data source netof the effect of this frequently used source. Due to the relative low volume of press releases
with only 6,477 published texts over the time period (of which 1,195 cover one of our four
topics of interest), we aggregate all parties to a single actor. Thus, press releases serve as the“baseline” of issue attention at a given time.

Methods
Measuring Attention to Political Issues with Ensemble Classifiers
We build two classification systems for the tweets and the longer news articles separately
using supervised machine learning.7 The main difference between the two classifiers is that
we implement a two-step classification for newspaper articles, since we collect not only
political but all articles from the respective newspapers. Therefore, we first use a simple
keyword counting decision tree to decide whether a text is politically relevant in Switzerland
or not (Figure 1). Only texts that contain information on politics are included in theclassifiers of issue emphasis.
To train the classifier for newspaper articles, we rely on a dataset of newspaper articles
classified by hand from the APS (Année Politique Suisse).8 The number of annotatedarticles included in the training set for the newspaper classification range from 1,210(gender) to 9,889 (environment) German articles and from 318 (gender) to 1,460 (immigration) French news articles. This allows us to use supervised machine learning for theclassification instead of unsupervised techniques (such as topic models).
For the classification of tweets we built separate classifiers because transfer learning(classifiers trained on newspapers and applied on tweets) does not work reliably. We used
Text classified as"Swiss politicalcontent"

Ensemble
topic classifierMore political keywords

Raw newspaper
articlesCount political
keywords (Swisscontext) and nonpolitical keywords

Text classified as"Swiss politicalcontent"

Less political keywords

Classified
newspaper
articlesMatch foundSearch for
Named Entities(Swisscontext)
No match found
Text classified asirrelevant

Figure 1. Binary classification procedure for newspaper articles running on our distributed cron-like taskscheduler.

10

F. GILARDI ET AL.

all tweets we collected from politicians, parties, organizations and experts participating in
the weekly political TV show SRF Arena published between January 1st and September 1st
2019. We considered the 2,000 most frequent German and French hashtags in the corpus toclassify the tweets into nine different political topics and a category for “other” topics. The
number of annotated tweets per class range from 345 to 1,956 French tweets and from 5,320
to 13,410 German tweets.
The classifiers are built around an ensemble procedure which uses different algorithms toclassify the texts (Géron, 2019). The feature engineering is based on a Word to Vector(Word2Vec) approach. Word2Vec is a neural network trained to reconstruct linguistic
context of words using a vector space built form a large corpus of words. We base the vector
space on our text corpus which enables us to reconstruct the linguistic context of words
within the political domain. The ensemble classifiers for tweets and longer texts both
perform reasonably well over all topics of interest. The main difference between the twoclassifier systems, besides the different training data, is that the social media classifierclassifies texts into ten different topics, while the second classifier considers a slightly
wider range of topics (see also Table A1). The ensemble method results in out-of-sample
accuracy of at least 80% for all classes in German and French. Tables A2 and A39 provide
detailed information on the classifier performance and underscore that the ensemble
methods do not suffer from systematic classification error.
The classifier for social media texts has an overall F1 score of 88% in German and 65% in
French, while the overall classification of newspaper articles has an F1 score of 78% in
German and 86% in French. Due to the large amount of training data, we do not need toconsider active learning approaches and do not face the issue of imbalanced classes (Fong &Tyler, 2020). Our extensive validation in the Supporting Information underscores that we
do not face the issue of systematic misclassification.
Autoregressive Models
To test our hypotheses we draw on vector autoregression models (VAR) with topic-fixed
effects. VAR models explain the evolution of multiple variables based on their own lagged
values as well as the lagged values of other terms, thereby allowing us to analyze the relation
between several evolving variables. VAR models are well suited to capture the process
between endogenous variables and have been used by similar studies (Barberá et al., 2019;
Edwards & Wood, 1999; Wood & Peake, 1998). Compared to similar models, e.g., structural
equation models, it is not necessary to have a precise theory regarding what influences thedevelopment of a variable. Instead, the model merely requires to include all which can be
hypothesized to affect each other inter-temporally (Qin, 2011). Note that we only interpretand present results for the effect of tweets by parties and politicians, as well as newspaper
articles. We include but do not discuss in our model specification other variables that may
affect the salience of political issues, namely the salience of these issues in press releases ofparties and other actors, as well as tweets by newspapers, organizations or administrations.
We build our model on the design of Barberá et al. (2019). They used a VAR model witha set of stationary time series Yi representing the share of the daily attention each group i paid
to the topics j of interest. The model employs a set of stationary time series Yi which represent
the proportion of the daily attention each group of actors i paid to each topic j of intereston day t over the period of the years 2018 and 2019. The distribution is strongly skewed to theright as attention is distributed among many issues. In turn, this results in low issue attention

