2016 International Conference on Collaboration Technologies and Systems
Supporting Collaborative Information Seeking in Online Community Engagement Feng Sun∗ , Ye Tian† and Guoray Cai‡ College of Information Sciences and Technology Pennsylvania State University University Park, PA 16802 USA ∗
[email protected], †
[email protected], ‡
[email protected] Abstract—Complex social issues can be better addressed collaboratively by stakeholders through democratic decisionmaking. A key prerequisite for engaging the public is the ability to gather what we know about a public issue and present such knowledge in a concise fashion. This is commonly done through a citizen’s panel who performs collaborative information seeking and analysis. The outcome of such process is to inform other local citizens to form perspectives and opinions. This work focuses on understanding collaborative information seeking behaviors in this process, and design of awareness support to collaboration. Specifically, we describe an ethnographic study that observes community practices and identifies requirements for supporting awareness in collaborative information seeking. Based on these findings, we developed a web-based visual analytic tool that supports activity awareness to encourage collaborations in seeking issue-based knowledge. Keywords—Component; formatting; style; styling; insert (key words)
I. INTRODUCTION Making decisions together through a democratic process often involves engaging stakeholders in a community through informed deliberation [1], [2]. A common barrier to effective civic engagement is the lack of a body of knowledge that represents the comprehensive understanding of an issue. Such body of knowledge is rarely accessible directly from the available data sources, but it has to be discovered through information seeking and knowledge crystallization. When a public issue is controversial, complex, and long-standing, knowledge about the issue could be scattered and buried in a large amount of data (documents, media, reports, etc) that must be carefully analyzed. This creates a data deluge on the one hand, and information overload on the other. Common ways to address this challenge is to organize some sort of randomly assembled citizen panel that performs the information seeking and analysis on behalf of their community [3]. In our research, we have developed a special version of such process, called “Community Issue Review” (CIR) [4], which is designed for engaging citizens around public issues in local communities. This CIR process involves a randomly assembled citizen panel (10-24 people) collaboratively making sense of documents concerning an issue, extracting information nuggets from documents, and using extracted nuggets to assemble statements 978-1-5090-2300-4/16 $31.00 © 2016 IEEE DOI 10.1109/CTS.2016.53
as findings. Finalized statements are shared with other local citizens to enable their formation and expression of opinions. Deliberative democracy theorists argue that a valid opinion must be based on a full consideration of all the facts and points of views [5]. Therefore, the information need of this task is unique in the sense that it requires the outcome of information seeking to be comprehensive about an issue under consideration. When the source data is large, information seeking to achieve comprehensive outcome becomes difficult to do by individuals. It will be likely to benefit from divideand-conquer and coordinated actions. Collaboration is a way to alleviate the data deluge in big data era, and information seeking with big data is most often collaborative [6]. Collaboration is an indispensable part in CIR process. First of all, this process is essentially democratic and deliberative that requires collaboration to ensure the outcome can reflect collective intelligence. Secondly, CIR as an analytic task is cognitively difficult and time-consuming for individual to handle. It can benefit from divide-and-conquer in collaborative work. Third, recognizing information nuggets from documents requires deep interpretation of data from diverse perspectives, which can benefit from joint work of multiple minds. Collaboration is capable of facilitating information seeking in CIR process, but it also comes with additional costs of communication and coordination. Collaboration could even be harmful if group members have conflicts of interest, lack intention for collaboration, or encounter trust problem [7]. [8] points out group-based work suffers from misunderstandings, decision biases, and conflicts, and proposes that boundary objects could help overcome these barriers. Therefore, one goal of enabling collaborative information seeking is to minimize the additional costs of communication and coordination by providing boundary objects and awareness among participants. This paper frames information seeking within community issue review as a collaborative task. Drawing from existing literature concerning information seeking, social search, sensemaking, and collaboration, as well as our own practices and experiments, we are able to provide the insights of how collaboration should be supported in such context. We particularly focus on developing a visual analytic tool that manages and promotes the awareness of information and task environments. We illustrate the features of the system through
