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This paper develops a typology of argumentation strategies used in lobbying. Unlike in other strategic communication functions such as crisis or risk communication, such typologies have not been proposed in the sub-field of public affairs.

The article synthesises the strategic communication, political communication and policy studies literature and employs exchange theory to explain the communicative-strategic exchange in public affairs. It showcases its explanatory potential with illustrative examples from Big Tech lobbying.

The paper describes that categories of argumentation strategies that a public affairs professional will choose are based on the contingency of the issue, policy objective and lobbying objective. The descriptive typology will require empirical testing to develop further.

The paper describes how public affairs professionals influence public policy through their argumentation strategies, which sheds light on the usually opaque activities of lobbying.

The proposed typology is the first of its kind for the field of public affairs. Beyond, it contributes communication-scientific insights from a rhetorical tradition to strategic communication research and other social science fields where lobbying is studied, e.g. policy studies.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

The purpose of this study is to fill the gap by researching the direct effects of media and personal characteristics on online participation in climate change, indirect effects when mediated by interpersonal communication and personal characteristics as predictors of media communications as sources of information about climate change.

A structured questionnaire is distributed to collect data about the uses of communication sources and online responses toward climate change by using a quota sampling technique. The structural equation modeling by using Smart PLS 4 is used to explore the effects’ size.

Small levels of direct and indirect effects are found. Direct effects are found in online newspapers, YouTube, television news, personal relevance toward climate change and political interest in online participation in climate change. Indirect effects are found of WhatsApp on online climate participation through interpersonal communication. Personal relevance toward climate change has motivated respondents to take information about climate change from Facebook. Climate skepticism is found among respondents who have received information from television news/talk shows, printed newspapers and WhatsApp.

University teachers in Pakistan will have to work on educational strategies to increase the knowledge of university students about energy generation through carbon and renewable energy sources.

The results of this study highlight the communicative-cultural dimensions of online discourse about climate change in the context of the less-researched country of Pakistan. This is the first study of researchers’ knowledge that comprehensively defines the digital media ecology in the context of climate change considering Pakistan.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.

Purpose: The main purpose of the study is to understand the factors that facilitate correction behavior among individuals. In this study the authors examine the impact of self-perceived media literacy (SPML) and reflection on participants’ correction behavior.

Methods: Data for the study were collected from Amazon's MTurk using an online survey. Data were collected after a certificate of exemption was received by the Institutional Review Board in a research university in the United States (US) Qualtrics software was used to collect data. The total number of participants was 797.

Findings: The findings show that although both SPML and reflection are positively associated with rumor refutation, higher SPML alone is not enough. Reflective judgment is critical for individuals to take part in this behavior online, such that individuals with higher reflective judgment indicated that they refute rumors online, irrespective of their SPML score.

Originality: The authors tested the relationship of multiple variables with participants correction behavior. Although research shows the importance of social correction, there is not much knowledge about what facilitates actual misinformation correction.

This study examines the effect of social media marketing on voting intention applying a combination of fuzzy logic methodology and a multidimensional panel data model.

The study adopts a multidimensional panel data method that includes several fixed effects. The dependent variable is a multifaceted construct that measures the participants’ intention to vote. The independent variables are electronic word of mouth (eWOM), customisation (CUS), entertainment (ENT), interaction (INT), trendiness (TRD), candidate’s perceived image (CPI), religious beliefs (RB), gender and age. The grouping variables that signify fixed effects are employment status, level of education, mostly used social media and religion. First, the significance of said fixed effects was tested through an ANOVA process. Then, the main model was estimated, including the significant grouping variables as fixed effects.

Employment status and level of education were significant fixed effects. Also, eWOM, ENT, INT, CPI, RB and gender significantly affected participants’ voting intention.

Being based on a questionnaire that asked participants about how they perceive different aspects of social media, the present study is limited to their perceptions. Therefore, further studies covering the voters’ behaviour in action could be efficient complements to the present study.

The findings could guide the political parties into prioritizing the aspects of social media in forming an effective campaign resulting in being elected.

The findings have the potential to help the public in making better informed decisions when voting. Furthermore, the results of this study indicate applications for social media which are beyond leisure time fillers.

Fuzzy logic and multidimensional panel data estimates are this study’s novelty and originality. Structural equation modelling and crisp linguistic values have been used in previous studies on social media’s effect on voting intent. The former refines the data gathered from a questionnaire, and the latter considers the possibility of including different grouping factors to achieve a more efficient and less biased estimation.

The study aims to access, monitor and visualize the scientific progress of Twitter-based research through a bibliometric analysis of scientific publications.

The data was retrieved from 2006 to February 23, 2022 using the Web of Science, a leading indexing and abstracting database. In response to the authors’ query, 6,193 items with 101,037 citations, an average citation of 16.31 and an h index of 126 were received. The “Biblioshiny” extension of the “Bibliometrics” package (www.bibliometrix.org) of R software was used to evaluate and visualize the data.