POLITICAL COMMUNICATION

11

for any single issue on any given day, except very few time points when a specific issue receives
more attention. Hence, we use the log odds Zi of the described series Yi.
We express the endogenous relationship of these variables as a system of equations where
each variable Zi is a function of its own previous lags plus the lags of all the other variables.
In this specification, there are no time-restrictions to when groups respond to changes inpublic issue attention. While we expect such responses to be rather fast on social media and,with a short delay, in news reports, we account for a possible longer-term decay by using
a seven day lag structure.10 Hence, our model therefore looks exactly like Barberá et al.
(2019) and can be expressed as:�
�Y
Z ¼ log
1 Y

Zi;j;t ¼ αj þX
X 7daysiβi;p Zi;j;t p þ εi;j;tp¼1

The model allows us to estimate how much issue attention by one group needs to change
on average to predict a subsequent reallocation of the issue attention of another group. Since
the results of these models are hard to interpret, we use cumulative impulse response functions(IRFs), which allow us to display how a 10 percentage point unit increase in attention toa given issue by a group, changes the cumulative attention the other actors contribute to thesame topic over time. We do this for brief changes in attention of 10 percentage points (from0% to 10%) for just one day at day zero. This change is calculated in a simulation for up to
60 days after the initial increase. These cumulative impulse responses are then summed overthe first seven days to calculate the seven day lag structure to incorporate a whole news cycle ofabout one week. In Figure A5, we report all results using lags of one, three, five, seven, andnine days. The direction of the effects is identical across specifications.

Results
We present three sets of results regarding the connection between the traditional media
agenda, the social media agenda of parties, and the social media agenda of politicians. First,
we describe the distribution of issue salience over time for the three agendas. Second, weanalyze the responsiveness of the traditional media, parties, and politicians to each other’s
agendas. Third, we examine which agenda, on balance, is more predictive of the others. This
third point allows us to test our hypotheses more directly.
Figure 2 shows the distribution of the three agendas over time, with a focus on the four
issues that we consider in this article: environment, gender, Europe, and immigration. First,
some context: the Swiss National Elections 2019 were held on October 20, 2019, and werecharacterized by massive gains for the Green and Green Liberal parties, at the expense of theSocial Democratic Party and the Swiss People’s Party. The election campaign was concernedwith various topics (Gilardi et al., 2020).11 In particular, there is widespread consensus
among political actors and commentators that climate change and environmental issues
dominated the campaign. This assessment is confirmed by a systematic media analysis(Gilardi et al., 2020). Moreover, 2019 was perceived to be a “women’s year,” due to thehistorical women’s strike held June 14, which was the biggest protest event since decades in12

F. GILARDI ET AL.

Figure 2. Distribution of issue emphasis over time.

Switzerland, as well as the record number of female politicians running for office in thenational elections (leading to a record number of women represented in parliament).
However, the two issues were not equally dominant and present (Gilardi et al., 2020).
The environment issue was constantly central in the traditional media and one of the more