241
several scenarios of use and discuss future work in improving collaborative information seeking of political knowledge in communities. II. BACKGROUND A. Collaborative Information Seeking (CIS) Information seeking is a process of attempting to obtain information purposely in order to satisfy certain information needs (either discovering patterns or filling in recognized information gaps) [9], [10]. It differs from other information behavior in its emphasis on purposeful activity and close relation to the information need. Human information seeking behavior remains to be poorly understood and seems to be ad hoc at times. However, researchers have discovered some recognizable patterns of information seeking process and captured such understanding into various conceptual models of information seeking [11]–[17]. They vary from each other in terms of structure, purposes, assumptions and application domains. One set of models describes the sequence and relationships of activities involved in information seeking as procedures that can be represented in flowcharts [14], [18]. Such models are more applicable to professional information seekers (such as students and lawyers) who have developed some structured methods of information seeking. In other application fields, rigid structures of information seeking may not exist, but still, there are certain recognizable “stages” and actions. For example, Kuhlthau’s model includes stages of initiation, selection, exploration, formulation, collection, and presentation. Ellis’s model identifies a list of actions involved in information seeking activity, including starting, browsing, chaining, monitoring, differentiating, extracting, verifying and ending [11]. Stagebased models tend to be more flexible and adaptive. Both stage-based models and procedural models characterized information seeking as somewhat a linear process, and they failed to consider human factors that can influence both the process and products of information seeking. In response to such criticisms, Kuhlthau [12] argued for enriching stagebased models with user’s feelings and thoughts. Ellis’s model was also enriched with more iterative flexibility by incorporating the modes of exploration and hierarchical natural of goals [15]. Foster [17] proposed a non-linear model of information seeking. It is worth noting that earlier models of information seeking usually conceptualize it primarily from the individual perspective. With the increasing and more accessible usergenerated content, online sharing, and other types of information, information sources, volumes, and complexities have grown exponentially which makes information seeking so challenging and demanding that goes beyond individual’s capacity. As a way forward, researchers turned their attention to collaborative aspects of information seeking. Collaborative
information seeking (CIS) concerns the understanding of how individuals work together in seeking information for satisfying a goal and how to support such activities using technologies Foster [19] [20]. Collaboration has benefits in information seeking process in terms of division of labor, diversity of skills, and reducing information complexity [21]. Collaborative information seeking differs from individual one in terms of the trigger, and the roles of communication and information retrieval technology [22]. The triggers of collaboration in information seeking include the complexity of information, distributed information resources, lack of expertise, and requirements imposed by tasks. In addition, communication plays a more important role in collaborative information seeking for coordination and promote awareness [22]. Information seeking practices are normally embedded within certain application domain which defines the nature of the data, the goals of information seeking, and the participants. Collaborative information seeking has been studied in a number of applications domains such as health care [22], software design [23], web search [24], and patent processing [25]. The application domain of this paper is in democratic decisionmaking in local communities, which is different from other domains in three aspects: (1) the data environment is messy with varying degree of truth or trustworthiness, because public opinions are value-driven human judgment and perceptions of the controversial issue; (2) the goal of information seeking is to inform considered opinions, which requires seeking to capture critical knowledge in the data without much loss; (3) the participants are citizens which are normally not as sophisticated information seekers as those professionals. B. Systems and Tools for CIS Systems and tools dedicated to supporting collaborative Information Seeking in a specific domain, setting, or task are still limited. Tools designed for web search tasks have received the most attention. For example, Paul and Morris [26] developed a tool, called CoSense, that support multiple users to perform investigative web search tasks collaboratively. CoSense