The internet is becoming increasingly visual, but social computing research and methodological training has relied heavily on textual methods. Methodological innovation is needed to study visual social data, including problematic information (mis- and disinformation, propaganda, hate, AI slop, etc). Contending with this, we present a framework for conducting grounded, interpretive, computationally supported, mixed-method research on collections of visual social media data. We developed this framework while grappling with the ethical, logistical, and methodological challenges of conducting in-depth analysis of potentially harmful visual content while caring for our research team. We document our framework components of visual grammars, human analysis, and computationally supported analysis with an umbrella commitment to care and its use in three empirical case studies. We also provide recommendations and implications for the HCI community in embracing training in and the advancing of visual methods and research, including a sensitizing concept of visual integrity.

Along with other visual content, data visualizations are increasingly used within online discourse, including political communication. Though often considered to be ''objective'', data visualizations can also be created and/or appropriated to mislead. Here, we study the use and evolution of data visualizations within social media discourse around the ongoing ''crisis'' at the US-Mexico border in 2024. Through computationally-assisted qualitative analysis, we first describe how data visualizations are used to support four anti-immigrant frames, highlighting key tactics and sources of these visualizations. Next, we conduct a deep analysis of three Data Visualization Lineages (DVLs), exploring the role of adaptations, annotations, and remixing within families of data visualizations that share the same origin but have diverged through distinct visual alterations. We conclude by discussing approaches for supporting researchers in identifying and unpacking data visualization lineages, and highlighting design opportunities for mitigating the impact of misleading data visualizations in online discourse.

Public records laws are standard for state and federal governments across the U.S. Such laws rest on the core notion that transparency, enacted through access to governmental documentation, will ensure accountability and minimize governmental corruption. However, these laws can also be exploited to harass or burden government employees, including researchers at public universities. CSCW researchers have both an acute vulnerability to these requests, e.g., due to increasing politicization of topics core to our research agenda, and a unique opportunity to study challenges around public records requests as they intersect with the design and use of collaboration technologies. In this paper, we explore how a large number of public records requests (PRRs) affected the collaborative work of a large, multi-site academic research project. We find that — though participants believed PRRs were a valuable tool for government transparency — they added a complicated new dimension to distributed "work" which blends personal and professional dimensions. We explain how researchers interpreted these requests and adapted their communication techniques in response. We discuss how current technologies for communication and collaboration are unprepared to ensure personal privacy and security within adversarial research environments — and collaborative work environments more broadly. Finally, we highlight the misalignment between long-standing transparency laws and the current design of collaboration technologies and provide recommendations for updating these laws.

Misinformation and disinformation about elections remain pressing concerns for researchers, policymakers, and the public. Critics, however, argue that fears surrounding these issues are exaggerated due to a lack of evidence of impact. This debate highlights the challenges inherent in assessing the impacts of misinformation, as the drivers of false and misleading content often exist in the context of a specific claim. To address this issue, we examined false and misleading information surrounding the 2020 and 2022 U.S. national elections, focusing on the contextual features of online conversations that fueled various rumors. We developed two qualitative codebooks, creating the second after realizing that the first, which labeled individual tweets, failed to capture broader rumoring dynamics. By integrating multi-layered qualitative coding with thematic analysis and quantitative visualizations, we show how influencers, political elites, and audiences collaboratively told deep stories from 2020 through 2022. As these stories were told, audiences interpreted events in 2022 through the lens of the 2020 story, guided by influencers' cues, leading to an evolution in storytelling style between the two election cycles. This ongoing performance was tailored to align with the incentive structures, affordances, and attention economy of social media. We combine deep stories with theories of collective sensemaking and rumoring, creating a framework to better assess the contextual features surrounding false and misleading information.

