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Abstract

Prior research highlights broad democratic benefits of sustained public trust in the government, and the confidence that the government performs responsively addressing citizens’ problems (i.e., unemployment, cost of living). As social media enhances citizens’ opportunities to interact with journalists and elected officials, little is known about these communication effects on people’s government trust, and citizens’ evaluations about how well the government is addressing important society problems. Relying on a two-wave US representative panel survey data, this study builds on prior literature to introduce Twitter Communication with Democracy Actors: journalists and politicians, as a single, yet two-dimensional construct. Then, advancing different ordinary least squares (OLS) predictive panel models, results indicate that people who interact with democratic actors on Twitter trust the government and assess its overall functioning more positively. Additional moderating tests indicate social media interactions with democracy actors help citizens who might need it the most, specifically those who have lower levels of external political efficacy. The study provides theoretical implications of findings and suggestions for future research.

Keywords

government trust government performance journalists twitter interaction elected public officials Twitter interaction external political efficacy

The erosion of public trust in government is a long-lasting institutional challenge, especially in Western democracies (Dalton, 2005; Inglehart & Abramson, 1995), where trust in government fairly constitutes a central tenet of democratic systems (Warren, 1999). Recent data from the Pew Research Center show that in 2021 only about one-quarter of Americans trust the government to do what is right. The decline of both trust in government and government performance represents a growing drawback as it intensifies a climate of “political malaise” (Im et al., 2014), disenchantment (Offe, 2006), and dissatisfaction (Hetherington, 1998), increasing the gap between citizens and their representatives and reducing citizens’ political engagement (Sharoni, 2012; Torcal & Montero, 2006).

For the most part, distrust levels to democratic agents are multifold and operate at distinct levels, including individual and institutional (Chanley et al., 2000; Kestilä-Kekkonen & Söderlund, 2016), which directly speaks to Easton’s (1965) classic distinction between diffuse and specific support. This prior research has largely shown that individual appraisals about trust in governments and their performance are typically framed on individual judgments on how public policies respond to citizen’s demands and expectations (Bouckaert & Van de Walle, 2001; Gamson, 1968; Norris, 2011). Some authors argue that positive appraisals of governments’ performance influences citizens’ overall trust in the government (Kampen et al., 2003; van Ryzin, 2007, 2011). Others consider the possibility that this relationship might be bidirectional, carrying a mutually reciprocal association (Vigoda-Gadot & Yuval, 2003). Regardless of how they relate to each other, the literature generally poses that democracies are more sustainable and healthier when the public holds robust levels of trust on the government, and their performance (Warren, 1999), even when government performance might be different for each citizen based on their perceptions and expectations (Bouckaert & Van de Walle, 2001).

Several studies showed that information from social media may yield a positive effect enhancing and restoring public trust in governments and their performance (Moon, 2003), by facilitating democratic power-checks, transparency, and overall accountability of the political process (Feenstra & Casero-Ripollés, 2014). Scholars have long regarded such digital ecologies as key democratic public spaces (Dahlgren, 2005; Gruzd & Roy, 2014), in which democratic actors can partake in democratic discussion and deliberations under similar conditions. Particularly relevant is Twitter, a platform for political expression, discussion, and interaction with democratic actors (Dolezal, 2015; Lilleker et al., 2011; Vaccari et al., 2015), in which active efforts by political institutions, representatives, and journalists to spread and explain government actions are being currently made, especially in liberal democracies.

Twitter is also a platform particularly favorable for trolling (Bulut & Yörük, 2017), malevolent, or destructive behaviors, thus raising important risks and challenges for journalists and politicians’ public profiles. Among these risks lies what Quandt (2018) theorizes as “dark participation” or Theocharis et al. (2020) as “online incivility,” taking forms of deviant behaviors such as threats and insults, hateful comments, manipulation of forums, harassment, and hostility, among many other behaviors. Many of these “attack tweets” are commonplace during electoral campaigns and are effective generating voter reactions, attracting a significantly higher number of both critics and supporters, as well as retweets (E.-J. Lee & Xu, 2018). In fact, according to Rossini et al. (2023), these types of attack messages generate stronger engagement and more attention from audiences.

On this backdrop, there are active efforts in the use of Twitter by political institutions, representatives, and journalists to spread and explain government actions. Many have analyzed the effect of the use of Twitter on the evaluation of different kinds of representation, for example, showing a better opinion of politicians when the citizens exchange tweets about their policy preferences rather than private (personal photos or celebrations) tweets (Giger et al., 2021). Other work pointed out a positive effect of Twitter interactions between politicians, journalists, and citizens with regard to a political agenda-building process (Parmelee, 2014; Skogerbø & Krumsvik, 2015). Likewise, these types of communication exchanges on Twitter and the information that politicians post in their own Twitter also foster the overall credibility of politicians (Hwang, 2013), and mobilize citizens, becoming increasingly politically active (Halpern et al., 2017; Xenos et al., 2014). These communication processes are not independent from contexts of hostility and attacks that can lead citizens, politicians, and journalists to control their degree and style of interactivity in Twitter. However, as highlighted by Legard (2022), responsive interactions within social media involve elements of both control and responsiveness. However, there is a gap in the literature when assessing the effect of information and communication via Twitter with lay publics and democratic actors (journalists and politicians) on government performance and overall government trust. This study seeks to close that gap. As we develop this goal, we also expect that government’s trust and performance assessment may be moderated by citizens’ external political efficacy. Previous research has found a significantly and positively relationship between political trust and different political attitudes such as political efficacy (Catterberg & Moreno, 2006; Finkel, 1985; Niemi et al., 1991), so external political efficacy, as an individual trait, may moderate the effect of Twitter interactions on evaluations about the government.

Based on two-wave panel survey from the United States, this study first offers a confirmatory factor analysis to show how citizens interacting on Twitter with politicians and journalists represent a valid single but bidimensional construct: Twitter democracy actors’ interaction. Although journalists and politicians may be different kinds of actors, both represent influential information and expertise sources for citizens. Second, taking advantage of our panel causal order design, we carried out a series of ordinary least squares (OLS) regression (cross-sectional, lagged, and autoregressive) to test the explanatory power of these Twitter interactions with democracy actors, as well as the moderating role of external political efficacy over citizen’s trust in government and positive appraisal of government performance.