focuses on the representation of activities about a given search session and supports the handoff of sensemaking between asynchronous information seekers. Paul and Morris argued that a group of collaborative information seekers should make sense of not only products of searching results, but also the process. Based on such understanding, they further designed a collaborative web searching prototype: SearchTogether [24]. Through analyzing an example usage scenario, they demonstrated that collaborative features of SearchTogether support awareness, division of labor, and persistence of knowledge repository, and can help avoid undesired duplicates of work. Going beyond web search tasks, Coagmento [27] supports interactive and collaborative writing using found information by providing a workspace in which users can interact with
242
collected information, including organizing, sharing, and visualizing for better sensemaking and reusing of information. It was later extended to incorporate new methods for capturing users action (activity history) in online search to which collaborators can refer [28]. Collaborative information seeking (CIS) has also been investigated under the umbrella of social information seeking, and several systems have been designed to utilize social activities and relationships to prompt users to seek information, such as Footprint [29], MrTaggy [30], and Dogear [31]. These systems keep track of users’ activities and their social relationships, and this information serves as the clues to which users can refer for more effective and efficient information seeking. Footprint [29] transforms interaction history into navigation tool based on the idea of history-enriched digital objects. MrTaggy [30] and Dogear [31] utilize social bookmarking and tagging techniques for information retrieval and information discovery. Some systems and tools have been developed for CIS, but they have not “enjoyed widespread success” by far [32]. On the one hand, systems and tools that were developed for traditional application contexts can not address new scenarios of information seeking in novel applications [32]. Second, a number of features are still missing from existing CIS tools [33]. The missing features mentioned above are summarized as follows: • bring awareness of what has already been gathered, viewed, and gathered by anyone in the group, enabling easy comparison of them to avoid duplicate and redundant work by providing a clear depiction of both the covered and yet-to-be-explored information landscape (F1); • support tracking knowledge development and corresponding activities, enhancing human cognitive capability in awareness interpretation and use by providing not only produces but also processes (F2); • offer the analyst a sense of accomplishment, a feeling of walking over the information landscape, and have their bearing on the terrain as they work, enabling them to choose strategies about who is to do what next appropriate to the situation of the information environment (F3); • provide efficient ways of organizing, structuring and prioritizing collected information to alleviate people’ internal cognitive burden (F4).
Figure 1: Task structure
in conducting CIR effectively and efficiently. First of all, community issue is usually too complicated and covers a variety of aspects. The complexity of the available data requires an in-depth understanding and interpretation of the data, which is too difficult for an individual to handle. Secondly, CIR brings additional requirements of ensuring comprehensiveness and completeness in collaborative information seeking, which differs CIR from many settings of which the information space appears to be infinite (e.g., library database, the internet). Thirdly, the amount of data (documents in particular) can be so large for an individual to handle and the number of themes to be considered can go beyond what one can handle with active memory. Therefore, being able to evaluate the completeness of information with regards to the given issue becomes more important in CIR than search.
A. Community Issue Review
This paper focuses on the second phase in which panelists collaboratively seek and gather relevant information nuggets that have the potential to be useful for assembling claims. The nugget extraction task in CIR involves activities of navigating through a collection of documents, reading the documents, recognizing relevant information contents (as nuggets), and extracting nuggets from the documents (see Fig. 1). Each extracted nugget is assigned to one of the predefined themes. These themes and the relationships among them constitute the knowledge schema about the public issue that CIR is analyzing. During nugget extraction process, panelists need to make several decisions: (1) decide if a piece of data is relevant; (2) decide which theme should be assigned to an extracted nugget; (3) decide where to look for more nuggets next; (4) decide if all the information nuggets have been found. They need to constantly manage what they have found and where are unexplored opportunities, and keep in touch with each other to monitor the progress of their collaborative work.
