Political and intergenerational communication has become a strategic arena where language accommodation, generational identities, and digital media converge. This paper investigated how politicians adapted their communication styles across generational divides, using Communication Accommodation Theory (CAT) as a central analytical lens. The study situated itself within the context of contemporary African democracies, specifically Nigeria, Kenya, and South Africa-where generational dynamics increasingly influenced political participation and civic engagement. Drawing on both qualitative discourse analysis and cross-national comparisons, the study evaluated political speech patterns, platform choices, and framing strategies aimed at engaging Gen Z, Millennials, and older generational cohorts. Examples of data sources were political speeches, campaign artefacts, and publicly available posts by select political figures on social media. In terms of CAT’s central strategies, convergence, divergence and maintenance, emphasis was placed on the ways politicians reduce, amplify or sustain communicative distance with audience members from various generations. Results showed an incredible convergence towards youth focused political discourse in the digital realm in Nigeria and Kenya. In contrast, politicians in South Africa adopted a formal discourse strategy, especially on traditional media, to distance themselves from older people and to sustain elder-centric communicative messages. These differences illustrated the impact of age, culture and technology on political communication. By showing how generational adaptation was a rhetorical strategy and an important aspect of political credibility and mobilisation, the study added to the growing body of literature on political linguistics, strategy communication, and media psychology. It also provided context-based communication frameworks for intergenerational engagement in democracies.
ChatGPT (Chat Generative Pre-trained Transformer) as an artificial intelligence (AI) tool has created a paradigm shift in the field of media studies by enhancing research efficiency, transforming teaching, and on the other hand, raising critical questions about authorship and algorithmic bias which has sparked concerns over ethical implications in media studies. This study therefore investigated communication scholars' views, concerns, and expectations regarding ChatGPT's integration into media studies through in-depth interview of 20 communication scholars purposively drawn from all communication scholars scattered in various universities within Anambra state, Nigeria. Findings revealed that most communication scholars in Anambra State, Nigeria, viewed ChatGPT as a beneficial tool for media studies sustainability regardless of the disadvantages inherent in its usage and are willing to adopt it as some have already put it into use for various activities in media studies. It is recommended that training programmes focused on AI literacy are essential to equip communication scholars with the necessary skills to engage with ChatGPT effectively.
Part I: Two Decades of Progress? Chapter 1: Introduction: We've Come a Long Way, Maybe... - Judith Cramer and Pam Creedon Chapter 2: Sexed and Gendered Bodies in Journalism Textbooks - Linda Steiner Chapter 3: How to Stir Up a Hornet's Nest: Studying the Implications of Women Journalism Majors - Maurine H. Beasley Part II: Update on the Professions Chapter 4: Women in Newspaper Journalism (Since the 1990s) - June O. Nicholson Chapter 5: Women's Salary and Status in the Magazine Industry - Sammye Johnson Chapter 6: Radio: The More Things Change...The More They Stay the Same - Judith Cramer Chapter 7: Women and Minorities in Commercial and Public Television News, 1994-2004 - Jannette L. Dates Chapter 8: Women in Public Relations: Success Linked to Organizational and Societal Cultures - Elizabeth L. Toth and Carolyn Garett Cline Chapter 9: Advertising Women: Images, Audiences, and Advertisers - Nancy Mitchell Chapter 10: The Power to Improve Lives: Women in Health Communication - Julie L. Andsager Chapter 11: Scholastic Media: Women in Quantity and Quality...But Is That Enough? - Candace Perkins Bowen Chapter12: Increased Legitimacy, Fewer Women? Analyzing Editorial Leadership and Gender in Online Journalism - Shayla Thiel Stern Chapter 13: Women Journalists in Toyland and in the Locker Room: It's All About the Money - Pam Creedon and Roseanna M. Smith Part III: International Perspectives