Social media platforms are known to facilitate sudden bursts of attention on individual pieces of content and their creators — dynamics often referred to as ''going viral''. However, questions remain about how these moments impact the individuals who experience them, especially within algorithmically-mediated, video-sharing platforms like TikTok. In this study, we seek to better understand the experiences of creators who received a burst of attention, as identified by their participation in a trend highlighting an earlier video that had ''blown up.'' Through mixed-methods analysis of TikTok trace data and interviews, we show that massive surges in attention can have significant, varied, and long-lasting impacts on creators — within the platform and beyond. These include a short-term increase in views and sharing more similar content, and posting more content responding to other content on the platform. We also show how algorithmically-mediated bursts — or ''boosts'' — of attention led to shifts in creators' conceptualizations of themselves, the platform, and their audiences. Our work contributes empirical and conceptual insights into online fame and celebrity, how audiences and influencers interact, and how the affordances and cultures of TikTok shape how sudden attention is experienced on the platform.

Pervasive falsehoods that erode trust in election processes are of increasing concern to democracies around the world. Misleading claims like these are often understood as simply ''getting the facts wrong''. Using a grounded, interpretative, mixed-method approach to study Twitter activity during the 2022 U.S. Midterm Election in Arizona, our work paints a more nuanced picture. We adapt Klein's data-frame theory of collective sensemaking to online rumors, demonstrating how misleading claims about election administration take shape online through interactions between (often factual) evidence and frames. We introduce a methodological approach for analyzing rumors through this evidence-frame lens and provide insights into the dynamics of online rumoring around claims of ''rigged elections''. Our work highlights how rumors are as much about political framing as they are about faulty facts, and locates the crux of the problem of misinformation in the interactions with and between evidence and distorted political frames.

The ability of large language models (LLMs) to perform zero-shot classification makes them viable solutions for data annotation in rapidly evolving domains where quality labeled data is often scarce and costly to obtain. However, the large-scale deployment of LLMs can be prohibitively expensive. This paper introduces an LLM chain ensemble methodology that aligns multiple LLMs in a sequence, routing data subsets to subsequent models based on classification uncertainty. This approach leverages the strengths of individual LLMs within a broader system, allowing each model to handle data points where it exhibits the highest confidence, while forwarding more complex cases to potentially more robust models. Our results show that the chain ensemble method often exceeds the performance of the best individual model in the chain and achieves substantial cost savings, making LLM chain ensembles a practical and efficient solution for large-scale data annotation challenges.

What types of governance arrangements make some self-governed online groups more vulnerable to disinformation campaigns? We present a qualitative comparative analysis of the Croatian and Serbian Wikipedia editions to answer this question. We do so because between at least 2011 and 2020, the Croatian language version of Wikipedia was taken over by a small group of administrators who introduced far-right bias and outright disinformation. Dissenting editorial voices were reverted, banned, and blocked. Although Serbian, Bosnian, and Serbo-Croatian Wikipedias share many linguistic and cultural features, and faced similar threats, they seem to have largely avoided this fate. Based on a grounded theory analysis of interviews with members of these communities and others in cross-functional platform-level roles, we propose that the convergence of three features---high perceived value as a target, limited early bureaucratic openness, and a preference for personalistic, informal forms of organization over formal ones---produced a window of opportunity for governance capture on Croatian Wikipedia. Our findings illustrate that online community governing infrastructures can play a crucial role in systematic disinformation campaigns and other influence operations.

As a research tradition, participatory design (PD) tends to focus on power dynamics where researchers hold greater power than participants. This paper uses design fiction to consider what this tendency overlooks by examining settings where participants may exist in multiple power relationships simultaneously implicated by the research, specifically focusing on the contexts of misinformation, disinformation, and online hate (M/D/OH). Drawing from existing literature in M/D/OH, we present a series of imaginary method abstracts that prompt questions for researchers to reflect on as they adapt PD techniques for new, different contexts. We highlight three value tensions—authenticity, reciprocity, and impact—integral to sustaining a concern for responsibility in PD scholarship. We end with reflections and potential considerations for responsibly applying PD and design fiction methods in M/D/OH settings.

The 2020 United States (US) presidential election was - and has continued to be - the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.

