Communication, Digital Conversation, and Media Technologies

Permanent URI for this collectionhttps://hdl.handle.net/10125/109887

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Let’s Choir-laborate: Designing a Multisensory Technology that Supports Real-Time Interaction in Digital Environments
(2025-01-07) Hacker, Janine; Schöbel, Sofia; Carôt, Alexander
The need to translate collaborative activities beyond work and education into online environments has become increasingly urgent following disruptive events such as the COVID-19 pandemic. This study identifies the requirements and explores the challenges associated with designing a multisensory technology that requires real-time audiovisual interaction, using the case of online group singing. Using a design science research methodology, initial requirements were derived from the literature and translated into a technology solution. The solution was demonstrated in online choir rehearsals and evaluated in interviews with choir members. Our findings highlight the need for synchronous audiovisual cues and social connection between the users to achieve an effective collaborative experience. Our work contributes to the information systems discipline by identifying specific requirements for collaborative multisensory online activities and suggesting improvements for digital collaboration tools in general. Furthermore, online collaborative musicmaking solutions can increase cultural participation and inclusion.
Conversations with Data: How Data Journalism Affects Online Comments in the New York Times
(2025-01-07) Kantor, Avner; Rafaeli, Sheizaf
Users in the data age have access to more data than ever before, but little is known how they interact with it. Using transparency and multimedia, data journalism (DJ) lets users explore and interpret data on their own. This study examines how DJ affects online comments as a case study of user interactions with data. The corpus comprises 6,400 stories and their comment sections from the DJ and other sections of the New York Times, from 2014-2022. Results indicate that DJ is positively associated with higher level of interactivity between the users. This relationship is mediated by statistical information, information sources, and static visualizations. However, there is a low level of interactivity with the content; consequently, only part of the users use it. The results demonstrate how data accessibility through DJ engages the users in conversation. According to deliberation theory, this creates a conducive environment for democratic processes.
From Ads to Addiction: The Role of Online Gambling Advertising Spend in Problem Gambling Search Trends over a Decade
(2025-01-07) Vargo, Chris; Hopp, Tobias; Gangadharbatla, Harsha; Pritha, Agarwal
This study examines the impact of online gambling companies’ ad expenditures on Google search behaviors related to gambling problems, using a decade of data (2014 – 2024). We contextualize the rise of problem gambling as a public health issue and discuss the limited, conflicting research on the effects of legalization and advertising. By analyzing Google Trends data as a proxy for gambling addiction, we find that problematic gambling-related searches have increased over time. Although the legality of gambling does not significantly influence search behaviors, increased advertising expenditures are positively correlated with higher search volumes for gambling problems, particularly following the 2018 legalization of sports betting. This relationship strengthens over time, suggesting that sustained exposure to gambling ads may contribute to rising problem gambling behaviors.
Self-exposure as a Way of Life. Privacy Tradeoff and Datafied Citizenship
(2025-01-07) Nowak, Jakub; Siuda, Piotr
This empirical research-based paper, grounded in media sociology, seeks to advance how datafication transforms citizenship by reconstructing practices and accompanying tensions of “doing” privacy. Upon the analysis of 31 in-depth interviews with activists, we ask how privacy is approached and done by people who perceive it as both important and vulnerable and who compromise their privacy with public outreach. To encapsulate two strands of media research—on privacy and social media visibility—in the context of scholarship on datafied citizenship, we introduce the theoretical concept of self-exposure as a way of life. The concept, we argue, highlights ongoing tradeoffs between securing privacy and being visible in digital environments, and by this, helps to learn the complex status of datafied citizenship.
An integrated view of Quantum Technology? Mapping Media, Business, and Policy Narratives
(2025-01-07) Suter, Viktor; Ma, Charles; Pöhlmann, Gina; Meckel, Miriam; Steinacker, Léa
Narratives play a vital role in shaping public perceptions and policy on emerging technologies like quantum technology (QT). However, little is known about the construction and variation of QT narratives across societal domains. This study examines how QT is presented in business, media, and government texts using thematic narrative analysis. Our research design utilizes an extensive dataset of 36 government documents, 165 business reports, and 2,331 media articles published over 20 years. We employ a computational social science approach, combining BERTopic modeling with qualitative assessment to extract themes and narratives. The findings show that public discourse on QT reflects prevailing social and political agendas, focusing on technical and commercial potential, global conflicts, national strategies, and social issues. Media articles provide the most balanced coverage, while business and government discourses often overlook societal implications. We discuss the ramifications for integrating QT into society and the need for well-informed public discourse.