Results show that both Twitter interactions with democracy actors and external political efficacy are directly associated with people trust in government and performance. Furthermore, external political efficacy moderates the effect of Twitter interaction on both variables. That is, interacting with democracy actors helps people with low external efficacy the most to trust the government and to assess more positively the government’s performance. These findings suggest that communication and information exchanges across different actors in a public digital space such as Twitter are key factors in reducing the erosion of public trust.

Interacting With Democratic Actors on Twitter, Trust in Government, and Government Performance

In the last decade, Twitter has become a growing platform for citizens to express, connect, and interact with politicians and elected officials (Frame & Brachotte, 2015; Golbeck et al., 2010; Grant et al., 2010; E.-J. Lee et al., 2020; E.-J. Lee & Shin, 2012; Lyons & Veenstra, 2016), and journalists alike (Gil de Zúñiga et al., 2018; Hermida, 2010; Molyneux & Mourão, 2019). Prior research has characterized Twitter as a useful tool for politicians to consult their constituents (Karpf, 2018), and for journalists to pulse public opinion to closely connect citizens to politics and political actors (McGregor, 2019).

Similarly, as these democratic actors connect and interact with citizens, Twitter has become a horizontal communication space that homogenize the roles of journalists and politicians, shaping, structuring, and highlighting specific political issues (Metag & Rauchfleisch, 2017), and helping the elaboration of the public agenda (Barberá et al., 2019). In this process, tweets represent a novel source of information, a channel for social communication, and a space for politics to either disseminating political opinions or spurring political actions (Skogerbø & Krumsvik, 2015). Moreover, with the growing advancement of AI and the autonomous agency of social media algorithms (Gil de Zúñiga, Goyanes, et al., 2023), Twitter has the potential to reach massive and heterogeneous audiences. This enables political actors to engage with and integrate diverse networks into their discussions and political plans (C. S. Park, 2013).

So far, prior literature has mainly theorized about three different aspects about the use of Twitter by politicians and professional journalists: (a) the motivations for their use (Colliander et al., 2017; Graham et al., 2016; Hameleers, 2021), (b) the relevance of contextual and systemic variables in affecting that use (Humprecht et al., 2022; Tromble, 2018a), and (c) the significance and meaning of such interactions (Metag & Rauchfleisch, 2017).

Regarding the first strand of the literature, authors have emphasized the influence of politicians’ use of their (private or professional) Twitter to boost their popularity and recognition (Colliander et al., 2017; J. Lee et al., 2018), and to motivate, rally, and organize voters and activists (Graham et al., 2016; Jungherr, 2016; E.-J. Lee & Oh, 2012). Likewise, research has suggested that many journalists use of Twitter to build personal brands, one of them being a brand of institutional service to democracy (Molyneux & Holton, 2015).

Taking into account the second subject, prior studies have compared how context and system-level dynamics influence the relationship between politicians and constituents through Twitter, and how system-level variables affect journalists’ work too. For instance, in a comprehensive review, Jungherr (2016) identified some variables related to the electoral context such as the intensity of competition, electoral cycles, number of parties, or candidates in opposition, as potential factors affecting the use of Twitter in different countries. Linked to this, contrasts between the presidential versus parliamentarian system, the majoritarian electoral system versus the proportional one, as well as the political party type, have all been suggested to influence politicians’ digital communication strategies (Tromble, 2018a). This macro-level context has similarly been examined in journalists’ work, considering how the type of media system (i.e., polarized pluralist, democratic corporatist, or liberal) influences their work. For example, journalistic professionalism is more frequent in countries with lower state support than democratic corporatist (Humprecht et al., 2022).

Finally, regarding the third aspect, prior literature documented that the communication between journalists, politicians, and citizens makes the information of political decisions and political news closer and direct (de Vreese et al., 2018). Citizens who interact on Twitter with democracy actors have more information about the political processes, facilitating political decisions (Metag & Rauchfleisch, 2017). In fact, politicians may gain further positive evaluations from lay citizens when actively engaging with followers because voters may feel that politicians are listening to their opinions (Hwang, 2013). At the same time, audiences show positive evaluations about the personalization and interactivity with journalists that this platform facilitates (E.-J. Lee et al., 2020).

However, research does not only point to the interaction of journalists or politicians with citizens as relevant, but also when politicians and journalists converse in a public space. “Journalist and politicians are mutually dependent on each other” (Verweij, 2012, p. 690). Politicians serve as information sources for journalists, and the latter function as news and information collectors and spreaders for politicians. Both use different Twitter interaction mechanisms (i.e., hashtags, likes, retweets, mentions, or dialogues) to exchange information based on reciprocity and mutual feedback. What is more important, reciprocal engagement via this platform may help to “increase people’s trust in policymakers and democratic processes” (Tromble, 2018a, p. 225). Accordingly, and drawing on this previous theoretical background, it stands to reason that citizens’ interaction exchanges with political actors may positively affect their trust in the government. In a formal hypothesis:

Hypothesis 1 (H1). Twitter interaction frequency with democracy actors (journalists and politicians) will be positively associated with people’s trust in government.

As previously elaborated, government trust sets on more diffuse support as opposed to government performance evaluations, which may be categorized as specific (Citrin, 1974; Hetherington, 2005; A. H. Miller, 1974; Seligson, 1983). Scholars have long argued that public discontent mainly relates to dissatisfaction with the incumbents in office, thus entailing the perceptual evaluation of government and political elites’ performance as specific support (Montero & Morlino, 1993), which are accordingly subjected to variance. Other scholars complemented these studies and focused instead on the political system itself (Muller et al., 1982), suggesting that public attitudes toward the system are typically more constant, thus offering indirect evidence about the legitimacy of a political system and its institutions as diffuse support. All in all, individual appraisals about trust in governments and their performance typically involve judgments about how public policies respond to citizen’s demands and expectations (Gamson, 1968). In this sense, understanding the impact that interactions with politicians and journalists on Twitter may yield to shape these judgments becomes especially relevant. Considering that Twitter interactions enable greater amount of information about public policies, government and political institutions performance, and processes, this study aims to substantiate whether these elements also have an impact on the assessment over government performance.