Claims of election fraud throughout the 2020 U.S. Presidential Election and during the lead up to the January 6, 2021 insurrection attempt have drawn attention to the urgent need to better understand how people interpret and act on disinformation. In this work, we present three primary contributions: (1) a framework for understanding the interaction between participatory disinformation and informal and tactical mobilization; (2) three case studies from the 2020 U.S. election analyzed using detailed temporal, content, and thematic analysis; and (3) a qualitative coding scheme for understanding how digital disinformation functions to mobilize online audiences. We combine resource mobilization theory with previous work examining participatory disinformation campaigns and "deep stories" to show how false or misleading information functioned to mobilize online audiences before, during, and after election day. Our analysis highlights how users on Twitter collaboratively construct and amplify alleged evidence of fraud that is used to facilitate action, both online and off. We find that mobilization is dependent on the selective amplification of false or misleading tweets by influencers, the framing around those claims, as well as the perceived credibility of their source. These processes are a self-reinforcing cycle where audiences collaborate in the construction of a misleading version of reality, which in turn leads to offline actions that are used to further reinforce a manufactured reality. Through this work, we hope to better inform future interventions.

Well-intentioned users sometimes enable the spread of misinformation due to limited context about where the information originated and/or why it is spreading. Building upon recommendations based on prior research about tackling misinformation, we explore the potential to support media literacy through platform design. We develop and design an intervention consisting of a tweet trajectory-to illustrate how information reached a user-and contextual cues-to make credibility judgments about accounts that amplify, manufacture, produce, or situate in the vicinity of problematic content (AMPS). Using a research through design approach, we demonstrate how the proposed intervention can help discern credible actors, challenge blind faith amongst online friends, evaluate the cost of associating with online actors, and expose hidden agendas. Such facilitation of credibility assessment can encourage more responsible sharing of content. Through our findings, we argue for using trajectory-based designs to support informed information sharing, advocate for feature updates that nudge users with reflective cues, and promote platform-driven media literacy.

As misinformation, disinformation, and conspiracy theories increase online, so does journalism coverage of these topics. This reporting is challenging, and journalists fill gaps in their expertise by utilizing external resources, including academic researchers. This paper discusses how journalists work with researchers to report on online misinformation. Through an ethnographic study of thirty collaborations, including participant-observation and interviews with journalists and researchers, we identify five types of collaborations and describe what motivates journalists to reach out to researchers — from a lack of access to data to support for understanding misinformation context. We highlight challenges within these collaborations, including misalignment in professional work practices, ethical guidelines, and reward structures. We end with a call to action for CHI researchers to attend to this intersection, develop ethical guidelines around supporting journalists with data at speed, and offer practical approaches for researchers filling a “data mediator” role between social media and journalists.

Despite increasing awareness and research about online strategic information operations, there remain gaps in our understanding, including how information operations leverage the wider information ecosystem and take shape on and across multiple social media platforms. In this paper we use mixed methods, including digital trace ethnography, to look beyond a single social media platform to the broader information ecosystem. We aim to understand how multiple social media platforms are used, in parallel and complementary ways, to achieve the strategic goals of online information operations. We focus on a specific case study: the contested online conversation surrounding Syria Civil Defense (the White Helmets), a group of first responders that assists civilians affected by the civil war within the country. Our findings reveal a network of social media platforms from which content is produced, stored, and integrated into the Twitter conversation. We highlight specific activities that sustain the strategic narratives and attempt to influence the media agenda. And we note that underpinning these efforts is the work of resilience-building: the use of alternative (non-mainstream) platforms to counter perceived threats of 'censorship' by large, established social media platforms. We end by discussing the implications on social media platform policy.

Journalists are increasingly investigating and reporting on problematic online content such as misinformation, disinformation, and conspiracy theories, leading to the creation of a new misinformation beat. The process of collecting, analyzing, and reporting on this kind of data is complex and nuanced. It is especially challenging as online actors attempt to undermine their work. Through in-depth interviews with twelve journalists, we explore how they investigate and report on online misinformation and disinformation. Our findings reveal some of the unique challenges of reporting on this beat, as well as the ways in which reporters overcome those challenges. We highlight and discuss how journalistic values could be better embedded into the design of tools to support their work, the power dynamics between social media companies and journalists, and the promise of collaborations as a way to support and educate journalists on this beat. This work provides contextual knowledge to researchers looking to better support investigative journalists - on the misinformation beat and beyond - as their work becomes more entangled in sociotechnical systems.

