Twitter Communication Research Articles

Redefining “expansion” in congressional communication: Homestyles for a digital constituency

AbstractSelf‐presentation in Congress is more nationalized, with digital constituencies on platforms such as Twitter offering new reputation‐building opportunities. Digital constituencies transform traditional expectations of constituent communication, challenging Richard Fenno's theory that representatives adopt an “expansionist” style with constituents during early, vulnerable phases of their tenure. In a digital era that encourages a more national approach, we question how Fenno's distinction between expansionist and protectionist phases of connecting with constituents applies to reputation‐building with digital constituencies. While electorally vulnerable senators still seek to expand their presentation among geographic constituents, digital platforms, such as Twitter, incentivize them to protect their reputation among a digital constituency. We analyzed senators' Twitter communications from 2013 to 2023. We find the electoral vulnerabilities of Fenno's expansionist local model are associated with a protectionist model of engagement with digital constituencies on Twitter. Our study sheds light on the significance of online reputation management and its implications for representing a media‐influenced, modern Congress.

Twitter, Politics, and the Pandemic

This article examines the intersection of politics, government, and health communication in the Scottish Twittersphere during the Covid-19 outbreak. It captures two phases of the pandemic: the beginning of the health crisis, and the rollout of the vaccination programme, coinciding with the emergence of the Delta variant in Scotland. A combination of thematic, quantitative content and social network analyses is employed to identify key themes emerging from the tweets of selected government and politicians' accounts, and to explore the formation of social networks communities. The thematic analysis reveals that Twitter has primarily been used for disseminating information about the virus, preventative measures, and government interventions, with limited efforts towards public engagement. Twitter communications became increasingly partisan as the pandemic progressed, with users frequently using the crisis as a political proxy. Five major clusters were detected in the Twitter network: two highly partisan and polarised clusters; a group containing numerous news media accounts reporting on the pandemic, and two clusters focusing primarily on the vaccination programme and the provision of health information, where the First Minister and the Scottish Government operate. Implications of these findings for government and political communication in health crises are discussed.

Digital Mass Hysteria during Pandemic? A Study of Twitter Communication Patterns in the US during the Stages of COVID-19 Vaccination.

This study examined the public's sentiments about vaccines by analyzing Twitter data during the CDC's vaccination management planning stage in the United States. Sentiment scores were assigned to each tweet using a sentiment dictionary and the sentiment changes were analyzed over 52 weeks from November 2020 to November 2021. An interrupted time series model was used to analyze the difference in sentiment, which revealed that there was a shift. Initially, overall sentiments were negative but became positive as the stage of general vaccine supply approached. However, negative sentiments sharply rose when the vaccine supply transitioned to the phase of universalization. The results identified two dominant strategic action fields for vaccines providing polarized messages on Twitter and the negative trend was strong for most of the period. The findings highlight the importance of managing strategic action fields on social networks to prevent mass hysteria during vaccine policy implementation. This study stresses the significance of effectively managing strategic action fields on social media platforms to prevent mass hysteria while implementing vaccine policies.

She Speaks for Millions: The Emergence of Female Diplomatic Voices in the Russo-Ukrainian War

This research critically investigates the public diplomacy strategies deployed by a cohort of influential female European leaders on Twitter during the Russo-Ukrainian War of 2022-2023. The study comprises eight leaders - Kallas (Estonia), Marin (Finland), von der Leyen (President of the European Commission), Metsola (President of the European Parliament), Sandu (Moldova), Simonyte (Lithuania), Zourabichvili (Georgia), and Meloni (Italy) - representing millions of constituents. By mirroring the analytical attention given to Ukraine's President, Volodymyr Zelenskyy, this study scrutinizes the distinct approaches and differences in emotional, cognitive, and structural language use between these influential female figures and President Zelenskyy in their respective Twitter discourse during the crisis. By employing the Linguistic Inquiry and Word Count (LIWC)-22 dictionary, this research intends to uncover unique patterns and nuances in the language use of these leaders, thereby offering fresh insights into the gendered communication and diplomacy employed by the female cohort during conflicts. Keywords: Public diplomacy, Female leadership, Russo-Ukrainian War, Twitter communications, Linguistic Inquiry and Word Count (LIWC), Diplomacy, gendered communication