Government performance may be measured by objective (van der Meer & Hakhverdian, 2017; van Erkel & van der Meer, 2016) and subjective indicators (Glaser & Denhardt, 2000). Objective instruments usually are related to macro-performance such inflation, unemployment, or gross domestic product (GDP) growth, while subjective indicators measure perceptions, evaluations, and personal experiences about the government performance of these macro-issues (Kroknes et al., 2015; Kumlin, 2011). In this case, we consider subjective perceptions about that performance because “the subjective evaluation, as the perception and expectation arguments go, is conceptually closer to the perception of trust” (Yang & Holzer, 2006, p. 119). Accordingly, we can compare the effect of Twitter interactions on both similarly subjective phenomena. Specifically, we measure the performance as an index of the perception of government performance on the following issues: extent of crime, unemployment, the difference between rich and poor, and the cost of living. All of which have been consistently shown to be systematically vital concerns in society that the Government requires to address (Demers et al., 1989; Sagarzazu & Klüver, 2017).

Recent communication studies have found that social media users leverage these platforms to maintain an accountable political process control over political leaders (Feenstra & Casero-Ripollés, 2014). In turn, politicians and journalists’ information about the design and the implementation of important issues on Twitter “may help correct biased public perception by narrowing the information gap between the public and governments” (Welch et al., 2005, p. 375). Citizens may know more about the electoral priorities of candidates and about the alternatives they provide to solve those social problems, facilitating a closer relationship with them (Graham et al., 2013; Guerrero-Solé, 2018; Lassen & Brown, 2011). Similarly, public administration research reflects that e-government policies empower citizens and increase their overall satisfaction with political institutions (Karlsson et al., 2012). Thus, the citizen’s use of information and communication technologies (ICTs) allows electronically access to government information in relation to decisions and services, taking part in the decision-making process, as well as “governments strengthen their legitimacy by generating more acceptable policies and satisfactory services” (Zahid & Karkin, 2013, p. 418). It stands to reason that when citizens interact with journalists and political representatives on Twitter, they will have a better understanding of these issues, as well as the efforts and difficulties democratic political actors go through to perform their roles. Thus, citizens should also be inclined to positively assess their performance. As a hypothesis:

Hypothesis 2 (H2). Twitter interaction frequency with democracy actors will be positively associated with a supportive government performance assessment.

External Political Efficacy, Trust in Government, and Government Performance

For decades, political efficacy has been established as a relevant concept in social sciences, generally used by theories of political and electoral participation (Blais & St-Vincent, 2011; Finkel, 1985; Gallego & Oberski, 2012), theories of responsiveness (Esaiasson et al., 2015; B. C. Hayes & Bean, 1993; Kölln et al., 2013), or theories of democracy (Craig et al., 1990; Dyck & Lascher, 2009; Mendelsohn & Cutler, 2000). But it has also been a controversial concept associated with discrepancies on the items to measure it and the operationalization of other concepts like political trust and government responsiveness or performance (Acock & Clarke, 1990; Chamberlain, 2012; Clarke et al., 2010; B. C. Hayes & Bean, 1993; Morrell, 2005).

One of the arguments in this controversial debate is that some studies consider external political efficacy closely relates and empirically captures the same construct as political trust. Key studies distinguish between internal and external political efficacy (Balch, 1974; Craig et al., 1990; B. C. Hayes & Bean, 1993; Lane, 1959). Accordingly, “Internal efficacy is the individual’s belief that means of influence are available to him. External efficacy is the belief that the authorities or regime are responsive to influence attempts” (Balch, 1974, p. 24). Political trust, in general, relates to the way citizens perceive the representative political system to be functioning.

In some studies, especially analyses which used the original measurement scale, there was an ambiguity “whether respondents are being asked to express their attitudes towards government institutions in general, the political performance of incumbent authorities, or something else” (Craig et al., 1990, p. 291). In these analyses, indicators of diffuse support and specific support were mixed with issues of external political efficacy. However, other scholars have argued that external political efficacy is distinct from political trust (Craig et al., 1990; Geurkink et al., 2020) because some external political efficacy indicators capture perceptions of individual political empowerment, while political trust measures how individuals feel that institutions and elites fulfill their expectations. In this work, we use only the items of external political efficacy applied in previous studies (Craig et al., 1990), thus capturing the belief that the public can influence political process or outcomes (W. Miller et al., 1980): “People like me don’t have any say in what the government does,” and “No matter whom I vote, it won’t make a difference” (both recoded).

Prior literature has reached mixed results on the relationship between political efficacy and trust in government. On the one hand, those who document a positive relationship suggest that high political efficacy is associated with greater trust in government and participation in politics (Finkel, 1985; H. B. Park, 2015). On the other hand, other work suggests that trust in government and political efficacy may not be related, hinting they may be orthogonal constructs. For example, an “individual may have full confidence in the proper functioning of the government but believe that it is not necessary for an individual’s opinion to be considered in political decision-making” (Sharoni, 2012; p. 122). Despite these somewhat mixed results, recent work has consistently provided valuable arguments as to why individual perceptions such as political efficacy facilitate trust in government (Bennett, 2006). For instance, in a comprehensive study spanning over 20 years of data trends across more than 30 societies, Catterberg and Moreno (2006) showed a robust positive relationship between a variety of democratic attitudes, including external political efficacy and government trust. The rationale is that external efficacy captures a sense of governmental responsiveness, and thus, authors anticipated that having “favorable orientations toward political authority will be positively related to favorable evaluations of government performance” (p. 42). Similarly, a survey study based on Denmark, Finland, Sweden, China, Japan, and Korea also established a positive relationship between external political efficacy, as opposed to internal political efficacy, and government trust, partly due to how external efficacy connects individuals’ needs with a sense of satisfaction and trust toward the government (H. B. Park, 2015). Accordingly, we propose the following hypothesis:

Hypothesis 3 (H3). External political efficacy will be positively associated with people’s trust in government.