POLITICAL COMMUNICATION

13

frequent topics online. By contrast, gender issues were highly salient around the women’s
strike, but were otherwise not a dominant topic. However, both issues ultimately shaped theelections, on the one hand with the “green wave” and the increase in Green seats, on theother hand with an increase in the proportion of women in both the National Council andthe Council of States. Two other issues that are usually highly salient – immigration andEurope – remained in the background during the election campaign, although the relationship with the European Union was debated intensely in the context of a referendum held in
May 2019. The referendum concerned gun regulations but had a direct link with theagreements that Switzerland has with the EU.
Several aspects stand out in Figure 2. First, in the traditional media, the environment was
the most salient issue in 2018 and, especially, 2019. Second, the politicians’ focus on theenvironment on social media is apparent throughout 2019, and especially during the core of
the election campaign (August to October 2019). Third, parties engaged intensively with theEurope issue during the campaign leading to the May 2019 referendum, which impacted
directly Switzerland’s relationship with the EU. Fourth, immigration and gender had low
salience throughout most of the period. Among politicians, immigration was the leastsalient issue for most of 2019. Regarding gender, the relative salience among politicians,
and a few peaks for newspapers and parties, have to do with the historical women’s strike
held on June 14, 2019, as well as with the record number of women running for office (andeventually being elected).
Figure 3 shows the interdependencies between the three agendas, controlling for the press
releases of the most relevant political actors.12 Specifically, the figure shows how each agenda
responds to a one-time, 10 percentage-point increase in attention to each of the four issues ineach of the other two agendas. For example, the fourth block from the top (Parties →Newspapers) shows the effect of such an increase in the social media agenda of parties on
the traditional media agenda. We see that when parties tweeted more about the environment
or gender, the attention traditional media gave to those issues in the following week increased
by about 1 (for gender) and 3 (for the environment) percentage points, although the relationship is statistically significant only for the environment issue. We translate these two estimates
into substantive effects. The estimate for gender translates to about 260 newspaper articles, 0.7
tweets by parties and 20 tweets by candidates per day. For the environment, this translates toabout 800 newspaper articles, 2.2 tweets by parties and 61 tweets by candidates. The effects arecomputed for an average week in our time period. These effect sizes are comparable to thosemeasured by Barberá et al. (2019, e.g., Figure 6, p. 897). For the two other issues, like forgender, more tweets by parties are not significantly associated with more media coverage. In
our context, what separates the environment and gender issue from Europe and immigrationis that the former became highly salient in 2019 due to two large-scale advocacy campaigns by
nonpartisan actors, namely, the climate respectively women’s strike.
Overall, several insights stand out in Figure 3. First, all correlations are either indistinguishable from zero or, more often, positive, which makes sense. Second, and related, we observea high degree of mutual influence among the various agendas. Third, the strongest relationships, in terms of effect size, are driven by the social media agenda of parties. Fourth, there are
relatively few differences among issue areas. Fifth, although for many topics media effects are
significant, they are small. This result is consistent with findings pointing to limited media
influence during elections campaigns (Brandenburg, 2002; Hopmann et al., 2012; Walgrave &Van Aelst, 2006), although we cover a much longer period.

14

F. GILARDI ET AL.

Figure 3. Agenda responsiveness of parties, politicians, and newspapers. Bars denote 95% confidence
intervals.

Figure 4 allows us to test our hypotheses more directly, again controlling for the press
releases of the most relevant political actors. The hypotheses imply a comparison between
the mutual responsiveness of agendas. For instance, as shown in Figure 3, the social media
agenda of parties predicts the social media agenda of politicians, but the opposite is also
true. On balance, does one agenda lead more than it follows, or do they lead and follow toroughly the same extent? We test the direct comparisons by calculating the differences
between the agenda responsiveness of each pair of actor groups against each other. The 95%
confidence interval of the differences are based on 1,000 simulated effects of each actor’s
agenda responsiveness on another actor, subtracting the values of the same pair of actors in
the opposite direction. This allows us to test if one group’s agenda leads the other more thanit follows it. Figure 4 shows that, overall, the three agendas both lead and follow each otherin roughly equal measure. For example, when politicians post more content on theenvironment issue on Twitter, it leads newspapers to publish more articles on that issue.
But the relationship goes also in the opposite direction: when newspapers publish more
articles on the environment, politicians tweet more about that issue. Figure 4 shows that formost issues and groups, the strength of those relationships is roughly the same, such that noagenda leads the others more than it is led by them.

POLITICAL COMMUNICATION

15

Figure 4. Attention responsiveness differences. Bars denote 95% confidence intervals.

There is one important exception to this pattern, which we did not anticipate: for theenvironment issue, the traditional media are over two percentage points more responsive toparties than parties are to traditional media. In other words, for the environment issue,
parties lead newspapers more than they follow them. Again, what characterized this issue in2019 was its high salience due to an advocacy campaign that was largely exogenous to the
Swiss political system. Given this high degree of salience, we expected that parties would not
be able to further increase it. However, the opposite turned out to be the case. Finally, forissues other than the environment, the social media agendas of parties seem to be morepredictive of the social media of politicians than vice-versa, which is consistent with ourthird hypotheses. None of the differences among these two sets of actors are statisticallysignificant, however.

Conclusion
In this article, we studied the relationship between three agendas: the traditional media
agenda, the social media agenda of candidates, and the social media agenda of politicians.
First, we observe significant influences among several agendas. Only in a few cases (e.g.,
the effect of parties on newspapers on three issues and newspapers on parties on two
issues) are the effects indistinguishable from zero. Second, contrary to most studies, wehave considered the mutual influences among these agendas, and found that, on balance,
they counterbalance each other. That is, each agenda influences each other agenda byroughly the same extent. There is an important, surprising exception, however: for theenvironment issue, the social media agenda of parties is more predictive of the traditional
media agenda, with a significant difference of about two percentage points. The analysis
takes into account the press releases of various political actors, including parties.