Chapter 14: Three Steps Forward and Two Steps Back? Women Journalists in the Western World Between Progress, Standstill, and Retreat - Romy Frohlich Chapter 15: Bewitched, Bedeviled, and Left Behind: Women in Mass Communication in a World of Faith - Debra L. Mason Chapter 16: The Global Context of Women in Communication - H. Leslie Steeves Part IV: Building a Foundation for Further Study Chapter 17: On the Margins: Examining the Intersection of Women and the Law of Mass Communication - Diane L. Borden and Maria B. Marron Chapter 18: Situating "the Other": Women, Racial, and Sexual Minorities in the Media - Carolyn M. Byerly Chapter 19: Myths of Race and Beauty in Teen Magazines: A Semiotic Analysis - Meenakshi Gigi Durham Chapter 20: The Social Construction of Leadership and Its Implications for Women in Mass Communication - Linda Aldoory Chapter 21: Got Theory? - Laura A. Wackwitz and Lana F. Rakow Part V: Where Do We Go From Here? Chapter 22: Our Conclusion: Gender Values Remain, Inequity Resurges, and Globalization Brings New Challenges - Pam Creedon and Judith Cramer
Communication of scientific knowledge beyond the walls of science is key to science's societal impact. Media channels play sizable roles in disseminating new scientific ideas about human health, economic welfare, and government policy as well as responses to emergent challenges such as climate change. Indeed, effectively communicating science to the public helps inform society's decisions on scientific and technological policies, the value of science, and investment in research. At the same time, the rise of social media has greatly changed communication systems, which may substantially affect the public's interface with science. Examining 20.9 million scientific publications, we compare research coverage in social media and mainstream media in a broad corpus of scientific work. We find substantial shifts in the scale, impact, and heterogeneity of scientific coverage. First, social media significantly alters what science is, and is not, covered. Whereas mainstream media accentuates eminence in the coverage of science and focuses on specific fields, social media more evenly sample research according to field, institutional rank, journal, and demography, increasing the scale of scientific ideas covered relative to mainstream outlets more than eightfold. Second, despite concerns about the quality of science represented in social media, we find that social media typically covers scientific works that are impactful and novel within science. Third, scientists on social media, as experts in their domains, tend to surface high-impact research in their own fields while sampling widely across research institutions. Contrary to prevalent observations about social media, these findings reveal that social media expands and diversifies science reporting by highlighting high-impact research and bringing a broader array of scholars, institutions and scientific concepts into public view.
Understanding and predicting user behavior on social media platforms is crucial for content recommendation and platform design. While existing approaches focus primarily on common actions like retweeting and liking, the prediction of rare but significant behaviors remains largely unexplored. This paper presents a hybrid methodology for social media user behavior prediction that addresses both frequent and infrequent actions across a diverse action vocabulary. We evaluate our approach on a large-scale Bluesky dataset containing 6.4 million conversation threads spanning 12 distinct user actions across 25 persona clusters. Our methodology combines four complementary approaches: (i) a lookup database system based on historical response patterns; (ii) persona-specific LightGBM models with engineered temporal and semantic features for common actions; (iii) a specialized hybrid neural architecture fusing textual and temporal representations for rare action classification; and (iv) generation of text replies. Our persona-specific models achieve an average macro F1-score of 0.64 for common action prediction, while our rare action classifier achieves 0.56 macro F1-score across 10 rare actions. These results demonstrate that effective social media behavior prediction requires tailored modeling strategies recognizing fundamental differences between action types. Our approach achieved first place in the SocialSim: Social-Media Based Personas challenge organized at the Social Simulation with LLMs workshop at COLM 2025.