Building on this debate, extant work renders some overlapping between the indicators of external political efficacy and those used to measure perceptions of government responsiveness and performance. Craig et al. (1990) and Clarke et al. (2010) define external political efficacy and government responsiveness as interchangeable concepts. Thus, citizen’s judgment of the political system responsiveness is linked to their own sense of competence in political life (B. C. Hayes & Bean, 1993). External efficacy is measured considering individual values and past experiences with government’s responsiveness (Chamberlain, 2012; Mishler & Rose, 2001). However, other authors suggest that perceptions toward government competence need to be measured as a separate construct than external political efficacy (Gil de Zúñiga et al., 2017; Klingemann, 1999; Kölln et al., 2013). Following Klingemann (1999), we consider government performance as an evaluative attitude, different from other affective beliefs or expectations that determine the perception of this performance. Specifically, we take into account the evaluation of the performance on the following issues: extent of crime, unemployment, the difference between rich and poor, and the cost of living.

Seminal work in the 1990s begun to clarify the relationship between efficacy and government performance, particularly at the local level. For instance, DeHoog et al. (1990) revealed that citizens with higher levels of local political efficacy are more satisfied with local governance. These findings are similar to Ulbig (2008) who highlights the relevance of external political beliefs linked to policy support as “feelings of policy satisfaction and political trust are increased only when respondents believe citizens had both increased voice and influence” (p. 523). Likewise, Wolak’s (2018) work suggests that people who believe that they have say or are capable of influencing politics will consider more rightful the actions of politicians and evaluate the outcomes of government as legitimate. Therefore, we expect that those who consider that, as actors of the political system, may have an influence on the political process or government outcome will also evaluate government’s performance assessment more positively.

Hypothesis (H4). External political efficacy will be positively associated with higher government performance assessment levels.

The Moderating Role of External Political Efficacy

In this study, we also consider external political efficacy as a moderating variable affecting the effect of Twitter interactions with democratic actors on government trust, and government performance. Considering prior studies that found a moderating effect of political efficacy between the association of social media use over political participation (H. B. Park, 2015), between Internet use and online or offline political participation (Hoffman et al., 2013), and between political ideology and policy preferences (Sulitzeanu-Kenan & Halperin, 2013), this study takes on this line of research and explores the moderating effects of political efficacy at conditioning the relationship of Twitter interactions with democracy actors over government trust and performance.

Our contribution, unlike the studies listed above that mostly focus on political participation, is to test whether this moderating role is also present when contrasting diffuse versus specific government support, such as government performance. Political efficacy, as underlying psychological antecedent, shapes citizens’ actions, perceptions, expectations, and evaluations (Hoffman et al., 2013). Thus, the positive association between Twitter interactions and government performance may be bolstered by levels of external political efficacy. We expect that internalized citizen’s beliefs and political empowerment moderate the relationship between Twitter interactions with politicians and journalists and government trust and performance. At the same time, this study also considers that role of external political efficacy could be different when Twitter interactions are related to more affective (i.e., trust) than evaluative attitudes (i.e., performance), because political efficacy and diffuse support may arise as are more stable, internalized, and affective beliefs (Balch, 1974; Niemi & Sobieszek, 1977; Pateman, 1971). Accordingly, we ask:

Research Question 1 (RQ1). Do people’s external political efficacy levels moderate the effect of Twitter democracy actors’ interaction on government trust?

Research Question 2 (RQ2). Do people’s external political efficacy levels moderate the effect of Twitter democracy actors’ interaction on government performance assessments?

Methods

Data Collection

Hypotheses and research questions were tested with US panel survey data administered in three different waves, two of which were used for this study (W¹ June 2019, n = 1,338; W² October 2019, n = 511). Data for this article come from a larger research project examining emerging media consumption patterns and political beliefs and attitudes (see Gil de Zúñiga et al., 2022). The survey instrument was created and distributed online via Qualtrics, while IPSOS, an international public opinion poll firm from (anonymized country), ensured the richness and heterogeneity of recruited participants. Specifically, the pooling company circulated the survey instrument to more than 3,000 panel members, mirroring US census in terms of key demographic measurements such as education, gender, or income. Between-waves time interval (4 months) is similar to comparable survey research with panel data, reducing attrition rate while considering causal, in-time effects. The cooperation rate for W¹ was 44.60%, while retention rate for W² was 38.19% (See Supplemental Table Appendix 1 for demographic breakdown and US census comparability). As noted above, there is a 3-year time gap between the year of the survey data collection and today. During this time, several changes have taken place, particularly in terms of the ownership and structure of the company (Twitter, now X). However, the use of the platform remains a valuable tool for analyzing citizens’ interaction. This platform still allows citizens to use DM and @mention as strategies to reach elected officials and elite journalists, just as they did in 2019.

Data were analyzed in SPSS in a series of cross-sectional, lagged, and autoregressive models. These analyses establish comparative benchmarks for future longitudinal designs that may use different time frames between waves, testing the association between the variables examined in this study with different model specifications. While cross-sectional analysis test associations measuring independent and dependent variables at the same time, the lagged models provide more robust findings addressing in-time effects, the autoregressive models (the most stringent) provide temporal causal order effects by measuring dependent variables in the second wave and the independent variables in the first one, controlling for prior baseline levels of the dependent variable.

To empirically test our hypotheses, this article employs autoregressive models (see Gil de Zúñiga, Marné, et al., 2023). These models predict the dependent variable based on the second wave of survey data, including not only a set of controls, but also the value of the dependent variable at Wave 1. Autoregressive models are increasingly becoming more common in the social sciences due to their ability to account stability effects over time while considering changes in the dependent variables (Adachi & Willoughby, 2015). Many behavioral outcomes are likely to be relatively stable over time, including prior levels of the dependent variable as a control removes a large part of the variance in the predicted outcome. The logic behind this is that the best predictor of future behavior is past behavior, and isolating and controlling for that effect would better capture other effects from potential independent variables of interest. Overall, one should consider small effects among the covariates included in autoregressive models, given that this type of test imposes stricter causal order relationships than would be the case otherwise (Huckfeldt et al., 2014).