Tweet tweet tick: a quantitative content analysis of risk communication about ticks on Twitter

IntroductionThis study determined what information presented on Twitter (X) on tickborne diseases and their prevention and investigated the relationships between content information and engagement rate. The escalating incidence of vectorborne diseases, particularly those transmitted by ticks, has emerged as a significant public health concern. Communicating tick risks effectively to the public has become an urgent issue.MethodsA quantitative content analysis was used to examine tick-related contents to understand how this infectious disease was framed on social media, with 340 tweets comprising the final sample for this study.ResultsIn Twitter communication about tick risks, over half (55.3%) of the tweets lacked any visual content. Among the tweets with visuals, static photographs and illustrations/rendered images were the most commonly employed forms. Individual persons, news, and health/governmental organizations are the main tweeters. Additionally, most tweets use situational awareness, tool acquisition, and research frames and are in loss-frame. Approximately half (48.8%) of the tweets highlight adverse consequences or frame risk preparedness in negative terms. Tweets with visual aids have higher engagement rates, while those with URLs do not. Finally, tweets use different preparedness response frames and tend to use different gain-/ loss-frames. Specifically, tweets emphasizing situational awareness to alert the public about ticks and tickborne diseases predominantly utilize loss-frames, emphasizing higher risks than tool acquisition or research frames.DiscussionThe study’s findings underscore the importance of strategic communication in public health messaging on social media. By understanding the types of content that generate higher engagement and the framing that resonates with audiences, health organizations and other stakeholders can tailor their communications better to inform the public about tick risks and prevention strategies. This could lead to more effective disease prevention efforts and a better-informed public ready to take appropriate actions to protect themselves from tickborne diseases.

Decoding Twitter: Sentiment Analysis with Machine Learning

This research undertakes a comprehensive examination of sentiment analysis on Twitter, leveraging the power of machine learning methodologies. With a focus on decoding the intricate landscape of emotions within the Twitterverse, the study aims to provide valuable insights into understanding sentiments expressed in this dynamic social media platform. The primary objective is to employ machine learning techniques to unravel the underlying sentiments, encompassing positive, negative, and neutral tones within the brevity of Twitter communication. The methodology involves the collection of a diverse dataset of tweets, followed by meticulous preprocessing steps to handle noise, eliminate irrelevant information, and perform tokenization. Feature extraction techniques, such as TF-IDF, are employed to convert textual data into numerical vectors, facilitating the subsequent application of various machine learning models. These models, ranging from traditional approaches like Naive Bayes to advanced ones like Support Vector Machines, are implemented and rigorously evaluated based on key performance metrics such as accuracy, precision, recall, and F1 score.

"Vicious Circle" of War: Metaphorization of Russia's Invasion of Ukraine in Éric Zemmour's Twitter Communication

In the crucible of conflict, the discourse employed by political leaders transcends the mere conveyance of information, delving into the realm of strategic communication and persuasion. Twitter, with its brevity and widespread usage among political actors, offers a rich source for investigating political discourse dynamics. Particularly notable is the pervasive use of metaphors by politicians during times of crisis, a linguistic phenomenon that extends beyond its conventional literary function to assume a critical role in shaping public perceptions, fostering unity, and framing the narrative of conflict. This article focuses on the analysis of political communication of Éric Zemmour, a prominent figure in the contemporary French political landscape, on Twitter to reveal main functions of metaphors during the first year of the Russian full-scale invasion of Ukraine. The research has been conducted using Critical Discourse Analysis (CDA) as the main approach and elements of Conceptual Metaphor Theory (CMT). By integrating these methodologies, the study revealed that Éric Zemmour employs conceptual and traditional metaphors to present to explain relations between France-Ukraine-Russia, enhance own credibility and trustworthiness, shape public opinion, simplify the complex geopolitical event, evoke emotional responses, appeal to the national pride and identity, construct social actors and events, underscore the urgency and seriousness of the current situation, and personify the subjects. The paper thus provides a more nuanced understanding of the linguistic and conceptual mechanisms of metaphors in Zemmour's Twitter communication during a crucial geopolitical event.