The study examined media literacy skills for promoting sustainable development in Rivers State. Three research questions guided the study. The study anchored on information literacy theory. The survey research design was adopted using a sample of 167 respondents drawn from a population of 278 lecturers in the four departments of Communication, film and media studies using proportionate sampling technique. The instrument for data collection was a questionnaire which was validated by experts and used for data gathering. The reliability of the instrument was established at 0.82 using the Pearson Product Moment Correlation Co-efficient (PPMC). The study found amongst others that critical and analytical thinking, digital literacy, environmental literacy, cultural and social awareness, and collaborative communication are media literacy skills needed for promoting sustainable development. The study concluded that media literacy and education can potentially empower the citizens to be able to critically analyze and evaluate media messages, be better informed, engage, and be proactive in addressing both local and global issues that can bring about sustainable development in Rivers State. Media literacy skills were essential for the citizens to be well informed on the various types of media outlets and their roles in promoting sustainable development in Rivers State-Nigeria. The study recommended amongst others that Policymakers and curriculum planners begin to think along the lines of integrating media literacy into the school curriculum. This will help students develop a holistic understanding of media and its impact on society.
This study was centered on the influence of the concept of acceptability on Magic FM 102.9 radio station, communication through media aesthetics. The study was able to relate aesthetics to media as conjugal twins that cannot be separated from each other no matter what. It also related aesthetics as a form of communication which sees it as something or object that has the capacity of producing an outstanding feeling of pleasure which makes communication attractive and believable to the audience. The study also reviewed aesthetics as a sign of communication that makes use of colour, design and shapes, lighting, films/movies, advertising/marketing, celebrities, parks and events. It was also found that the tools that enhance communication as aesthetics include camera, colour, lighting, music and sound. The interview granted to the 5 selected respondents from Magic Fm on the components which enhance aesthetics revealed that lighting, music, microphone and colour were amongst. It was concluded that it is empirical to say that without media aesthetics, communication cannot be acceptable and believable by the audience. It was also recommended that media practitioners and owners should endeavour to prioritize the use of media aesthetics in order to achieve their aim of attracting and sustaining the interest of their audience as well as packaging and presenting their messages in a way they would be saleable through the use of synchronizing aesthetic tools and qualities.
This article explains the Homo Sapiens Immodicus model's entire cycle, an operationalization of what occurs inside technobrains in 2024. Each cycle phase is compared to characters in Ray Bradbury’s Fahrenheit 451 book, Mildred, Montag, and Faber, using Bradbury’s literary work and the teachings of Marshall McLuhan, Jaques Ellul, and Neil Postman.
This study investigates news audiences’ platform preferences, usage patterns, and factors affecting their mobile news consumption through news apps. Four explanatory factors, news app users’ demographics, news media usage, perceptions, and motivations, are proposed to predict adoption intention. By surveying 698 mobile news app users in the US, this study’s findings indicate that user perceptions of news apps (i.e., perceived ease of use, compatibility, relative content advantage, and observability) and instrumental motivations of news consumption (i.e., information-seeking and opinion needs) best predict news consumers’ willingness to continue using mobile news apps. Theoretical and practical implications are discussed to offer new insights into mobile news audience behavior and inform current digital publishers on cross-media strategies in the highly competitive mobile news market.
Journalism. The periodical press, etc., Communication. Mass media
This study examines and compares online incivility on China’s Weibo and the U.S.’s X (Twitter) amid the Russia-Ukraine conflict, aiming to unravel how different cultural and geopolitical contexts influence online incivility and identify factors that may influence the occurrence of online incivility in different national contexts.
Francis Mawuli Abude, Jones Odei-Mensah, Eric Schaling
Central bank communication is a valuable source of information designed to shape the expectations of economic agents within and outside an economy. In particular, the content of Monetary Policy Committees’ press releases and statements reflect the central banks’ view of current and future macroeconomic developments, making them useful for creating high-frequency indicators as alternatives to traditional but slower-to-publish macroeconomic indicators. In this study, Artificial Intelligence (AI)-powered text-mining techniques were employed to create monetary policy communication-based indicators, namely the Monetary Policy Readability Index (MPRI), the Monetary Policy Sentiment Index (MPSI), and the Monetary Policy Uncertainty Index (MPUI), using press releases from the Bank of Ghana's monetary policy committee spanning January 2003 to December 2022. The findings suggest that while readability and sentiments generally declined over the sample period, uncertainty increased, indicating persistent macroeconomic imbalances and vulnerabilities in the domestic economy. The newly developed time series-based indicators demonstrate Granger causal relationships with key macroeconomic variables, affirming their relevance to the central bank, the Ministry of Finance, researchers, investors, and development partners. Notably, the indicators can serve as an early warning system for monitoring and predicting the country's macroeconomic risks, forecasting lagging indicators, assessing the effectiveness of the Bank’s monetary policy communication, and addressing monetary policy inquiries.