Community issue review (CIR) process consists of five phases: comprehension of documents, extracting information nuggets, assembling claims, improving claims, and representing final statements. Participants are randomly selected citizens that form a review penal. However, panelists have difficulties
Collaborative information seeking in CIR can be better understood using information foraging theory [34]. This theory suggests that people typically use a variety of information seeking strategies when navigating through documents for seeking information nuggets. Specific strategies are chosen at
III. I NFORMATION SEEKING IN CIR
243
a certain situation for the purpose of optimizing rate-of-return and minimizing costs. One important concept in information foraging theory is that relevant information nuggets exist in documents (information space) as “patches”. Information foragers have to adopt appropriate strategies concerning interpatch and intra-patch moves for better exploring the space. Much of the costs in information seeking is involved in interpatch and intra-patch moves for relocating oneself from one document area to another. Such costs can be reduced when collaborator carefully divide their work into different areas of the patchy information environment. It allows individuals who are knowledgeable about certain patches to maximize chances of completeness in a shorter time. Second, collaborators bring into the process a diversity of expertise, experiences, and perspectives about a public issue. Such knowledge is critical because the issue under consideration can be deep and long-standing community concern that is complex and multi-faceted. They may disagree on how to interpret data and they can jointly create the contextual knowledge necessary for understanding and insight. B. Challenges of Supporting CIS Although collaboration can benefit CIR information seeking process in theory, certain difficulties exist that prevent effective and efficient collaboration from happening. For example, it is challenging for a panelist to evaluate collaborators’ work quickly and accurately due to lack of access to the most up-to-date knowledge about their collaborative workspace. To manage such awareness information depending on the limited human cognitive resource is not viable. These challenges call for additional technological support. IV. U NDERSTANDING THE N EEDS FOR AWARENESS S UPPORT As we target designing tools for supporting awareness for collaborative information seeking in CIR, we realize that there is a lack of understanding on what kinds of awareness information that are necessary for making decisions and choosing strategies for information foraging. In order to elicit requirements of supporting CIS in CIR, we conducted an ethnographic study of real collaboration involving nugget extraction task. We expected this formative study to answer the following questions: When is there a need for collaboration? What shared artifacts can be used to coordinate collaboration? What are benefits and additional costs introduced by collaboration perceived by users? A. Study Design 1) Participants: Six participants are recruited from undergraduate students majored in Information Sciences and Technology at Penn State University. At the time when this
Figure 2: User Interface of NuggetExtractor
ethnographic study was conducted, they were enrolled in an undergraduate course on “visual analytics” in the middle of the semester. 2) Data preparation: The issue we chose for this study is “Collegiate Housing Overlay (CHO)”, which was around a pending ordinance that attempts to incentivize commercial builders to include housing features (diverse apartment sizes, commercial areas on the first floor, etc). 3) Tool environment: For conducting this experiment, we developed and deployed a web-based tool called NuggetExtractor. The user interface consists of several components, as shown in Fig. 2. Document panel is the workspace where users can read documents and extract information nuggets. Collected nuggets are listed in the information panel, where users can make claims and discuss them. Users can browse all the documents in sequence or quickly access a relevant one by navigating through the table of contents available in the document panel. Once a piece of texts is selected, a context menu will pop up, allowing users to extract it as an information nugget, raise a question or comment. After that, other collaborators’ document views will be updated accordingly. Extracted nuggets are stored in nugget list panel in which all nuggets are collected and sorted by time. Once a nugget is newly extracted, it will immediately appear in the top of the list. Context menu associated with each nugget allows individual to use it to construct a new claim or trace back to the document where the nugget is from. Users are enabled to comment and discuss these objects. Since the system allows panelists to work on nugget extraction individually, there is a tendency for the panel to generate nuggets that contain duplication of similar information. To address this concern, the system provides three features: (1) highlights the extracted nuggets in documents to alert other late visitors; (2) allows the panel to use a divide-and-conquer strategy to distribute efforts to different parts of the data space; (3) supports coordination through instant communication (chat
244
room). 4) Experimental protocol: Before performing tasks specified in Fig. 1, each participant is given a short introduction to the issue (Collegiate Housing Overlay) and a training session on how to use the tool environment. A researcher introduced the community issue review in general and particularly described the nugget extraction task in terms of its process, expected outcome, as well as the themes that help organize the knowledge on CHO issue. In the training session, the researcher firstly explained to the participants the user interface and available functionalists. After that, the participants were given about 10 minutes to interact with the system using sample data with the help of the instructor. Then, participants started to perform tasks. Although seated in the same room, they were required to use the chat room provided by the system to communicate as if in a distributed situation. During the process, we used standard ethnographic techniques for observing people. 5) Observations and analysis: Participants’ physical activities were video recorded. Their online activities were logged in the system, including their contributions, communication, and active patterns. After the system logs, observations and interviews were transcribed. Their activities were coded as making choices of staying within the current place, selecting another document, hesitating to move, communicating with other collaborators. The formative study combines observations, system logs, and semi-structured interview to dive deep into the intentions behind their activities. B. Findings