Understanding Supporters Response to Online Petitions: A Value System Moderated Perspective
(2025-01-07) Owusu, Gabriel; Andoh-Baidoo, Francis; Ayaburi, Emmanuel
Understanding the effectiveness of online petitions is vital for social change and inclusion. In recent years, the use of online petitions across various cultural contexts has grown rapidly. Although supporting an online petition is voluntary, individual contextual cultural values can influence supporters’ decision to align with an outcome. We theorize that an individual’s value system amplifies the effectiveness of linguistic persuasive appeals on the success of online petitions. Using dataset on online petitions from Change.org we test our thesis. Overall, our results suggest that persuasive appeals such as cognitive, emotional, and moral appeal cues are influenced by an individual’s harmony, embeddedness, hierarchy, mastery, affective autonomy, intellectual autonomy, and egalitarianism values and that culture, in terms of geographical locations may serve as boundary condition.
AI Deepfake Interaction, Authentication and Correction in Taiwan: Examining the Roles of Echo Chamber and Conspiracy Mentality
(2025-01-07) Lin, Trisha T. C.
Before 2024 Presidential Elections, this web study investigates how deepfake interaction experiences influence Taiwanese online video users’ act of authentication and correction. Based on a modified Stimulus-Reasoning-Orientation-Response model, we examine the dynamics of deepfake interaction as the media stimulus (S), mediated by echo chamber, conspiracy mentality (R), deepfake self-efficacy and presumed influence (O), which in turn shapes user responses to deepfake authentication and correction (R). Structural Equation Modeling results show that deepfake interaction experience is negatively associated with echo chamber, but positively related to conspiracy mentality, and such interaction results in proactive responses to deepfake authentication and correction. In addition, users with higher tendency of echo chambers demonstrate lower deepfake self-efficacy, and perceive less presumed influence of deepfakes. Yet, the pronounced conspiracy mentality increases deepfake presumed influence. Moreover, deepfake self-efficacy is positively related to authentication and correction activities, while its presumed influence only influences act of authentication. Implications are discussed.
Political Campaigning and Social Media Affordances – Mood-setting on TikTok
(2025-01-07) Kannasto, Elisa; Pöyry, Essi
Social media has become an essential tool for political communication, particularly during elections. Social media affords politicians to share information on political views, interact with potential voters and present themselves in a favorable light. In addition, social media affords conveying feelings and emotions, which may be essential in creating an effective subtext for political arguments and influence. We refer to this practice as mood-setting. By utilizing the affordance theory and by engaging in quantitative and qualitative content analysis and multiple correspondence analysis, we study six successful newcomer candidates in the Finnish parliamentary elections in 2023 and their posts (N=145) on TikTok – a platform centered around concise audiovisual content. The analysis suggests that mood-setting is highly candidate-specific. Affordances of short-video platforms such as TikTok support mood-setting and creative self-presentation, but platform features are used flexibly to set different kinds of moods for influence.
Beyond Friends: Exploring the Effects of Unknown Users' Social Media Posts on Individuals’ Perceptions and Behaviors
(2025-01-07) Wu, Mengke; Chang, Jiyeon; Epstein, Ziv; Rand, David
Individuals express themselves on social media, but discrepancies between online content and reality are concerning. Previous research has primarily focused on interactions within people’s social circles, highlighting that exposure to idealized posts can lead to negative effects through social comparison or positive emotions via emotional contagion. This study examines a less-explored scenario: browsing posts from unknown users. 499 participants were randomly assigned to view one of the simulated feed–idealized, harsh, or neutral life posts. The analysis revealed minimal influence from idealized posts, whereas harsh posts significantly worsened participants' mood, life satisfaction, and social self-evaluation. Feed variations had no effect on subsequent posting behavior. These findings suggest that in the short term, emotional contagion is more likely to occur over social comparison after exposure to life moments from people outside one's social circles. This study contributes to understanding the effects of social media feeds and provides insights for improving the social media environment.
I 👍 your Hate: Emojis as Infrastructural Platform Violence on Telegram
(2025-01-07) Morales, Esteban; Hodson, Jaigris; Gruzd, Anatoliy; Mai, Philip
Emojis are a ubiquitous form of online expression. In this paper, we explore emojis as affordances that enact and sustain discursive violence via toxic content. We take a case study approach by focusing on Chismes Frescos Medellin (Fresh Gossip Medellin), a Colombian Telegram group with over 125,676 members. Relying on Communalytic, we collected 98,729 publicly accessible posts. Next, we subdivided the posts into 3,155 toxic and 95,574 non-toxic posts using Detoxify, a popular machine-learning classifier. We explored and compared the two subsets through statistical analysis and thematic analysis. Our findings show that emojis—and specifically, emojis suggesting positive emotions such as 👍 and 😁—are often used to accompany toxic speech in ways that indicate the approval and normalization of toxic speech. Overall, our study points to the need to pay closer attention to how affordances can enable symbolic forms of violence on digital platforms in unexpected ways.