16

F. GILARDI ET AL.

Therefore, the differences we uncover can be attributed specifically to the social media
activity of parties.
Regarding our results for the environment issue, our paper highlights the need forfurther research in differences across issues and the role of advocacy campaigns thatpoliticize an issue. Recent literature has pointed to both permanent “problem characteristics” and more variable “problem information” as determinants of political attention toissues (Green-Pedersen, 2019). Two of the issues we discuss, namely gender and theenvironment, were the subject of large-scale campaigns by actors not explicitly coveredby our empirical approach. Such an expansion of actors, including to the protest arena, has
frequently been identified as crucial to the politicization of issues (Hutter et al., 2016). At
least for the environment, this expansion seems to have increased responsiveness to theagenda of political parties. This may be a consequence of the high public resonance ofpolitical statements on this issue in the context of the climate strike. As our empiricalapproach compares several issues, we are unable to test how the variation in the availabilityof “problem information” affects agenda-setting capacity on single issues like the environment in detail. However, we hope that our findings will inspire future work that analyzes therelationship between advocacy and election campaigns with an event-centered approach.
Our paper stands out in several ways. We study a longer period (two years) than moststudies; we consider both traditional and social media; we distinguish between parties andpoliticians; and we analyze the mutual influences among the different agendas instead of
focusing on a specific direction. Our findings underscore the complexity of the relationshipbetween the political and media agendas, but also the potential of social media for politicalcommunication. In some contexts – in our case, a topic already enjoying a high degree ofsalience – political parties can further increase media attention to the environment issue,
net of press releases and other confounding factors.
Our study also highlights the value of Twitter data to extend the analysis of issueemphasis by parties and politicians to new forms of political communication. First, eventhough Twitter users are not representative of the population (Blank, 2017; Jungherr, 2016;
Mellon & Prosser, 2017), an increasing proportion of candidates and elected politicians use
Twitter extensively. Second, parliamentary questions or speeches, previously used as
proxies for politicians’ issues emphasis, usually do not receive much public attention. In
fact, politicians tend to over-estimate the influence of these channels (Soontjens, 2020).
Third, politicians can state their policy preferences on social media platforms without
institutional restrictions, such as speaker selection or limited speaking time in parliament(Proksch & Slapin, 2015). Online communication might offer a more comprehensive
assessment of personal issue priorities. As we have argued in discussing the importanceof social media, these statements are treated as newsworthy by traditional media andultimately reach large audiences (McGregor & Molyneux, 2020; Jungherr, 2014). We
present a method to classify tweets by parties and politicians and link them to newspaper
reports in order to study daily agenda setting dynamics.
This article contributes primarily to a better understanding of the role of social media
and agenda setting, but our results also provide avenues for further research. While we havealready pointed to differences between topics, cross-national and comparative research
could investigate how party characteristics shape actors’ agenda setting capacity. For
instance, the size of a party, its incumbency status or the ownership of certain issues (GreenPedersen, 2019) could mediate the ability to set the political agenda.

POLITICAL COMMUNICATION

17

Social media have become an established channel for political communication, which
parties and politicians are eager to use. Despite their relevance, many aspects remain poorly
understood, including their role in political agenda setting. Our findings contribute toa growing body of research and, we hope, provide a template that could be replicated andextended in many different contexts.

Notes
1. “[T]he list of subjects or problems to which governmental officials, and people outside ofgovernment closely associated with those officials, are paying some serious attention at any
given time” (Kingdon, 1984, p. 3).
2. Social media have an elite character in our case. During the 2019 Swiss election campaign, onlyaround 15% of respondents stated that they were attentive to political news on social media like
Facebook and Twitter (compared to over 50% for newspapers) (Selects, 2020).
3. This hypothesis, and the corresponding analysis, does not distinguish among parties.
Disaggregated results by party are shown in SI Section G.
4. We also rerun our models by including retweets which increases the number of tweets to397,828 but does not change our substantive conclusions (SI Section C).
5. Specifically, we consider all politicians who ran for office in the national elections 2019.
6. The six strongest parties in the national council between 2015 and 2019 were the SVP (Swiss
People’s Party), SP (Social Democratic Party), FDP (Liberal Party), CVP (Christian Democratic
Party), Greens, GLP (Green Liberal Party) and the BDP (Conservative Democratic Party).
7. We also use the classification system based on newspaper articles to classify the content of press
releases included in our model.
8. https://anneepolitique.swiss/.
9. As a point of comparison, Barberá et al. (2021) report an accuracy of 71% for the classificationof sentiment on the level of sentences.
10. Using seven days as lag structure accounts for the differences in article numbers due to weekdays(see SI Section F). We also use a seven day lag to account not only for the differences in news
article frequencies over different weekdays but also to account for the typical news cycle in
Switzerland which ends and starts with the weekly Sunday editions of many newspapers.
11. The analysis does not contrast “campaign” and “routine” times because such a clean comparison is not possible in our case. First, it is not clear when exactly the electoral campaign starts.
The campaign proper is considered to start in August, but the media consider anything
happening in the whole year as part of the election campaign. Second, the Swiss politicalsystem is characterized not only by national elections every four years, but also by nationalreferenda (four times a year) as well as cantonal elections and referenda.
12. We include the same model without the press releases. There is no difference visible whencomparing the two models regarding the agendas of interest, since the model is only looking at
the interaction of one agenda with all others at once (see also SI Section D).