To account for potential cofounds, different control variables were included in the analysis in blocks, as extant empirical research has pointed out that government trust and evaluations may differ due to sociodemographic factors, news consumption, trust orientations, and political antecedents (Strömbäck et al., 2016; Torcal, 2014).

Accordingly, age (Mdn = 3 [36–55]), gender (males = 46.7%; females = 53.3%), income (Mdn = 4 [$50,000–$99,999]), education (Mdn = 3 [Some college]), traditional news consumption (M = 4.57; SD = 1.84; Cronbach’s α = .85), social media news use (M = 3.63; SD = 2.07; Cronbach’s α = .91), media trust (M = 6.53; SD = 2.22; Spearman–Brown = .69), social trust (M = 5.09; SD = 2.22; Spearman–Brown = .85), party identification (M = 6.00; SD = 3.06), political ideology (M = 6.44; SD = 2.80; Spearman–Brown = .94), and political interest (M = 6.57; SD = 2.56; Spearman–Brown = .96) were introduced as control variables in all regression models. For exploring moderating effects, we used Hayes PROCESS for SPSS (A. F. Hayes, 2017; Model 1, 5,000 bootstrap samples). Variables, otherwise stated, are measured on a 10-point Likert-type scale. Zero-order correlations are reported in Table 1.

Table 1.

Wave 1: Zero-Order Correlations Among Key Independent and Criterion Variables in the Study.

Variables	1	2	3	4
1. External political efficacy	—
2. Twitter democracy actors’ interaction	.14***	—
3. Government trust	.20***	−.01	—
4. Government performance	−.33***	−.21***	−.06*	—

Note. Sample size = 1,338. Cell entries are two-tailed zero-order correlation coefficients. Pearson coefficients based on bootstrapping to 5,000 samples with confidence intervals set at 95%.

p < .05. **p< .01. ***p< .001.

Measurements

Twitter Democracy Actors’ Interaction

Based on literature on citizens’ interaction with politicians and journalist in Twitter (Gil de Zúñiga et al., 2018; Tromble, 2018b), we designed a construct that measures how often citizens respondents ask questions “(via DM or @mention) to a member of the news media,” “(via DM or @mention) to a citizen journalist,” “(via DM or @mention) to an elected official,” and “(via DM or @mention) to a politician” (three items, M = 2.21; SD = 2.28; Cronbach’s α = .98). In Wave 1, the average percentage of respondents who reported having some type of interaction with journalists and politicians is about 35% in both cases.

Results of the confirmatory factor analysis reported in Table 2 indicate a similar structural fit for the one- or two-factor solution, partially penalizing the one-factor solution in the root mean square error of approximation (RMSEA). Despite this difference, we decide to use the collapsed measured to present a more parsimonious and less saturated model. Similarly, the foundation of democracy actors builds on the symbiotic and democratically beneficial relation between journalists and elected officials, with the general public being the third leg of democracy (Van Aelst & Aalberg, 2011; Verweij, 2012). Accordingly, future research may collapse both dimensions in one single-factor solution (as we did), or independently consider both, although variance inflation factor (VIF) values may be high, as assessed by the strong correlation between both factors (see Figure 1; r = .980; p < .001).

Table 2.

Confirmatory Factor Analysis Comparison of One-Factor Versus Two-Factor Models.

Model	χ² (df)	CFI	TLI	RMSEA	SRMR	Avg. loading	Min. loading
One factor	68.88*** (1)	.991	.974	.16	.006	.953	.944
Two factors	4.32* (1)	1.000	.997	.05	.001	.959	.952

Note. Factor loadings are standardized. Chi-square test of model fit for the baseline model: χ² = 7,810.199, df.

p < .05. **p< .01. ***p< .001.

Figure 1.

Confirmatory factor analysis for democracy actors Twitter interaction with its two dimensions: journalism and public-elected officials Twitter interaction.

Government Performance

Based on the literature on agenda setting, political dissatisfaction, and issue attention (Demers et al., 1989; Sagarzazu & Klüver, 2017), we measure the performance as an index of the respondents’ perceptions about government performance on the following issues: “extent of crime,” “extent of unemployment,” “the difference between rich and poor,” and “the cost of living” (four items, M = 4.72; SD = 2.23; W¹ Cronbach’s α = .89; M = 4.72; SD = 2.22; W² Spearman–Brown = .89).

Government Trust

This construct computes citizens’ feeling or trust toward the following institutions (Mari et al., 2022): the “government” and the “political system in the USA” (M = 4.12; SD = 2.29; W¹ Cronbach’s α = .87; M = 4.73; SD = 2.18; W² Spearman–Brown = .88).

External Political Efficacy

Building on previous measurement of the construct (Bernardi et al., 2022), we asked respondents their agreement or disagreement with the following statements: “People like me don’t have any say in what the government does” and “No matter whom I vote, it won’t make a difference.” Both variables were recoded to implement the analysis (M = 6.14; SD = 2.53; W¹ Spearman–Brown = .76).

Results

To test our hypothesis and answer research questions, we ran a series of cross-sectional, lagged, and autoregressive OLS regressions. The first hypothesis predicted a positive association between Twitter interaction with democracy actors and people’s trust in government. In the three regression models reported in Table 3, our first hypothesis was empirically supported, after controlling for demographics, media antecedents, trust antecedents, political antecedents, and even government trust levels in Wave 1: (a) cross-sectionally (β = .104, p < .01; total R² = 34.2%), (b) lagged (β = .199, p < .001; total R² = 22%), and (c) autoregressive models (β = .149, p < .001; total R² = 52.2%).

Table 3.

Cross-Sectional, Lagged, and Autoregressive Regression Models Testing External Political Efficacy, Twitter Interaction With Democracy Actors,^a and Trust in Government.