Collaborations increasingly draw on personal data. We examine personal-data-supported collaborations in a high stakes, high-performance environment: collegiate sports. We conducted 22 interviews with people from four common roles within collegiate sports teams: athletes, sport coaches, athletic trainers, and strength and conditioning coaches. Using boundary negotiating artifacts as a lens for analysis, we describe an ecology of personal data in collaborations among these four roles. We use this ecology to highlight tensions and foreground issues of power asymmetry in these collaborations. To characterize these power asymmetries in the collaborative use of personal data, we propose an extension of boundary negotiating artifacts: extraction artifacts.

In this paper, we argue that strategic information operations (e.g. disinformation, political propaganda, and other forms of online manipulation) are a critical concern for CSCW researchers, and that the CSCW community can provide vital insight into understanding how these operations function-by examining them as collaborative "work" within online crowds. First, we provide needed definitions and a framework for conceptualizing strategic information operations, highlighting related literatures and noting historical context. Next, we examine three case studies of online information operations using a sociotechnical lens that draws on CSCW theories and methods to account for the mutual shaping of technology, social structure, and human action. Through this lens, we contribute a more nuanced understanding of these operations (beyond "bots" and "trolls") and highlight a persistent challenge for researchers, platform designers, and policy makers-distinguishing between orchestrated, explicitly coordinated, information operations and the emergent, organic behaviors of an online crowd.

Journalists are struggling to adapt to new conditions of news production and simultaneously encountering criticism for their role in spreading misinformation. Against the backdrop of this "crisis in journalism", this research seeks to understand how journalists are actually participating in the spread and correction of online rumors. We compare the engagement behaviors of journalists to non-journalists- and specifically other high visibility users-within five false rumors that spread on Twitter during three crisis events. Our findings show journalists engaging earlier than non-journalists in the spread and the correction of false rumors. However, compared to other users, journalists are (proportionally) more likely to deny false rumors. Journalists are also more likely to author original tweets and to be retweeted-underscoring their continued role in shaping the news. Interestingly, journalists scored high on "power user" measures, but were distinct from other power users in significant ways-e.g. by being more likely to deny rumors.

Social media are becoming sites of information operations-activities that seek to undermine information systems and manipulate civic discourse [26,36,44,47]. Through a mixed methods approach, our research extends investigations of online activism to examine the "work" of online information operations conducted on Twitter. In particular, we analyze the English-language conversation surrounding the reemergence of Omran Daqneesh (the "Aleppo Boy") on Syrian state television, almost a year after his family's home was bombed in an airstrike conducted by the Syrian government. We uncover: a network of clustered users that contributes to a contested and politicized information space surrounding Omran's story; the presence of undermining narratives that serve to disrupt the mainstream media's narrative and confuse the audience; and the techniques used when promoting, defending, or undermining narratives. In the current climate of increasing polarization in online social spaces, this work contributes an improved understanding of information operations online and of the collaborations that take shape around and through them.

This research examines how Russian disinformation actors participated in a highly charged online conversation about the #BlackLivesMatter movement and police-related shootings in the USA during 2016. We first present high-level dynamics of this conversation on Twitter using a network graph based on retweet flows that reveals two structurally distinct communities. Next, we identify accounts in this graph that were suspended by Twitter for being affiliated with the Internet Research Agency, an entity accused of conducting information operations in support of Russian political interests. Finally, we conduct an interpretive analysis that consolidates observations about the activities of these accounts. Our findings have implications for platforms seeking to develop mechanisms for determining authenticity---by illuminating how disinformation actors enact authentic personas and caricatures to target different audiences. This work also sheds light on how these actors systematically manipulate politically active online communities by amplifying diverging streams of divisive content.