Acknowledgments
We thank Lucien Baumgartner for research assistance, Clau Dermont for coordinating data collection
and contributing valuable ideas, and Deen Freelon, Alexandra Feddersen, Christina Gahn, Christoffer
Green-Pedersen, Zachary Greene, Emiliano Grossmann, Ana Langer, Thomas Meyer, Markus Wagner,and three anonymous reviewers for helpful comments. Previous versions of this article were presented atthe 2020 Annual General Conference of the European Political Science Association (virtual), the Digital
Democracy Workshop at the Digital Democracy Lab (virtual), and the workshop Party Competition in
the Electoral Cycle at the Université de Fribourg/Humboldt University of Berlin (virtual).

18

F. GILARDI ET AL.

Disclosure Statement
No potential conflict of interest was reported by the author(s).

Funding
This project received funding from the Swiss National Science Foundation (grant nr.
10DL11_183120) and the European Research Council (ERC) under the European Union’s Horizon
2020 research and innovation program (grant agreement nr. 883121)

Notes on contributors
Fabrizio Gilardi (Ph.D., University of Lausanne) is Professor of Policy Analysis at the Department of
Political Science of the University of Zurich, and director of the Digital Democracy Lab. His researchinterests include digital technology & politics, gender & politics, and policy diffusion.
Theresa Gessler (Ph.,D., European University Institute) is a Postdoctoral Researcher at the Digital
Democracy Lab and the Department of Political Science of the University of Zurich. Her researchinterests include political communication, democracy, immigration, gender, and computationalsocial science.
Maël Kubli (M.A., University of Zurich) is a PhD Student at the Digital Democracy Lab and the
University of Zurich. His research interests include social media, e-government, political communication, and computational social science.
Stefan Müller (Ph.D., Trinity College Dublin) is an Assistant Professor and Ad Astra Fellow in the
School of Politics and International Relations at University College Dublin. His research interests
include political representation, party competition, political communication, public opinion, andcomputational social science.

ORCID
Fabrizio Gilardi
http://orcid.org/0000-0002-0635-3048
Theresa Gessler
http://orcid.org/0000-0003-2339-6266Maël Kubli
http://orcid.org/0000-0002-5592-9648
Stefan Müller
http://orcid.org/0000-0002-6315-4125

Data Availability Statement
The data described in this article are openly available within the Harvard Dataverse Network at
https://doi.org/10.7910/DVN/PS3MNB.

Open ScholarshipThis article has earned the Center for Open Science badges for Open Data and Open Materials. The
data and materials are openly accessible at https://doi.org/10.7910/DVN/PS3MNB.