	Government trust (Cross-sectional)	Government trust (Lagged)	Government trust (Autoregressive)
Block 1: Autoregressive term (W¹)
Trust in government	—	—	.655***
∆R² (%)			48.4
Block 1: Demographics (W¹)
Age	−.066*	−.066	.008
Gender (female)	.001*	−.132**	−.097**
Education	.015	.043	.051
Income	−.014	−.015	−.034
Ethnicity (White)	−.048	.045	.059
∆R² (%)	3.5	3.9	1
Block 2: Media antecedents (W¹)
Traditional news	.109**	.099	.038
Social media news use	.142***	.063	.003
∆R² (%)	17.9	9	0.7
Block 3: Trust antecedents (W¹)
Media trust	.177***	.202***	.059
Social trust	.300***	.134**	−.069
∆R² (%)	11.2	4.4	0.3
Block 4: Political antecedents (W¹)
Party identification	.071	.057	.001
Political ideology	.032	.012	−.013
Political interest	−.043	−.121*	−.065
∆R² (%)	0.9	1.1	0.2
Block 4: IV of interest
External political efficacy	.055*	.136**	.076*
Twitter interaction	.104**	.199***	.149***
∆R² (%)	0.7	3.6	1.6
Total R² (%)	34.2	22	52.2

Note. Sample size = 1,338 (Wave 1); 511 (Wave 2). Cell entries are final-entry standardized beta (β) coefficients.

*p < .05. **p < .01. ***p < .001.

Democracy actors refers to a construct that measures both journalist and politician.

Likewise, H2 presumed a positive association between Twitter interaction with democracy actors and government performance assessment. Consistent with this hypothesis, the series of OLS regressions reported in Table 4 supported empirically this association: (a) cross-sectionally (β = .168, p < .001; total R² = 26.3%), (b) in time-lagged relationships (β = .193, p < .001; total R² = 24.7%), and (c) in panel autoregressive relationships (β = .141, p < .01; total R² = 37.1%). Thus, H2 was fully supported.

Table 4.

Cross-Sectional, Lagged, and Autoregressive Regression Models Testing External Political Efficacy, Twitter Interaction With Democracy Actors,^a and Government Performance.

	Government performance (Cross-sectional)	Government performance (Lagged)	Government Performance (Autoregressive)
Block 1: Autoregressive term (W¹)
Government performance	—	—	.421***
∆R² (%)			29
Block 1: Demographics (W¹)
Age	.032	−.051	−.059
Gender (female)	−.050	.060	.095*
Education	−.019	.000	.007
Income	−.014	.087	.079
Race (White)	−.014	−.056	−.039
∆R² (%)	0.7	2.3	1.8
Block 2: Media antecedents (W¹)
Traditional news	.109**	.190**	.161**
Social media news use	.125**	.004	−.048
∆R² (%)	9.5	6.2	2.2
Block 3: Trust antecedents (W¹)
Media trust	−.131***	−.128*	−.097
Social trust	.143***	.111*	.064
∆R² (%)	6.8	6.2	1.5
Block 4: Political antecedents (W¹)
Party identification	.107**	.028	−.024
Political ideology	.216***	.267***	.138*
Political interest	−.022	−.051	−.020
∆R² (%)	8	7.8	1.3
Block 4: IV of interest
External political efficacy	.029	−.019	−.039
Twitter interaction	.168***	.193***	.141**
∆R² (%)	1.3	2.2	1.3
Total R² (%)	26.3	24.7	37.1

Note. Sample size = 1,338 (Wave 1); 511 (Wave 2). Cell entries are final-entry standardized beta (β) coefficients.

*p < .05. **p < .01. ***p < .001.

Democracy actors refers to a construct that measures both journalist and politician.

However, when it comes to the association between external political efficacy and both government trust and government performance, results of regressions provided different results. On the one hand, H3 predicted a positive association between external political efficacy and trust in government, and the empirical tests reported in Table 2 support this statement. Across our series of cross-sectional (β = .055, p < .05), lagged (β = .136, p < .01), and autoregressive models (β = .076, p < .05), data empirically support our predictions, even after controlling for levels of government performance in Wave 1 (autoregressive model). On the other hand, however, results reported in Table 4 revealed that none of our predictions were empirically supported for H4, which stated a positive association between external political efficacy and government performance assessment. Accordingly, H4 was non-supported.

Finally, a series of moderating effects were run to answer RQ1 and RQ2. RQ1 explores the moderating role of external political efficacy over the relationship between Twitter democracy actors’ interaction and government trust. Results reported in Table 5 indicate a significant and negative moderating effect: (a) cross-sectionally (β = −.043, p < .001; ∆R² = 1.2%) and (b) in time-lagged relationships (β = −.057, p < .001; ∆R² = 2.2%). However, when it comes to the most stringent model, namely, the autoregressive, the regression revealed a non-significant association (β = −.018, p = .186). For the sake of parsimony, as the most stringent model is non-significant, we refrain to plot the moderating effect in the cross-sectional and lagged models.

Table 5.

Cross-Sectional, Lagged, and Autoregressive Moderating Effects Test.

	Government trust (Cross-sectional)	Government trust (Lagged)	Government trust (Autoregressive)
Block 1: All prior blocks Table 3
∆R² (%)	34.2	22	52.2
Block 2: Moderation/interaction
Twitter Democracy Actors’ Interaction^W1 × External Political Efficacy^W1	−.043 (.009)***	−.057 (.017)***	−.018 (.014)
∆R² (%)	1.2	2.2	0.2
Total R² (%)	35.4	24.2	52.4

Note. Estimates are unstandardized beta coefficients. Standardized errors between brackets. Interaction effects based on bootstrapping to 5,000 samples with bias-corrected confidence intervals. The effects account for the same demographic, political antecedents, and media orientation control variables as found in Table 2. Sample W¹ = 1,338; Sample W² = 511.

*p < .05. **p < .01. ***p < .001.

