論文アブストラクト： The financial crisis and austerity politics in Europe has had a devastating impact on public services, social security and vulnerable populations. Greek civil society responded quickly by establishing solidarity structures aimed at helping vulnerable citizens to meet their basic needs and empower them to co-create an anti-austerity movement. While digital technology and social media played an important role in the initiation of the movement, it has a negligible role in the movement's on-going practices. Through embedded work with several solidarity structures in Greece, we have begun to understand the "solidarity economy" (SE) as an experiment in direct democracy and self-organization. Working with a range of solidarity structures we are developing a vision for a "Solidarity HCI" committed to designing to support personal, social and institutional transformation through processes of agonistic pluralism and contestation, where the aims and objectives of the SE are continuously re-formulated and put into practice.
論文アブストラクト： In this paper we consider various genres of citizen science from the perspective of citizen participants. As a mode of scientific inquiry, citizen science has the potential to "scale up" scientific data collection efforts and increase lay engagement with science. However, current technological directions risk losing sight of the ways in which citizen science is actually practiced. As citizen science is increasingly used to describe a wide range of activities, we begin by presenting a framework of citizen science genres. We then present findings from four interlocking qualitative studies and technological interventions of community air quality monitoring efforts, examining the motivations and capacities of citizen participants and characterizing their alignment with different types of citizen science. Based on these studies, we suggest that data acquisition involves complex multi-dimensional tradeoffs, and the commonly held view that citizen science systems are a win-win for citizens and science may be overstated.
論文アブストラクト： Using the 2014 Carlton Complex Wildfire as a case study, we examine who contributes official information online during a crisis event, and the timeliness and relevance of the information provided. We identify and describe the communication behaviors of four types of official information sources (Event Based Resources, Local Responders, Local News Media, and Cooperating Agencies), and collect message data from each source's website, public Facebook page, and/or Twitter account. The data show that the Local News Media provided the highest quantity of relevant information and the timeliest information. Event Based Resources shared the highest percentage of relevant information, however, it was often unclear who managed these resources and the credibility of the information. Based on these findings, we offer suggestions for how providers of official crisis information might better manage their online communications and ways that the public can find more timely and relevant online crisis information from official sources.
論文アブストラクト： As algorithmically-driven content curation has become an increasingly common feature of social media platforms, user resistance to algorithmic change has become more frequent and visible. These incidents of user backlash point to larger issues such as inaccurate understandings of how algorithmic systems work as well as mismatches between designer and user intent. Using a content analysis of 102,827 tweets from #RIPTwitter, a recent hashtag-based backlash to rumors about introducing algorithmic curation to Twitter's timeline, this study addresses the nature of user resistance in the form of the complaints being expressed, folk theories of the algorithmic system espoused by users, and how these folk theories potentially frame user reactions. We find that resistance to algorithmic change largely revolves around expectation violation, with folk theories acting as frames for reactions such that more detailed folk theories are expressed through more specific reactions to algorithmic change.