POLITICAL COMMUNICATION

19

References
Ansolabehere, S., & Iyengar, S. (1994). Riding the wave and claiming ownership over issues: The joint
effects of advertising and news coverage in campaigns. Public Opinion Quarterly, 58(3), 335–357.
https://doi.org/10.1086/269431
Bachrach, P., & Baratz, M. S. (1962). Two faces of power. American Political Science Review, 56(3),947–952. https://doi.org/10.2307/1952796
Barberá, P., Boydstun, A. E., Linn, S., McMahon, R., & Nagler, J. (2021). Automated text classificationof news articles: A practical guide. Political Analysis, 29(1), 19–42. https://doi.org/10.1017/pan.
2020.8
Barberá, P., Casas, A., Nagler, J., Egan, P. J., Bonneau, R., Jost, J. T., & Tucker, J. A. (2019). Who leads?
Who follows? Measuring issue attention and agenda setting by legislators and the mass publicusing social media data. American Political Science Review, 113(4), 883–901. https://doi.org/10.
1017/S0003055419000352
Barberá, P., & Zeitzoff, T. (2017). The new public address system: Why do world leaders adopt social
media? International Studies Quarterly, 62(1), 121–130. https://doi.org/10.1093/isq/sqx047
Blank, G. (2017). The digital divide among twitter users and its implications for social research. Social
Science Computer Review, 35(6), 679–697. https://doi.org/10.1177/0894439316671698
Bowler, S., McElroy, G., & Müller, S. (2020). Campaigns and the selection of policy-seeking
representatives. Legislative Studies Quarterly, 45(3), 397–431. https://doi.org/10.1111/lsq.12260
Boydstun, A. E. (2013). Making the news: Politics, the media, and agenda setting. University of
Chicago Press.
Brandenburg, H. (2002). Who follows whom? The impact of parties on media agenda formation in
the 1997 British general election campaign. The International Journal of Press/Politics, 7(3), 34–54.
https://doi.org/10.1177/1081180X0200700303
Chadwick, A. (2011). The political information cycle in a hybrid news system: The British prime
minister and the “bullygate” affair. The International Journal of Press/Politics, 16(1), 3–29. https://doi.org/10.1177/1940161210384730
Chadwick, A. (2017). The hybrid media system: Politics and power. Oxford University Press.
Chen, K., Lee, N., & Marble, W. (2019). How policymakers evaluate online versus offline constituent
messages. https://ssrn.com/abstract=3251651
Dalton, R. J., Beck, P. A., Huckfeldt, R., & Koetzle, W. (1998). A test of media-centered agenda setting:
Newspaper content and public interests in a presidential election. Political Communication, 15(4),463–481. https://doi.org/10.1080/105846098198849
Edwards, G. C., & Wood, B. D. (1999). Who influences whom? The president, congress, and the
media. American Political Science Review, 93(2), 327–344. https://doi.org/10.2307/2585399
Enli, G. S., & Skogerbø, E. (2013). Personalized campaigns in party-centred politics: Twitter andFacebook as arenas for political communication. Information, Communication & Society, 16(5),757–774. https://doi.org/10.1080/1369118X.2013.782330
Fazekas, Z., Popa, S. A., Schmitt, H., Barberá, P., & Theocharis, Y. (2021). Elite-public interaction on
Twitter: EU issue expansion in the campaign. European Journal of Political Research, 60(2), 376–
396. https://doi.org/10.1111/1475-6765.12402
Feezell, J. T. (2018). Agenda setting through social media: The importance of incidental news
exposure and social filtering in the digital era. Political Research Quarterly, 72(2), 482–494.
https://doi.org/10.1177/1065912917744895
Fong, C., & Tyler, M. (2020). Machine learning predictions as regression covariates. Political Analysis,
1–18. Online First. https://doi.org/10.1017/pan.2020.38
Géron, A. (2019). Hands-on machine learning with Scikit-learn, Keras, and TensorFlow: Concepts,
tools, and techniques to build intelligent systems. O’Reilly Media.
Gilardi, F., Dermont, C., Kubli, M., & Baumgartner, L. (2020). Der Wahlkampf 2019 in traditionellen
und digitalen Medien. Selects Medienstudie 2019.
Greene, Z., & Haber, M. (2016). Leadership competition and disagreement at party national
congresses. British Journal of Political Science, 46(3), 611–632. https://doi.org/10.1017/
S0007123414000283