The present study highlighted the scientific progress of the field evolved over a period of time. The obtained results uncovered the publication trends, productive countries and their collaboration pattern, active authors who nurture the field by making their contribution, prolific source titles adopted by authors to publish the literature on the topic, most productive language in which literature was written, productive institutions, funding agencies that sponsor the research, influential articles, prominent keywords used in publications were also identified which will aid scientists in identifying research gaps in a particular area.

This study comprehensively illustrates the research status of Twitter-related research by conducting a bibliometric analysis. The study’s findings can assist relevant researchers in understanding the research trend, seeking scientific collaborators and funding for their research. Further, the study will act as a ready reference tool for the scientific community to identify research gaps, select research topics and appropriate platforms for submitting their scholarly endeavors.

This study examined how likeminded Facebook comments with incivility and without supporting evidence influence readers' evaluations of the commenter and online political participation intention. This study also investigated whether the indirect effect of exposure to uncivil comments and comments without evidence on online political participation through evaluations of the commenters is contingent on the strength of partisanship.

The study used a 2 (uncivil comments vs civil comments) × 2 (comments without supporting evidence vs comments with supporting evidence) factorial design with a scenario about reading similar viewpoints about the gun control issue in Facebook comments.

The results showed that compared to exposure to civil agreeing comments, exposure to uncivil likeminded comments resulted in higher levels of negative evaluations of the commenters, which in turn decreased willingness to participate in political activities online. Exposure to comments without evidence led to more negative evaluations of the commenters, but it did not significantly influence online political participation. In addition, the strength of partisanship did not significantly moderate the indirect effect of exposure to uncivil comments and comments without evidence on online political participation through evaluations of the commenters.

Although previous studies have demonstrated the significant effects of incivility and reasoned argument, little is known about whether and how people evaluate ingroup members' comments that are uncivil and lacking reasoned arguments. Most of these studies have examined incivility in political contexts, but few have extended the context to the effect of likeminded comments, especially when the comments are uncivil and lack supporting evidence. The current study aims to fill this gap in the literature.

The study investigates factors like media, tribal, religious and party politics' influence on Nigerias’ 2023 presidential election choice. It confirms dominant social media platforms and examines their influence on election polls, e-participation and political candidate choice. The main objectives of this study are to: investigate if tribal, religious and party politics affect the respondent’s choice of a presidential candidate, ascertain the respondent's most used social media platform for political engagement and determine how social media platforms influenced the election polls during the 2023 Nigerian presidential election.

A sample size of 384 registered voters was used to survey three states in Southeast Nigeria hinged on the technological acceptance model, the instrumentalist theory of ethnicity and the theory of reasoned action.

The study found that tribal politics did not influence political candidates during the 2023 Nigerian presidential election. However, religious and party politics influenced their choices as well as X (Twitter), found as the most used and most influential social media platform vital for enhancing participatory democracy and informing people at real-time.

The researchers experienced challenges such as ensuring that the respondents filled the questions appropriately to reduce the number of void questionnaires and a funding problem since they had yet to receive any grant to enhance the study.

The study commends improved Internet connectivity and accessibility among the citizens for increased political engagement on social media. It also recommends that the Nigerian government enforce the rule of law in politics to enable diverse tribes and religions to experience democratic e-participation and development without marginalisation or subjugation by incumbent power. The findings affirm that social media is apt in political communication during the 2023 presidential elections in Nigeria. The study is a contribution to knowledge, timely and original.

The purpose of this paper is to understand communication strategies formed by multinational subsidiaries in a transition economy during disruptive events. The authors develop and test a framework based on political realism and situational crisis communication theory (SCCT).

The authors collect and analyze communication statements from multinational subsidiaries located in Russia and made in the first two months (March–April, 2022) of the Russia–Ukraine conflict.

This study’s main findings are twofold. First, this study extends SCCT by showing that multinational subsidiaries use communication strategies that go beyond the traditional categories of diminish, rebuild and bolster. In particular, this study identifies so-called “shut the door” and “burning bridges” methods as possible industrial and home country contingent communication strategies. Second, this study shows that possession of a political realism lens provides us with powerful communication strategies made requisite during disruptive events.

The results provide practical hands-on advice for subsidiary managers on how to communicate effectively and efficiently during disruptive events such as the one described. This study offers novel communication strategies that help to understand the wider implications for managers in both home and host countries.

To the best of the authors’ knowledge, this is one of the first papers to apply SCCT and political realism to a current disruptive event for multinational enterprises, i.e. the ongoing Russia–Ukraine conflict. In that context, this study expands both perspectives by highlighting their complementarities and their conceptual boundaries. The authors can base those insights on two unique and purpose-built databases of multinational subsidiary characteristics in Russia-proper.