Mediated Conversation

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    Understanding the Bystander Audience in Online Incivility Encounters: Conceptual Issues and Future Research Questions
    ( 2021-01-05) Kim, Yeweon
    This paper presents a theoretical exploration of how and why the 1960’s bystander theory is a valuable lens through which to study contemporary uncivil online communication, particularly in user commenting spaces. Based on the literature on bystander intervention, which includes extensive field and experimental research on bystander behavior in emergency situations, this paper understands non-target readers of uncivil comments as the bystander audience, which is made up of people who encounter an emerging form of online emergencies and can decide whether and how to intervene. In doing so, some particularities of online affordances are taken into account to predict how they might challenge the application of traditional bystander literature. Through such considerations, this paper identifies a set of future research questions about the underlying conditions, causes, and consequences of intervention against online incivility, and then concludes with some limitations and implications of the proposed approach.
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    The Role of New York Times Picks in Comment Quality and Engagement
    ( 2021-01-05) Wang, Yixue ; Diakopoulos, Nicholas
    While various methods can be used to maintain online discussion quality, one aspect that is underexplored is the role of highlights from professional moderators. In this work, we look at the impact of New York Times (NYT) Picks. We present an analysis of more than 13 million NYT comments, examining the quality and frequency of commenting on the site in response to NYT Picks. The findings offer evidence that NYT Picks are associated with an increase in the quality of first-time receivers’ next approved comment, as well as the commenting frequency during commenters’ early tenure on the site. The quality boost associated with receiving a Pick attenuates after subsequent picks and diminishes over time as the user continues commenting, but is still higher than commenters who don’t receive Picks. The findings also indicate that exposure to Pick badges is positively correlated with subsequent higher quality approved comments, albeit to a lesser extent.
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    Resolving the Chatbot Disclosure Dilemma: Leveraging Selective Self-Presentation to Mitigate the Negative Effect of Chatbot Disclosure
    ( 2021-01-05) Mozafari, Nika ; Weiger, Welf H. ; Hammerschmidt, Maik
    Chatbots are increasingly able to pose as humans. However, this does not hold true if their identity is explicitly disclosed to users—a practice that will become a legal obligation for many service providers in the imminent future. Previous studies hint at a chatbot disclosure dilemma in that disclosing the non-human identity of chatbots comes at the cost of negative user responses. As these responses are commonly attributed to reduced trust in algorithms, this research examines how the detrimental impact of chatbot disclosure on trust can be buffered. Based on computer-mediated communication theory, the authors demonstrate that the chatbot disclosure dilemma can be resolved if disclosure is paired with selective presentation of the chatbot’s capabilities. Study results show that while merely disclosing (vs. not disclosing) chatbot identity does reduce trust, pairing chatbot disclosure with selectively presented information on the chatbot’s expertise or weaknesses is able to mitigate this negative effect.
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    Prompt-Rich CMC on YouTube: To What or to Whom Do Comments Respond?
    ( 2021-01-05) Herring, Susan ; Chae, Seung Woo
    Participants in textual CMC must identify to whom or what a message responds in order to establish conversational coherence. Media sharing sites provide rich prompts that make available multiple, diverse, potential addressees. We conducted an exploratory content analysis of addressee types and how they relate to the content of messages in three YouTube comment threads related to the COVID-19 pandemic, applying addressee and topic coding schemes inspired by [6]’s YouTube participation framework. Some addressee types and content types were mentioned significantly more frequently than others, although there was variation across threads. Also, certain kinds of topics were addressed more with certain addressees, and different topics were addressed in replies than in comments. Thus, the interaction between addressee and message content should be considered in analyses of conversational coherence on YouTube and other rich-prompt CMC platforms.
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    Online-Computer-Mediated Interviews and Observations: Overcoming Challenges and Establishing Best Practices in a Human-AI Teaming Context
    ( 2021-01-05) Stephens, Keri ; Nader, Karim ; Harris, Anastazja ; Montagnolo, Caroline ; Hughes, Amanda ; Stevens, Ashley ; Wijesuriya, Yasas Pramuditha Senarath ; Purohit, Hemant
    Prior research has established the feasibility of conducting online interviews and observations, yet there is limited guidance in how to interact with participants when conducting fully mediated research with screen-sharing and video. This study, conducted during early phases of COVID-19, included 15 volunteer tweet annotators working with an emergency response organization. This method contribution uses cues-related and surveillance theories to reveal challenges and best practices when asking research participants to share their screen, be on video, and participate in a multiple-interview study. The findings suggest that researchers conducting online-mediated research should be prepared to provide technical support for the devices and interfaces participants use during the study, find ways to “see” beyond what is on the mediated screen, and consider ethical issues not often discussed. In addition to these findings, an output of this research is two brief training videos useful for other researchers embarking on conducting fully mediated research.