People are increasingly sharing information on social media during disaster events. This information could be valuable to emergency responders, but there remain challenges for using it to inform response efforts---including filtering relevant information from the large volumes of noise. Previous research has largely focused on identifying information that can contribute to a generalized concept of situational awareness. Our work explores the value of approaching this problem from a different perspective---one of actionablity---with the idea that information relevance may vary across responder role, domain, and other factors. This approach asks how we can get the right information to the right person at the right time? We interviewed and surveyed diverse responders to understand what "actionable" information is, allowing that actionability might differ from one responder to another. Through the findings, we (a) offer a nuanced understanding of actionability and differentiate it from situational awareness; (b) describe responders' perspective of what distinguishes good information when making rapid judgments; and (c) suggest opportunities for augmenting social media use to highlight information that needs immediate attention. We offer researchers an opportunity to frame different models of actionability to suit the requirements of a responding role.

Widespread rumoring can hinder attempts to make sense of what is going on during disaster scenarios. Understanding how and why rumors spread in these contexts could assist in the design of systems that facilitate timely and accurate sensemaking. We address a basic question in this line: To what extent does rumor evolution occur (1) through reliance on a centralized information source, (2) in parallel information silos, or (3) through a web of complex informational interactions? We develop a conceptual model and associated analysis algorithms that allow us to distinguish between these possibilities. We analyze a case of rumoring on Twitter during the Boston Marathon Bombing. We find that rumor spreading was predominantly a parallel process in this case, which is consistent with a hypothesis that information silos may underlie the persistence of false rumors. Special attention towards detecting and resolving parallel information threads during collective sensemaking may hence be warranted.

This paper examines how users of social media correct online rumors during crisis events. Focusing on Twitter, we identify different patterns of information correcting behaviors and describe the actions, motivations, rationalizations and experiences of people who exhibited them. To do this, we analyze digital traces across two separate crisis events and interviews of fifteen individuals who generated some of those traces. Salient themes ensuing from this work help us describe: 1) different mechanisms of corrective action with respect to who gets corrected and how; 2) how responsibility is positioned for verifying and correcting information; and 3) how users' imagined audience influences their corrective strategy. We synthesize these three components into a preliminary model and explore the role of imagined audiences-both who those audiences are and how they react to and interact with shared information-in shaping users' decisions about whether and how to correct rumors.

This paper examines social media use after a tragic disaster in a rural community in the USA--the 2014 Oso Landslide. Drawing upon interviews with community members and digital traces from multiple platforms, we explore how affected locals, government responders and journalists utilized a broad range of social media in their work-assembling different platforms to meet the information needs of their audiences. We borrow the analytical lens of stitching suggested by Vertesi, which allows us to see where these infrastructural alignments are seamless vs. seamful-highlighting some of the emergent and persistent challenges for those responding to disasters with and through social media. We demonstrate how this work is extremely dynamic, as the technical affordances of these platforms and the evolving practices of users shape how crisis communication occurs. Simultaneously, the pervasive and in some places institutionalized use of these platforms across a wide range of local actors suggests they are performing as critical infrastructure during crisis response. This raises questions of what it means to have so much local crisis information work occurring through platforms that mediate from a distance.

This research examines Twitter discourse related to #BlackLivesMatter and police-related shooting events in 2016 through a mixed-method, interpretative approach. We construct a "shared audience graph", revealing structural and ideological disparities between two groups of participants (one on the political left, the other on the political right). We utilize an integrated networked gatekeeping and framing lens to examine how #BlackLivesMatter frames were produced--and how they were contested --by separate communities of supporters and critics. Among other empirical findings, this work demonstrates hashtags being used in diverse ways--e.g. to mark participation, assert individual identity, promote group identity, and support or challenge a frame. Considered from a networked gatekeeping perspective, we illustrate how hashtags can serve as channeling mechanisms, shaping trajectories of information flow. This analysis also reveals a right-leaning community of BlackLivesMatter critics to have a more well-defined group of crowdsourced elite who largely define their side's counter-frame.