RQ2 explores the moderating role of external political efficacy over the relationship between Twitter democracy actors’ interaction and government performance. Results reported in Table 6 indicate a significant and negative moderating effect: (a) cross-sectionally (β = −.041, p < .001; ∆R² = 1.3%), (b) in time-lagged relationships (β = −.081, p < .001; ∆R² = 4.8%), and (c) in panel autoregressive relationships (β = −.060, p < .001; ∆R² = 2.5%).

Table 6.

Cross-Sectional, Lagged, and Autoregressive Moderating Effects Test.

	Government performance (Cross-sectional)	Government performance (Lagged)	Government performance (Autoregressive)
Block 1: All prior blocks Table 4
∆R² (%)	26.3	24.7	37.1
Block 2: Moderation/interaction
Twitter Democracy Actors’ Interaction^W1 × External Political Efficacy^W1	−.041 (.009)***	−.081 (016)***	−.060 (.015)***
∆R² (%)	1.3	4.8	2.5
Total R² (%)	27.6	29.5	39.6

*p < .05. **p < .01. ***p < .001.

In Figure 2, we plot the moderating effects of RQ2. As can be seen, the positive association between Twitter democracy actors’ interaction and government performance decreases as levels of external political efficacy increase. At both low and moderate levels of external political efficacy, there is a positive and statistically significant association between Twitter democracy actors’ interaction and government performance. However, at high levels of external political efficacy, this association is non-significant, as assessed by the visual inspection of the p values in the pick-a-point approach (A. F. Hayes, 2017).

Figure 2.

Autoregressive regression model interaction effect of external political efficacy (M) on the relationship between Twitter democracy actors’ interaction (X) and government performance (Y).

Discussion

The aim of this study was to provide diverse stringent models identifying the explanatory power of two fundamental attributes of well-functioning democracies: government performance assessment and government trust. Furthermore, this study theoretically and empirically furthers our understanding of this literature by examining the moderating role of external political efficacy at explaining the effects of people’s Twitter interaction with democracy actors. By expanding our understanding on the nature of these relationships, our study provides several inter-related contributions to better assess the potential muscle of Twitter as forum to reducing the gap between lay citizens, their democratic representatives, and journalists as public affair news providers. Our findings also contribute to the literature on political discontent and political disaffection through their connection with both diffuse and specific support. Interaction with politicians and journalists increases both specific and diffuse support, thereby reducing discontent and disaffection.

First, at an empirical level, our study broadly supports the work of Verweij (2012, p. 690), when suggesting that “journalist and politicians are mutually dependent on each other” to influence and inform the public. Our confirmatory factor analysis further supports this idea, confirming a bidimensional nature of both journalists and politicians Twitter interactions with citizens, yet being a potential unique construct of two highly correlated dimensions (r = .98; see Figure 1). This finding is empirically relevant, because some authors used the main effects of communicating with these democratic actors separately (Gil de Zúñiga et al., 2018; Guerrero-Solé, 2018; Hwang, 2013; McGregor, 2019), leaving a relatively unclear academic testing on their general impact as collapsed factor. In fact, our study shows that citizens’ interaction with democratic actors (politicians and journalists) on Twitter represents a pivotal path to increase citizens’ trust in government and government performance assessment. Accordingly, future scholars, politicians, and policy makers alike many consider this platform not only as an information source (Hermida, 2010; Molyneux & Mourão, 2019), but also, and above all, as an important ground to reach citizens and increasing government trust and government performance assessment to foster healthier democracies.

Related to this empirical contribution, the theoretical implication of testing H1 is relevant for both the fields of communication and political science. Mainly, it reveals that citizens’ interactions with democratic actors on Twitter should be partially reframed. Specifically, rather than being perceived and used as a strategic and contingent tool for political gain (Dolezal, 2015), for instance, during voting campaigns and personal branding (Giger et al., 2021), our study identifies broader contexts of Twitter impact, disjointed from the electoral or governmental cycle. Our findings revealed that Twitter interactions with democracy actors trigger a positive effect on government trust, thus suggesting that frequent Twitter exchanges build relative stable attitudes that facilitate and increase the citizens diffuse support in time. Accordingly, our study adds to the current literature about the potential power of governmental online communication (DePaula, 2023) with Twitter as engaging political communicative tool for elected officials. Thus, politicians may use this platform to give feedback, discuss, and explain political decisions, knowing that in turn, and over time, it can improve citizens’ trust and support.

Similarly, as supported by the test of our second hypothesis, the study’s results are in line with prior studies showing that democracy actors’ interaction on Twitter facilitate more direct, horizontal, and heterogeneous conversations with citizens (Metag & Rauchfleisch, 2017), in which both agents frame their messages in a reciprocal relationship in terms of issue attention and political congruence (Barberá et al., 2019). Twitters’ affordances to reach a wider and diverse audience arguably intensify democracy actors’ influence to distribute insightful information about political decisions or deliberations and their ulterior consequences for citizens’ daily lives. These informative inputs may facilitate citizens’ short- and long-term political knowledge acquisition about the complexity of making political decisions and, consequently, laying ground for more positive assessments over government actions.

Our study also lends support for a positive association between external political efficacy and trust in government (third hypothesis). Specifically, findings shed additional light to Catterberg and Moreno’s (2006) contribution by which they showed a positive association between a myriad of democratic attitudes (including external political efficacy), on government trust. Contributing to this field of research, our study provides results to replicate the idea that citizens with a strong belief that their decisions can shape politics, facilitating the feeling of involvement in political processes and deliberations (H. B. Park, 2015), can nurture, in time, the trust in their government.

Our final hypothesis (a positive association between external political efficacy and government performance assessment), although not empirically supported, provides interesting food for thought to further advance current discussions on existing literature on specific government support (van Ryzin, 2007; Yang & Holzer, 2006). On the one hand, our study complements prior studies (Gil de Zúñiga et al., 2017; Klingemann, 1999), showing that both constructs measure different perceptions and, on the other hand, that external political efficacy is not a significant predictor of citizens’ government performance assessment. In this regard, although prior scholarship has found a positive association between external political efficacy and both local government satisfaction (DeHoog et al., 1990) and general satisfaction with the functioning of democracy (Ulbig, 2008), our results nuance these findings and illustrate that this association does not hold significant in explaining government performance assessment. Accordingly, citizens’ assessments of government performance are not connected by their external political efficacy, suggesting that citizen’s specific support and beliefs about their political empowerment are disconnected.