Grasping the themes of social media content is key to understanding the narratives that influence public opinion and behavior. The thematic analysis goes beyond traditional topic-level analysis, which often captures only the broadest patterns, providing deeper insights into specific and actionable themes such as "public sentiment towards vaccination", "political discourse surrounding climate policies," etc. In this paper, we introduce a novel approach to uncovering latent themes in social media messaging. Recognizing the limitations of the traditional topic-level analysis, which tends to capture only overarching patterns, this study emphasizes the need for a finer-grained, theme-focused exploration. Traditional theme discovery methods typically involve manual processes and a human-in-the-loop approach. While valuable, these methods face challenges in scalability, consistency, and resource intensity in terms of time and cost. To address these challenges, we propose a machine-in-the-loop approach that leverages the advanced capabilities of Large Language Models (LLMs). To demonstrate our approach, we apply our framework to contentious topics, such as climate debate and vaccine debate. We use two publicly available datasets: (1) the climate campaigns dataset of 21k Facebook ads and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads. Our quantitative and qualitative analysis shows that our methodology yields more accurate and interpretable results compared to the baselines. Our results not only demonstrate the effectiveness of our approach in uncovering latent themes but also illuminate how these themes are tailored for demographic targeting in social media contexts. Additionally, our work sheds light on the dynamic nature of social media, revealing the shifts in the thematic focus of messaging in response to real-world events.
The editability of online news content has become a significant factor in shaping public perception, as social media platforms introduce new affordances for dynamic and adaptive news framing. Edits to news headlines can refocus audience attention, add or remove emotional language, and shift the framing of events in subtle yet impactful ways. What types of media bias are editorialized in and out of news headlines, and how can they be systematically identified? This study introduces the MediaSpin dataset, the first to characterize the bias in how prominent news outlets editorialize news headlines after publication. The dataset includes 78,910 pairs of headlines annotated with 13 distinct types of media bias, using human-supervised LLM labeling. We discuss the linguistic insights it affords and show its applications for bias prediction and user behavior analysis.
The concept that media has an effective role in social change is a complex question in itself. However, as the media is directly connected with lives of people, it is necessary to focus on this subject-area. This study has been presented on the basis of the ground of mass communication and principles of the media and review- analysis of previous studies in this sector. Media content and messages have a deep impact on the mood of readers, sources and viewers. Consequently, the person's concept, perspective and field of thought are affected. Eventually, because of this the individual's behavior will change and in overall scenes of change appear in the society. In the past few years, the number of media in Nepal has been growing exponentially, and our society is not immune to the changes caused by the influence of media.
L’article s’interroge sur la place des « polars ruraux » dans une plus vaste production littéraire portant la trace de préoccupations environnementales. Il aborde, en particulier, les modalités d’insertion d’un discours écologique et les enjeux de celui-ci dans un ensemble de romans policiers européens contemporains.
Fabio Barbero, Sander op den Camp, Kristian van Kuijk
et al.
Coordinated multi-platform information operations are implemented in a variety of contexts on social media, including state-run disinformation campaigns, marketing strategies, and social activism. Characterized by the promotion of messages via multi-platform coordination, in which multiple user accounts, within a short time, post content advancing a shared informational agenda on multiple platforms, they contribute to an already confusing and manipulated information ecosystem. To make things worse, reliable datasets that contain ground truth information about such operations are virtually nonexistent. This paper presents a multi-modal approach that identifies the social media messages potentially engaged in a coordinated information campaign across multiple platforms. Our approach incorporates textual content, temporal information and the underlying network of user and messages posted to identify groups of messages with unusual coordination patterns across multiple social media platforms. We apply our approach to content posted on four platforms related to the Syrian Civil Defence organization known as the White Helmets: Twitter, Facebook, Reddit, and YouTube. Results show that our approach identifies social media posts that link to news YouTube channels with similar factuality score, which is often an indication of coordinated operations.