Twitter Communication Among Democracy Actors: How Interacting With Journalists and Elected Officials Influence People’s Government Performance Assessment and Trust

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.

Tweeting Sustainability: A Comparative Analysis of Adidas and Nike's Twitter Communication on Environmental Initiatives

Tweeting Sustainability: A Comparative Analysis of Adidas and Nike's Twitter Communication on Environmental Initiatives

Emotions and Information Diffusion on Social Media: A Replication in the Context of Political Communication on Twitter

This paper presents a methodological and conceptual replication of Stieglitz and Dang-Xuan’s (2013) investigation of the role of sentiment in information-sharing behavior on social media. Whereas Stieglitz and Dang-Xuan (2013) focused on Twitter communication prior to the state parliament elections in the German states Baden-Wurttemberg, Rheinland-Pfalz, and Berlin in 2011, we test their theoretical propositions in the context of the state parliament elections in Saxony-Anhalt (Germany) 2021. We confirm the positive link between sentiment in a political Twitter message and its number of retweets in a methodological replication. In a conceptual replication, where sentiment was assessed with the alternative dictionary-based tool LIWC, the sentiment was negatively associated with the retweet volume. In line with the original study, the strength of association between sentiment and retweet time lag insignificantly differs between tweets with negative sentiment and tweets with positive sentiment. We also found that the number of an author’s followers was an essential determinant of sharing behavior. However, two hypotheses supported in the original study did not hold for our sample. Precisely, the total amount of sentiments was insignificantly linked to the time lag to the first retweet. Finally, in our data, we do not observe that the association between the overall sentiment and retweet quantity is stronger for tweets with negative sentiment than for those with positive sentiment.

Participant Self‐Reference, Under‐Determination, Generalisation, Vagueness, and Ambiguity in Political Discourse: A Contrastive Analysis of Parliamentary Debate and Twitter Usages*

AbstractThe main aim of this chapter is to analyse under‐determination, generalisation, vagueness, and ambiguity in the speaker's self‐reference strategies used in Catalan parliamentary debate and Twitter. Parliaments are one of the main arenas where politicians develop their activity. On the other hand, Twitter allows politicians to build their own public image and to be in touch with voters, and citizens in general, in a more direct way. The theoretical background of the analysis includes the classical studies on deixis and on person deixis in Catalan; Goffman (1981)'s participation frameworks; functional linguistics and the ethnography of communication, and Twitter communication analysis.In this corpus‐driven analysis, parliamentary discourses and tweets of two members of the Parliament of Catalonia are analysed. The corpus has been drawn from their speeches in the 2020 general politics debate and from the Twitter messages they published in the same period, in autumn 2020.

Building bridges or digging the trench? International organizations, social media, and polarized fragmentation

AbstractCommunication departments of international organizations (IOs) are important intermediaries of global governance who increasingly use social media to reach out to citizens directly. Social media pose new challenges for IO communication such as a highly competitive economy of attention and the fragmentation of the audiences driven by networked curation of content and selective exposure. In this context, communication departments have to make tough choices about what to communicate and how, aggravating inherent tensions between IO communication as comprehensive public information (aimed at institutional transparency)—and partisan political advocacy (aimed at normative change). If IO communication focuses on advocacy it might garner substantial resonance on social media. Such advocacy nevertheless fails to the extent that it fosters the polarized fragmentation of networked communication and undermines the credibility of IO communication as a source of trustworthy information across polarized “echo chambers.” The paper illustrates this argument through a content and social network analysis of Twitter communication on the Global Compact for Safe, Orderly and Regular Migration (GCM). Remarkably, instead of facilitating cross-cluster communication (“building bridges”) Twitter handles run by the United Nations Department of Global Communications (UNDGC) seem to have substantially fostered ideological fragmentation (“digging the trench”) by their way of partisan retweeting, mentioning, and (hash)tagging.