Rumors are regular features of crisis events due to the extreme uncertainty and lack of information that often characterizes these settings. Despite recent research that explores rumoring during crisis events on social media platforms, limited work has focused explicitly on how individuals and groups express uncertainty. Here we develop and apply a flexible typology for types of expressed uncertainty. By applying our framework across six rumors from two crisis events we demonstrate the role of uncertainty in the collective sensemaking process that occurs during crisis events.

This paper examines how 'official' accounts participate in the propagation and correction of online rumors in the context of crisis events. Using an emerging method for interpretive analysis of 'big' social data, we investigate the spread of online rumors through digital traces-in this case, tweets. Our study suggests that official accounts can help to slow the spread of a rumor by posting a denial, and-supported by reflections from an organization that recently dealt with a rumor-crisis-offers best practices for organizations around social media strategies and protocols. Based on tweet data and connections to existing literature, we also demonstrate and discuss how mainstream media participate in rumoring, and note the role of a new breed of online media, 'breaking news' accounts. This analysis offers a complementary perspective to existing studies that use surveys and interviews to characterize the role official accounts play in online rumoring.

In this paper we highlight three distinct approaches to studying rumor dynamics-volume, exposure, and content production. Expanding upon prior work, which has focused on rumor volume, we argue that considering the size of the exposed population is a vital component of understanding rumoring. Additionally, by combining all three approaches we discover subtle features of rumoring behavior that would have been missed by applying each approach in isolation. Using a case study of rumoring on Twitter during a hostage crisis in Sydney, Australia, we apply a mixed-methods framework to explore rumoring and its consequences through these three lenses, focusing on the added dimension of exposure in particular. Our approach demonstrates the importance of considering both rumor content and the people engaging with rumor content to arrive at a more holistic understanding of communication dynamics. These results have implications for emergency responders and official use of social media during crisis management.

During crises, the ability to access relevant information is extremely important for those affected. Previous research shows that social media have become popular for rapid information exchange between members of the online community after crisis events. This study focuses on the effects of proximity to a crisis on information sharing behaviors. Using constructivist grounded theory to guide our inquiry, we conducted interviews with eleven people who used social media in the aftermath of the 2013 Boston Marathon Bombings. Salient themes emerging from this study suggest that both physical and emotional proximity to a crisis influence online information seeking and sharing behaviors. Additionally, speed of information sharing and information access renders social media especially useful during crisis and particularly susceptible to the spread of misinformation. We view the latter as a consequence of the inevitable sensemaking process that occurs as individuals attempt to make sense of incomplete information.

This study offers an in-depth analysis of four rumors that spread through Twitter after the 2013 Boston Marathon Bombings. Through qualitative and visual analysis, we describe each rumor's origins, changes over time, and relationships between different types of rumoring behavior. We identify several quantitative measures-including temporal progression, domain diversity, lexical diversity and geolocation features-that constitute a multi-dimensional signature for each rumor, and provide evidence supporting the existence of different rumor types. Ultimately these signatures enhance our understanding of how different kinds of rumors propagate online during crisis events. In constructing these signatures, this research demonstrates and documents an emerging method for deeply and recursively integrating qualitative and quantitative methods for analysis of social media trace data.

Social media are a potentially valuable source of situational awareness information during crisis events. Consistently, "digital volunteers" and others are coming together to filter and process this data into usable resources, often coordinating their work within distributed online groups. However, current tools and practices are frequently unable to keep up with the speed and volume of incoming data during large events. Through contextual interviews with emergency response professionals and digital volunteers, this research examines the ad hoc, collaborative practices that have emerged to help process this data and outlines strategies for supporting and leveraging these efforts in future designs. We argue for solutions that align with current group values, work practices, volunteer motivations, and organizational structures, but also allow these groups to increase the scale and efficiency of their operations.