Finally, as explored in the research questions, the moderating role of external political efficacy differs when Twitter interactions with democracy actors are related to more affective (i.e., government trust; non-significant) than evaluative attitudes (i.e., government performance; significant). Results of the moderation analysis revealed that the effect is statistically significant and negative for all models explaining the association between Twitter interactions with democracy actors and government performance assessment. In this regard, findings highlight that the positive effect of Twitter interactions over government performance assessment, and to some extent on trust, is moderated by levels of external political efficacy, especially when such levels are low. In other words, when people have high levels of external political efficacy, connecting and exchanging ideas with journalists and politicians on Twitter does not improve their trust levels or their positive government assessment. However, when people have low levels of external efficacy, the more they interact with democracy actors on Twitter, the more they will tend to trust the government and assess their performance more optimistically. In short, communicating with democracy actors on Twitter helps those who may need it the most: citizens with low levels of political efficacy.

All things considered, our study provides insightful theoretical and empirical contributions suggesting that both diffuse and specific support can be enhanced by citizens’ attitudes and behaviors. First, related to citizens’ behaviors, our findings further our understanding of Twitter as a potential deliberative space in which democratic actors such as journalists and politicians both can enhance citizens’ government trust and government performance assessment. Second, related to attitudes, it shows how citizens’ political empowerment can trigger a positive in-time effect on government trust, reducing the actual worrying climate of “political malaise.”

Our work has certain limitations due to the nature of our data, which prevented us from discerning which tweets originate from fake accounts or what leads to accounts being blocked, whether it be due to insults and harassment, or attempts to discredit the messages of journalists and politicians. In short, our measurement instrument does not discern positive and civil interactions to negative, critical, or even uncivil interactions with democracy actors. The effects found in this study are based on all possible contents. To address this limitation, future research may consider implementing a content analysis of tweets involving interactions between the public and journalists/politicians, and then survey respondents’ levels of government trust and performance.

We used an online panel data collection strategy and long Likert-type response formats to measure beliefs, perceptions, and behaviors. The evidence shows that Internet self-administered surveys are less susceptible to social desirability response bias (Holbrook & Krosnick, 2010; Persson & Solevid, 2014). However, it should be noted as a limitation that participants may misremember their interactions on Twitter or intensify their occurrence due to desirability bias. In addition to this, our research uses self-report measures as well since the main dependent variables (i.e., government trust and performance assessment) are “public opinion” variables and may not be easily captured otherwise.

Another limitation of our work is its focus on a single social media platform (Twitter) as a source of interaction. Cross-platform comparative studies are increasingly relevant for examining and exploring the nuanced effects that various social media platforms may have on political attitudes, perceptions, and behaviors. Therefore, future research should incorporate similar measurements for multiple platforms and compare whether interactive behaviors effects are similar or different across platforms according to their affordances.

Finally, other studies used different interactive affordances, such as liking, retweeting, or quoting to measure citizens’ interaction on Twitter. This Twitter behavior may align much more with either informational or political expressive uses of the platform (Vaccari et al., 2015), rather than unobtrusive engagement behaviors with democracy actors, leading to other potential (self-)effects (see Cho et al., 2018). Our study focuses on “DM” (Direct Message) or “@mention” to assess two of the most significant and pragmatic pathways to directly engaging with politicians and media elites. This methodological choice is motivated by our focus on understanding the direct effects of citizens’ interactions with democracy actors in the process of public conversations, rather than the exploration of the diffusion of information or expression through retweets or through “likes.” Thus, our study shows how the effects of engaging with democracy actors leads to pro-civic attitudes.

Supplemental Material

sj-docx-1-sms-10.1177_20563051241232907 – Supplemental material for Twitter Communication Among Democracy Actors: How Interacting With Journalists and Elected Officials Influence People’s Government Performance Assessment and Trust

Supplemental material, sj-docx-1-sms-10.1177_20563051241232907 for Twitter Communication Among Democracy Actors: How Interacting With Journalists and Elected Officials Influence People’s Government Performance Assessment and Trust by Homero Gil de Zúñiga, Manuel Goyanes and Araceli Mateos in Social Media + Society

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has benefited from the support of the Spanish National Research Agency’s Program for the Generation of Knowledge and the Scientific and Technological Strengthening Research + Development Grant PID2020-115562GB-I00.

ORCID iDs

Homero Gil de Zúñiga

Manuel Goyanes

Supplemental material

Supplemental material for this article is available online.

Author Biographies

Homero Gil de Zúñiga (PhD, Universidad Eurpea de Madrid; PhD, University of Wisconsin–Madison) is distinguished research professor at University of Salamanca where he serves as director of the Democracy Research Unit (DRU). He is also distinguished professor of media studies and AI at Pennsylvania State University, and senior research fellow at Universidad Diego Portales, Chile. In general, his work draws from theoretically driven research, aiming to shed an empirical social scientific light over how social media, algorithms, AI, and other technologies affect society and democracy.

Manuel Goyanes (PhD, University of Santiago de Compostela) serves as an assistant professor at Carlos III University in Madrid and as a visiting fellow at the Democracy Research Unit—DRU (University of Salamanca). His research addresses the influence of journalism and new technologies over citizens’ daily lives, as well as the effects of news consumption on citizens’ political knowledge and participation. He is also interested in global inequalities in academic participation, the systematic biases toward global South scholars, and publication trends in Communication.

Araceli Mateos (PhD, University of Salamanca) is associate professor in political science and administration area at University of Salamanca. Her lines of research are linked to the influence of political attitudes on political and electoral behavior, public opinion studies, and research methodology. She has published books, volumes and dozens of peer review journal articles on these issues, including American Politics Research, Quality & Quantity, Revista Internacional de Sociologia, and so on.

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