The politics of evasion: The missing statehood debate in puerto rico

In this research, we explored how foundational issues can become subtext in political discourse by studying how statehood was debated in social media prior to two plebiscites in Puerto Rico. Historically in Puerto Rico, local parties are divided on this issue instead of along the more common conservative versus liberal division that is found in most parts of the United States. We collected the corpus of Twitter communication by members of Puerto Rico’s Legislature for the term of 2017-2021. Using latent topic modeling techniques, we classified the political discussion along party lines. Surprisingly, statehood was not a major topic in our model when using the full corpus of data. However, when we filtered the data to include only those tweets discussing statehood and sub-sampled them by major party, the sub-topics within statehood communication became clear and coherent as was the partisan divide. Ultimately, while there is a clear division about statehood between the parties, the issue has become so intrinsic to the political sphere in Puerto Rico that it no longer commanded significant attention in the political discourse during this period.

Scandal frames (Political News)

Scandal frames (Political News)

Identifying the communication of burnout syndrome on the Twitter platform from the individual, organizational, and environmental perspective

Addressing the escalating prevalence of burnout syndrome, which affects individuals across various professions and domains, is becoming increasingly imperative due to its profound impact on personal and professional aspects of employees’ lives. This paper explores the intersection of burnout syndrome and human resource management, recognizing employees as the primary assets of organizations. It emphasizes the growing importance of nurturing employee well-being, care, and work-life balance from a human resource management standpoint. Employing social media analysis, this study delves into Twitter-based discourse on burnout syndrome, categorizing communication into three distinct dimensions: individual, organizational, and environmental. This innovative approach provides fresh insights into interpreting burnout syndrome discourse through big data analysis within social network analysis. The methodology deployed in this study was predicated upon the enhanced Social Media Analysis based on Hashtag Research framework and frequency, topic and visual analysis were conducted. The investigation encompasses Twitter communication from January 1st, 2019, to July 31st, 2022, comprising a dataset of 190,770 tweets. Notably, the study identifies the most frequently used hashtags related to burnout syndrome, with #stress and #mentalhealth leading the discussion, followed closely by #selfcare, #wellbeing, and #healthcare. Moreover, a comprehensive analysis unveils seven predominant topics within the discourse on burnout syndrome: organization, healthcare, communication, stress and therapy, time, symptoms, and leadership. This study underscores the evolving landscape of burnout syndrome communication and its multifaceted implications for individuals, organizations, and the broader environment, shedding light on the pressing need for proactive interventions. In organizations at all levels of management, the concept of burnout should be included in the value philosophy of organizations and should focus on organizational aspects, working hours and work-life balance for a healthier working environment and well-being of employees at all levels of management.

Short text classification with machine learning in the social sciences: The case of climate change on Twitter.

To analyse large numbers of texts, social science researchers are increasingly confronting the challenge of text classification. When manual labeling is not possible and researchers have to find automatized ways to classify texts, computer science provides a useful toolbox of machine-learning methods whose performance remains understudied in the social sciences. In this article, we compare the performance of the most widely used text classifiers by applying them to a typical research scenario in social science research: a relatively small labeled dataset with infrequent occurrence of categories of interest, which is a part of a large unlabeled dataset. As an example case, we look at Twitter communication regarding climate change, a topic of increasing scholarly interest in interdisciplinary social science research. Using a novel dataset including 5,750 tweets from various international organizations regarding the highly ambiguous concept of climate change, we evaluate the performance of methods in automatically classifying tweets based on whether they are about climate change or not. In this context, we highlight two main findings. First, supervised machine-learning methods perform better than state-of-the-art lexicons, in particular as class balance increases. Second, traditional machine-learning methods, such as logistic regression and random forest, perform similarly to sophisticated deep-learning methods, whilst requiring much less training time and computational resources. The results have important implications for the analysis of short texts in social science research.