Sentiment Analysis Using Dialogue Data: A Taxonomy of Literature
(2025-01-07) Äyräväinen, Laura E. M; Hinds, Joanne; Davidson, Brittany I.
In a rapidly advancing world of artificial intelligence and natural language processing, the need to understand human dialogue with information systems is ever more pressing. This paper presents a taxonomy of sentiment analysis using dialogue data, developed via a scoping literature review and employment of a widely recognized taxonomy method. By synthesizing the diverse approaches across 18 papers (comprising 22 dimensions and 328 categories), we present a framework that highlights the components underpinning current work, including application domains, data characteristics, sentiment analysis pipelines, methods and dialogue-specific information utilized. By offering a detailed, method-focused view of the existing research, our taxonomy aims to guide future studies that seek to integrate sentiment analysis into dialogue systems. We discuss issues in the current state of the literature and conclude by providing directions for future research.
Like and Hate by Platform: Exploring How Users’ Reactions to Alexandria Ocasio-Cortez’s Posts Vary between YouTube and Instagram
(2025-01-07) Chae, Seung Woo; Hwang, Jennifer
How users' communication varies by social media affordances, especially in the context of political communication, is underexplored. This study extends the existing literature by observing comments on a politician’s identical messages across two platforms, YouTube and Instagram. Comments on Alexandria Ocasio-Cortez's 18 identical videos on both platforms were analyzed using natural language processing. The research design of this study is three-fold. First, we tested if three engagement metrics—number of views, likes, and comments—are significantly associated between the two platforms. Second, we examined which platform has more positive comments. Lastly, we studied how the extent of offensive language and hate speech varies by platform. Our results showed that (1) user engagement with AOC’s videos is consistent across the two platforms, (2) Instagram had more positive comments than YouTube, and (3) YouTube comments were more likely to contain offensive language, whereas Instagram comments were more likely to include hate speech.
Leveraging the Power of ChatGPT: Evaluating Its Effectiveness for Content Analysis and Framing Research in Mass Communication
(2025-01-07) Kwon, Hyerin; Lu, Linqi; Kang, Jiwon; Mcleod, Douglas
This study explores the application of ChatGPT (GPT) to content analysis within the context of framing research, specifically examining its effectiveness in identifying public health, economic stability, and civic vitality frames in COVID-19 press releases. Our methodology is grounded in the Semantic Architecture Model (SAM), which conceptualizes framing as a process by which meaning is embedded in content units at various textual levels (i.e., concepts, assertions, arguments and narratives). In addition, this study underlines the necessity of AI prompt engineering to improve GPT’s coding performance in identifying frames at the concept, assertion, and thematic argument levels. The findings indicate the transformative potential of AI in communication research, highlighting its ability to analyze complex message framing across diverse contexts.
LLMs Enhance Emotional Expression While Maintaining Analytical Depth in News Writing
(2025-01-07) Mazzone, Samuel; Harlan , Jonah; Xu, Larry Zhiming; Ow, Terence
As the impact of generative artificial intelligence (GenAI) becomes increasingly evident in automated journalism, leveraging its potential while mitigating the risks becomes a priority in research. To address this need, our study evaluated the performance of large language models (LLMs) in news writing. We tested 11 LLMs by having them rewrite headlines and content from articles published by Milwaukee Neighborhood News Service (NNS) between 2011 and 2023. The analysis and comparison of 3,623 human-written and 39,853 AI-adapted news pieces showed that different LLMs consistently enhanced emotional expression in headlines (Cohen’s d = .33) and in news content (Cohen’s d = .83). Importantly, this emotional enhancement did not seem to compromise analytical thinking, while some LLMs even improved the analytical depth of reporting. The theoretical and practical implications are discussed, particularly regarding the importance of high-quality training data and how LLMs can better assist journalists in newsrooms.
As a Large Language Model: Ontological-Category Cue Effects on Agent and Message Evaluations
(2025-01-07) Banks, Jaime
The tendency for AI to refer to itself—for instance using first-person pronouns and referring to (in)capabilities—raises questions about the interpretation and effects of machines’ self-referential language in human-machine communication. AI vary in their tendencies to identify themselves as machines or to mask that ontological category in the course of interactions. To examine how self-referential ontological-category cues (i.e., “As a large language model …”) influence judgments of contextualized agents and their responses, a 2×2×2 experiment was conducted. Participants (N = 800) evaluated an exchange between an inconspicuous user and ChatGPT, manipulated to represent three variables: Machine cue present/absent × natural/technical topic × creative/logical framing. Experimental findings point to a weak interaction effect of the cue and the topic suggesting a mild “stay in your lane” effect. Findings have implications for whether and in what context machines may be more or less favorably evaluated when their machine status is cued.
Introduction to the Minitrack on Communication, Digital Conversation, and Media Technologies
(2025-01-07) Lewis, Seth; Kalman, Yoram; Masullo, Gina

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