Sarah Bro Trasmundi, Sarah Bro Trasmundi, Juan Toro
et al.
This paper applies an embodied perspective to the study of reading and has a two-fold aim: (i) to discuss how reading is best understood in terms of cultural-cognitive performance that involves living bodies who actively engage with reading materials, and (ii) to spark a dialogue with neighboring disciplines, such as multimodality studies and movement studies, which likewise pivot on how practices and performances involve moving bodies: life is something we do. An embodied cognitive perspective considers how performance is constrained by and draws on expertise such as lived experience as well as the material affordances available in the situation. Such a perspective is crucial for reading research as this domain has been, and largely still is, dominated by the view that reading is a silent, disembodied activity that takes place in the reader's brain by means of neural mechanisms. However, recent studies of reading practices are starting to develop new explanations emphasizing the multimodal engagement in reading as crucial for managing the activity. While this perspective is still empirically underexplored, we seek to highlight how reading is managed by readers' dynamic, embodied engagement with the material. We call this engagement cognitive pacemaking, an action-perception phenomenon we argue should be considered as the key mechanism for controlling attention. We present here a framework to understand reading in terms of pacemaking by emphasizing attentional shifts constituted by embodied modulations of lived temporality. Methodologically, we combine a close reading of a classic literary text, with the focus on attentional modulation with a qualitative study of university students reading different short texts. We highlight how meaning emerges not primarily from linguistic decoding and comprehension, but also from cognitive-cultural, multimodal engagement with the text. Finally, we conclude that empirical reading research should focus on how embodied reading differs across contexts, genres, media and personalities to better scaffold and design reading settings in accordance with those aspects.
This study explores the multimodal action formation of second language (L2) apologies, particularly in relation to the members’ orientation to the significance of the misdemeanour. Although talk is the primary means through which participants accomplish apologies, embodied and paralinguistic interaction also play an integral role in conveying the proportional intensity of the apology. Members may bolster a second language apology with gestures from their first language, such as the Japanese gassho gesture. The study draws on conversation analytic research on L2 use as situated within a complex ecology of multimodal social interaction to reflect on the notions of interactional competence and interactional repertoires. The sequences were video-recorded among Japanese homestay visitors and their American host families.
The massive spread of visual content through the web and social media poses both challenges and opportunities. Tracking visually-similar content is an important task for studying and analyzing social phenomena related to the spread of such content. In this paper, we address this need by building a dataset of social media images and evaluating visual near-duplicates retrieval methods based on image retrieval and several advanced visual feature extraction methods. We evaluate the methods using a large-scale dataset of images we crawl from social media and their manipulated versions we generated, presenting promising results in terms of recall. We demonstrate the potential of this method in two case studies: one that shows the value of creating systems supporting manual content review, and another that demonstrates the usefulness of automatic large-scale data analysis.
While social media offers freedom of self-expression, abusive language carry significant negative social impact. Driven by the importance of the issue, research in the automated detection of abusive language has witnessed growth and improvement. However, these detection models display a reliance on strongly indicative keywords, such as slurs and profanity. This means that they can falsely (1a) miss abuse without such keywords or (1b) flag non-abuse with such keywords, and that (2) they perform poorly on unseen data. Despite the recognition of these problems, gaps and inconsistencies remain in the literature. In this study, we analyse the impact of keywords from dataset construction to model behaviour in detail, with a focus on how models make mistakes on (1a) and (1b), and how (1a) and (1b) interact with (2). Through the analysis, we provide suggestions for future research to address all three problems.