This study examines the information-processing activities of digital volunteers and other connected ICT users in the wake of crisis events. Synthesizing findings from several previous research studies of digital volunteerism, this paper offers a new approach for conceptualizing the activities of digital volunteers, shifting from a focus on organizing to a focus on information movement. Using the lens of distributed cognition, this research describes collective intelligence as transformations of information within a system where cognition is distributed socially across individuals as well as through their tools and resources. This paper demonstrates how digital volunteers, through activities such as relaying, amplifying, verifying, and structuring information, function as a collectively intelligent cognitive system in the wake of disaster events.

Humanity Road is a volunteer organization working within the domain of disaster response. The organization is entirely virtual, relying on ICT to both organize and execute its work of helping to inform the public on how to survive after disaster events. This paper follows the trajectory of Humanity Road from an emergent group to a formal non-profit, considering how its articulation, conduct and products of work together express its identity and purpose, which include aspirations of relating to and changing the larger ecosystem of emergency response. Through excerpts of its communications, we consider how the organization makes changes in order to sustain itself in rapid-response work supported in large part by episodic influxes of volunteers. This case enlightens discussion about technology-supported civic participation, and the means by which dedicated long-term commitment to the civic sphere is mobilized.

We examine the public, social media communications of 110 emergency medical response teams and organizations in the immediate aftermath of the January 12, 2010 Haiti earthquake. We found the teams through an inductive analysis of Twitter communications acquired over the three-week emergency period from 89,114 Twitterers. We then analyzed the teams' Twitter streams, as well as all digital media they generated and pointed to in their streams - blog posts, photographs, videos, status updates and field reports - to understand the medical coordination challenges they faced from pre-deployment readiness to on-the-ground action. Here we identify opportunities for improving coordination in a decentralized and distributed environment where staffing, disease trajectories, and other circumstances rapidly change. We extrapolate from these findings to theorize about how "beaconing" behavior is a sign of latent potential for coordination upon which mechanisms of coordination can capitalize.

This paper examines microblogging information diffusion activity during the 2011 Egyptian political uprisings. Specifically, we examine the use of the retweet mechanism on Twitter, using empirical evidence of information propagation to reveal aspects of work that the crowd conducts. Analysis of the widespread contagion of a popular meme reveals interaction between those who were "on the ground" in Cairo and those who were not. However, differences between information that appeals to the larger crowd and those who were doing on-the-ground work reveal important interplay between the two realms. Through both qualitative and statistical description, we show how the crowd expresses solidarity and does the work of information processing through recommendation and filtering. We discuss how these aspects of work mutually sustain crowd interaction in a politically sensitive context. In addition, we show how features of this retweet-recommendation behavior could be used in combination with other indicators to identify information that is new and likely coming from the ground.

This empirical study of "digital volunteers" in the aftermath of the January 12, 2010 Haiti earthquake describes their behaviors and mechanisms of self-organizing in the information space of a microblogging environment, where collaborators were newly found and distributed across continents. The paper explores the motivations, resources, activities and products of digital volunteers. It describes how seemingly small features of the technical environment offered structure for self-organizing, while considering how the social-technical milieu enabled individual capacities and collective action. Using social theory about self-organizing, the research offers insight about features of coordination within a setting of massive interaction.

We analyze microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service. We focus on communications broadcast by people who were "on the ground" during the Oklahoma Grassfires of April 2009 and the Red River Floods that occurred in March and April 2009, and identify information that may contribute to enhancing situational awareness (SA). This work aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques.

This paper considers a subset of the computer-mediated communication (CMC) that took place during the flooding of the Red River Valley in the US and Canada in March and April 2009. Focusing on the use of Twitter, a microblogging service, we identified mechanisms of information production, distribution, and organization. The Red River event resulted in a rapid generation of Twitter communications by numerous sources using a variety of communications forms, including autobiographical and mainstream media reporting, among other types. We examine the social life of microblogged information, identifying generative, synthetic, derivative and innovative properties that sustain the broader system of interaction. The landscape of Twitter is such that the production of new information is supported through derivative activities of directing, relaying, synthesizing, and redistributing, and is additionally complemented by socio-technical innovation. These activities comprise self-organization of information.