Session:「Communication and Discourse」

Conceptualizing Disagreement in Qualitative Coding

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3173733

論文アブストラクト: Collaborative qualitative coding often involves coders assign- ing different labels to the same instance, leading to ambiguity. We refer to such an instance of ambiguity as disagreement in coding. Analyzing reasons for such a disagreement is essential-- both for purposes of bolstering user understanding gained from coding and reinterpreting the data collaboratively, and for negotiating user-assigned labels for building effective machine learning models. We propose a conceptual definition of collective disagreement using diversity and divergence within the coding distributions. This perspective of disagreement translates to diverse coding contexts and groups of coders irrespective of discipline. We introduce two tree-based ranking metrics as standardized ways of comparing disagreements in how data instances have been coded. We empirically validate that, of the two tree-based metrics, coders' perceptions of dis- agreement match more closely with the n-ary tree metric than with the post-traversal tree metric.

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Multidimensional Risk Communication: Public Discourse on Risks during an Emerging Epidemic

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3173788

論文アブストラクト: Crisis informatics has examined how institutions and individuals seek, communicate, and curate information in response to crises. The public's communication and perception of risks on social media remain understudied. In this study, we report a qualitative analysis of public perceptions of risks and risk management measures on Reddit during the Zika crisis, an emerging epidemic associated with high uncertainty regarding pathology, epidemiology, and broad consequences. We found two types of perceived risks: ones directly caused by the Zika virus, and ones potentially introduced by authorities' risk management measures. Risk perceptions unfolded along multiple dimensions beyond the imminent and personal level. Reddit users discussed in a speculative way to foresee various risks in the long run or at larger geographical scales. We discuss the multidimensionality and speculative nature of risk perception on social media, and derive implications for crisis informatics research and public health research and practice.

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Fostering Commonfare. Infrastructuring Autonomous Social Collaboration

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3174026

論文アブストラクト: Recently, HCI scholars have started questioning the relationship between computing and political economy, with both general analyses of such relationships, and specific design cases describing design interventions. This paper contributes to this stream of reflections, and argues that IT designers and HCI scholars can critically engage with the contemporary phase of capitalism by infrastructuring the emergence of new institutional forms of autonomous social collaboration through IT projects. More specifically, we discuss strategies and tactics that are available for IT designers embracing an activist agenda while infrastructuring autonomous social collaborations. We draw on empirical data from an H2020 EU funded project -- Commonfare -- that seeks to foster the emergence of alternative forms of welfare provision rooted in social collaboration. In this context, we discuss how the necessary multiple relations that unfold in a project with such ambitions shape both the language and the technologies of the project itself.

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Confronting Social Criticisms: Challenges when Adopting Data-Driven Policing Strategies

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3174043

論文アブストラクト: Proponents of data-driven policing strategies claim that it makes policing organizations more effective, efficient, and accountable and has the potential to address some policing social criticisms (e.g. racial bias, lack of accountability and training). What remains less understood are the challenges when adopting data-driven policing as a response to these criticisms. We present results from a qualitative field study about the adoption of data-driven policing strategies in a Midwestern police department in the United States. We identify three key challenges police face with data-driven adoption efforts: data-driven frictions, precarious and inactionable insights, and police metis concerns. We demonstrate the issues that data-driven initiatives create for policing and the open questions police agents face. These findings contribute an empirical account of how policing agents attend to the strengths and limits of big data's knowledge claims. Lastly, we present data and design implications for policing.

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