Nutrition Information Post COVID-19: A twitter content analysis

Realizing social media's importance, many doctors, nutritionists, health coaches and general users have registered on social media and actively share health information. Users may easily access and exchange health information. It benefits both users and practitioners. Qualitative data analysis is employed to study Twitter communication content to understand better the relationship between users’ interest in healthy eating. The research examined Twitter nutrition health information using hashtags. The frequency of hashtags was ranked. The content analysis quantifies social media healthy diet hashtags. Theme modification and word and phrase recurrence analysis to identify two primary themes and significant sentiments relating to Covid-19 and nutrition. Python and NLP are used to analyze and interpret the data to help acquire in-depth information.

Frames and sentiments of the Twitter communication by German Chancellor Scholz during the Russian invasion of Ukraine

Abstract In a complex information environment, Russia’s invasion of Ukraine presents a major challenge to the communication of political leaders throughout the world. The objective of this article is to analyse the frames and sentiments used by German chancellor Olaf Scholz, employing a novel data set of his Twitter communication (N = 612) during the Russian invasion of Ukraine between 24 February 2022 and 24 February 2023. A combination of computational text analysis approaches with natural language processing (NLP) techniques was used, including the Valence Aware Dictionary and the sentiment Reasoner (VADER) model for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modelling. This research investigates the prevalent frames and emotions in the chancellor’s communication, providing valuable insights into the German government’s stance and strategic communication during this critical geopolitical event. The results of the study revealed that the chancellor used the frames ‘effects of the Ukraine invasion’, ‘climate & environment’, ‘solidarity’ and ‘Russian aggression’ and communicated with positive sentiments. By examining the chancellor’s Twitter communication, this study contributes to the understanding of political communication in the digital era, particularly in the context of international crises, and offers implications for policymakers, scholars and the broader public.

Female gender stereotyping and President Samia Suluhu Hassan’s political communication on Twitter: a blessing for female political leaders?

ABSTRACT This paper explores the Twitter communication of Tanzania’s first female President Samia Suluhu Hassan. Informed largely by African feminism, this study explores the relationship between Samia’s political discourse and gender perspectives on her Twitter account within the frame of Feminist Critical Discourse Analysis. Specifically, this study looked at the construction of her identity and leadership attributes as the country’s first female leader and found that her identity, ‘Mama Samia’, signals respect that evokes the African conception of a respected mother. Indeed, even though gender-constructed feminine roles as mother take precedence, gradually her stature as a national leader is eroding these stereotype labels a handful of hostile sexist comments notwithstanding. In effect, the president’s leadership attributes as constructed in her Twitter discourse are more feminine than masculine. Yet, it is these same feminine attributes of nurturance, wisdom, humility, humanity, collaborative-ness, calmness and politeness that present more of an advantage than a disadvantage in her leadership. Overall, netizens have been accommodative of these feminine attributes, which they find to be effective in addressing pressing national issues in the economy, political and social sectors.

Digital Religious Communication and the Facilitation of Social Resilience, Part 2: Empirical Test of the Theoretical Model. A Study of the Twitter Activity of Ecumenical and Social Justice-Oriented Groups during the COVID-19 Pandemic

As societies have sought to adapt to the (post-)pandemic realities, one of the most profound and far-reaching consequences has been a society-wide acceleration of the turn toward the digital. Following a crucial link between social media communication and resilience, the article (1) aims to investigate how “digital religious communication” on social media can be used to measure and assess ecclesial organizations’ social resilience. In a second step, the Twitter communication of 126 ecumenical and social justice-oriented organizations is then analyzed for how much they communicated about the pandemic during the early phases, for the sentiment of their communication, and for religious semantics and narratives used to address the pandemic. In doing so, the study (2) inquires after the role of communicating religious self-understandings in navigating the pandemic, deepening thereby understanding of the connection between “digital religious communication” and the facilitation of social resilience in the face of crisis.