20

F. GILARDI ET AL.

Green-Pedersen, C. (2019). The reshaping of West European party politics: Agenda-setting and party
competition in comparative perspective. Oxford University Press.
Green-Pedersen, C., & Mortensen, P. B. (2010). Who sets the agenda and who responds to it in theDanish parliament? A new model of issue competition and agenda-setting. European Journal of
Political Research, 49(2), 257–281. https://doi.org/10.1111/j.1475-6765.2009.01897.x
Harder, R. A., Sevenans, J., & Van Aelst, P. (2017). Intermedia agenda setting in the social media age:
How traditional players dominate the news agenda in election times. The International Journal ofPress/Politics, 22(3), 275–293. https://doi.org/10.1177/1940161217704969
Haselmayer, M., Wagner, M., & Meyer, T. M. (2017). Partisan bias in message selection: Media
gatekeeping of party press releases. Political Communication, 34(3), 367–384. https://doi.org/10.
1080/10584609.2016.1265619
Herzog, A., & Benoit, K. (2015). The most unkindest cuts: Speaker selection and expressed goverment
dissent during economic crisis. The Journal of Politics, 77(4), 1157–1175. https://doi.org/10.1086/
682670
Hopmann, D. N., Elmelund-Præstekær, C., Albæk, E., Vliegenthart, R., & De Vreese, C. H. (2012).
Party media agenda-setting: How parties influence election news coverage. Party Politics, 18(2),173–191. https://doi.org/10.1177/1354068810380097
Hutter, S., Grande, E., & Kriesi, H. (2016). Politicising Europe. Cambridge University Press.
James, C., Banducci, S., Cioroianu, I., Coan, T., Katz, G., & Stevens, D. P. (2019). Flows of informationin election campaigns: Who influences whom? https://papers.ssrn.com/abstract=3722044 .
Jones, B. D., & Baumgartner, F. R. (2004). Representation and agenda setting. Policy Studies Journal,32(1), 1–24. https://doi.org/10.1111/j.0190-292X.2004.00050.x
Jungherr, A. (2014). The logic of political coverage on Twitter: Temporal dynamics and content.
Journal of Communication, 64(2), 239–259. https://doi.org/10.1111/jcom.12087
Jungherr, A. (2016). Twitter use in election campaigns: A systematic literature review. Journal of
Information Technology & Politics, 13(1), 72–91. https://doi.org/10.1080/19331681.2015.1132401
Jungherr, A., Posegga, O., & An, J. (2019). Discursive power in contemporary media systems:
A comparative framework. The International Journal of Press/Politics, 24(4), 404–425. https://doi.org/10.1177/1940161219841543
King, G., Schneer, B., & White, A. (2017). How the news media activate public expression andinfluence national agendas. Science, 358(6364), 776–780. https://doi.org/10.1126/science.aao1100
Kingdon, J. W. (1984). Agendas, alternatives, and public policies. Boston: Little, Brown.
Klüver, H., & Spoon, J.-J. (2016). Who responds? Voters, parties and issue attention. British Journal of
Political Science, 46(3), 633–654. https://doi.org/10.1017/S0007123414000313
Kobayashi, T., & Ichifuji, Y. (2015). Tweets that matter: Evidence from a randomized field experimentin Japan. Political Communication, 32(4), 574–593. https://doi.org/10.1080/10584609.2014.986696
Langer, A. I., & Gruber, J. B. (2021). Political agenda setting in the hybrid media system: Why legacy
media still matter a great deal. The International Journal of Press/Politics, 26(2), 313-340. https://doi.org/10.1177/1940161220925023
Lehrer, R., & Lin, N. (2020). Everything to everyone? Not when you are internally divided. Party
Politics, 26(6), 783–794. https://doi.org/10.1177/1354068818812222
Lewandowsky, S., Jetter, M., & Ecker, U. K. H. (2020). Using the president’s tweets to understandpolitical diversion in the age of social media. Nature Communications, 11(1), 1–12. https://doi.org/
10.1038/s41467-020-19644-6
Lin, N., & Lehrer, R. (2020). Everything to everyone and the conditioning effect of intraparty
cohesion: A replication in a cross-national context. Party Politics, 1–8. Online First. https://doi.
org/0.1177/1354068820918780
McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion
Quarterly, 36(2), 176–187. https://doi.org/10.1086/267990
McCombs, M. E., & Shaw, D. L. (1993). The evolution of agenda-setting research: Twenty-five years
in the marketplace of ideas. Journal of Communication, 43(2), 58–67. https://doi.org/10.1111/j.
1460-2466.1993.tb01262.x
McGregor, S. C. (2019). Social media as public opinion: How journalists use social media to represent
public opinion. Journalism, 20(8), 1070–1086. https://doi.org/10.1177/1464884919845458

POLITICAL COMMUNICATION

21

McGregor, S. C., & Molyneux, L. (2020). Twitter’s influence on news judgment: An experimentamong journalists. Journalism, 21(5), 597–613. https://doi.org/10.1177/1464884918802975
Mellon, J., & Prosser, C. (2017). Twitter and Facebook are not representative of the general population: Political attitudes and demographics of British social media users. Research & Politics, 4(3),1–9. https://doi.org/10.1177/2053168017720008
Meyer, T. M., Haselmayer, M., & Wagner, M. (2020). Who gets into the papers? Party campaign
messages and the media. British Journal of Political Science, 50(1), 281–302. https://doi.org/10.
1017/S0007123417000400
Neundorf, A., & Adams, J. (2018). The micro-foundations of party competition and issue ownership:
The reciprocal effects of citizens’ issue salience and party attachments. British Journal of Political
Science, 48(2), 385–406. https://doi.org/10.1017/S0007123415000642
O’Grady, T., & Abou-Chadi, T. (2019). Not so responsive after all: European parties do not respond to
public opinion shifts across multiple issue dimensions. Research & Politics, 6(4), 1–7. https://doi.
org/10.1177/2053168019891380
Pedersen, H. H. (2012). What do parties want? Policy versus office. West European Politics, 35(4),896–910. https://doi.org/10.1080/01402382.2012.682350
Peeters, J., Van Aelst, P., & Praet, S. (2019). Party ownership or individual specialization?
A comparison of politicians’ individual issue attention across three different agendas. Party
Politics, 1–12. Online First. https://doi.org/10.1177/1354068819881639
Polk, J., & Kölln, A.-K. (2017). The lives of the party: Contemporary approaches to the study of
intraparty politics in Europe. Party Politics, 23(1), 3–6. https://doi.org/10.1177/
1354068816655572
Popa, S. A., Fazekas, Z., Braun, D., & Leidecker-Sandmann, M.-M. (2020). Informing the public: How
party communication builds opportunity structures. Political Communication, 37(3), 329–349.
https://doi.org/10.1080/10584609.2019.1666942
Proksch, S.-O., & Slapin, J. B. (2015). The politics of parliamentary debate: Parties, rebels, andrepresentation. Cambridge University Press.
Qin, D. (2011). Rise of VAR modelling approach. Journal of Economic Surveys, 25(1), 156–174.
https://doi.org/10.1111/j.1467-6419.2010.00637.x
Russell, A. (2018). US senators on Twitter: Asymmetric party rhetoric in 140 characters. American
Politics Research, 46(4), 695–723. https://doi.org/10.1177/1532673X17715619
Sältzer, M. (2020). Finding the bird’s wings: Dimensions of factional conflict on Twitter. Party
Politics, 1–10. Online First. https://doi.org/10.1177/1354068820957960
Schattschneider, E. E. (1960). The semi-sovereign people: A realist’s view of American democracy.
Rinehart and Winston.
Sciarini, P., & Tresch, A. (2019). The political agenda-setting power of the media: The
Europeanization nexus. Journal of European Public Policy, 26(5), 734–751. https://doi.org/10.
1080/13501763.2018.1458890
Sciarini, P., Tresch, A., & Vliegenthart, R. (2020). Political agenda-setting and-building in small
consensus democracies: Relationships between media and parliament in the Netherlands andSwitzerland. The Agenda Setting Journal, 4(1), 109–134. https://doi.org/10.1075/asj.19004.sci
Selects. (2020). Panel survey (waves 1–3) – 2019 [dataset]. Distributed by FORS. https://doi.org/10.
23662/FORS-DS-1184-1
Shapiro, M. A., & Hemphill, L. (2017). Politicians and the policy agenda: Does use of Twitter by the
US congress direct New York Times content? Policy & Internet, 9(1), 109–132. https://doi.org/10.
1002/poi3.120
Somer-Topcu, Z. (2015). Everything to everyone: The electoral consequences of the broad-appeal
strategy in Europe. American Journal of Political Science, 59(4), 841–854. https://doi.org/10.1111/
ajps.12165
Soontjens, K. (2020). The awareness paradox: (Why) politicians overestimate citizens’ awareness ofparliamentary questions and party initiatives. Representation, 1–20. Online First. https://doi.org/
10.1080/00344893.2020.1785538
Strom, K. (1990). A behavioral theory of competitive political parties. American Journal of Political
Science, 34(2), 565–598. https://doi.org/10.2307/2111461

22

F. GILARDI ET AL.

Sudulich, L., & Trumm, S. (2019). A comparative study of the effects of electoral institutions on
campaigns. British Journal of Political Science, 49(1), 381–399. https://doi.org/10.1017/
S0007123416000570
Vliegenthart, R., Walgrave, S., Baumgartner, F. R., Bevan, S., Breunig, C., Brouard, S., Bonafont, L. C.,
Grossman, E., Jennings, W., & Mortensen, P. B. (2016). Do the media set the parliamentary
agenda? A comparative study in seven countries. European Journal of Political Research, 55(2),283–301. https://doi.org/10.1111/1475-6765.12134
Walgrave, S., Soroka, S., & Nuytemans, M. (2008). The mass media’s political agenda-setting power:
A longitudinal analysis of media, parliament, and government in Belgium (1993 to 2000).
Comparative Political Studies, 41(6), 814–836. https://doi.org/10.1177/0010414006299098
Walgrave, S., & Van Aelst, P. (2006). The contingency of the mass media’s political agenda setting
power: Toward a preliminary theory. Journal of Communication, 56(1), 88–109. https://doi.org/10.
1111/j.1460-2466.2006.00005.x
Wolfe, M., Jones, B. D., & Baumgartner, F. R. (2013). A failure to communicate: Agenda setting inmedia and policy studies. Political Communication, 30(2), 175–192. https://doi.org/10.1080/
10584609.2012.737419
Wood, B. D., & Peake, J. S. (1998). The dynamics of foreign policy agenda setting. American Political
Science Review, 92(1), 173–184. https://doi.org/10.2307/2585936