completed work. As the process continued, each participant became an immediately accessible information resource to the rest. 2) Seeking Beyond Search: Our observations and interviews show that the participants sought information and judged the relevance by scanning titles and skimming contents, rather than by reading them fully. However, due to people’s limited cognitive resources, they failed to remember all of the information, let alone organizing perceived contents well. This led to a misunderstanding of information and an omission of information. 3) Situation Awareness: The participants tended to work on their own during online setting since they had asynchronous focuses and could not receive immediate feedback from others, resulting in the problems of duplicate work and inaccurate judgments. However, during the interview, they expressed interests in knowing how other collaborators navigate the documents and identify relevant information. Observation confirmed that the same information nuggets were being used and re-used by different participants for assembling different claims. 4) Question and Answer: By reviewing online chatting messages, we found that valuable questions raised in our system usually received answers of high quality, compared to the immediate answers received in face-to-face CIR. This is because the system forwarded questions to the right persons and allowed them to have enough time to collect and organize evidence, and then respond with confidence. C. Design implications
The pilot CIR revealed the need for collaboration support in many activities, including identifying information needs, making sense of documents, extracting information nuggets, communicating about information needs and sharing retrieved information, and coordinating information retrieval activities. 1) Task Structure: Information seeking task is an iterative process in which a set of subtasks interleave with each other. For example, we noticed that some participants extracted a nugget and immediately made a claim based on the extracted nugget they work mostly individually since there are no currently available relevant nuggets from other collaborators. 1) Dynamic Roles: We found that in CIR the roles of the participants are dynamically and socially negotiated depending on different strategies of the division of labor, which is different from some collaboration settings in which participants come with predetermined roles. All of the participants were newbies towards a community issue (though they had heard about the issue before). They started from a different set of documents at the beginning, and as time proceeded, each individual gained more diverse knowledge about the same issue as if they were equipped with different expertise. Some of them served as major drivers while others evaluated and examined
In order to better streamline the process, a phase-based design is desired (D1) so that the participants can work relatively synchronously and their contributions can be evaluated and utilized collaboratively. The quality of extracted nuggets is expected to be increased for their receiving contributions from multiple sources. To achieve this, each phase is carefully defined in terms of expected input, process, and output. Only when participants feel that the time is right for evaluating and using the found information, CIR will move to the next phase and adopt output from the previous phase as input. Given the dynamic roles played by the participants, dividing the participants into smaller groups that cover different documents and themes is considered a more efficient strategy than asking all of them to do all work since it encourages them to engage by leveraging their developing knowledge and avoids potential lurking and redundant work (D2). Due to the limitation of human memory and attention, the system should take advantages of its capability of storing data, visualizing information, and computational power to augment participants’ capacity in managing work histories, changes in workspace, and communication artifacts (D3).
245
(a) Nugget extraction workspace (b) NuggetLens
Figure 3: User interface (the latest version)
In terms of situation awareness, information forager is expected to be aware of various aspects of the searching and sensemaking processes, including the task and its context, past and present actions, and various attributes of the information objects and the system [35]. This is a salient aspect of a CIS process when the information landscape is complex and dynamically changing, and the task lasts several sessions, while an individual only has limited cognitive resources and is typically only involved in a part of them. In CIR, information nuggets extraction phase is considered complete when participants feel that the time is right for evaluating and using the extracted information nuggets. But what is the right time? If it is not the right time yet, what is the current progress? Answering these frequently asked questions requires an immediate evaluation of progress in terms of information development, both the activities associated with and the outcome of the process (D4). This led to further questions, how to take advantage of the traces left by participants as navigation tools (D5), and how to provide this information as context for communication and coordination (D6).
Sankey diagram [36] representation for depicting the task environment, while the DocuMap is customized to represent information environment based on parallel heatmaps [37]. The bar chart not only shows the trends of nugget extraction activities but also allows users to specify a time range that enables them to investigate environments over time. The interactive Sankey diagram is designed for displaying and highlighting who has done what during nugget extraction. It uses three sets of nodes to represent documents, panelists, and themes, respectively. We use distinct colors to encode different categories of elements: green is used for encoding document, red for panelist and yellow for themes. Links (representing nuggets) connect nodes of document, panelist, and theme, making it easy to find where a nugget came from and which theme is associated with. The height of each node represents the total number of nuggets related that node, and the thickness of a link signals the number of nuggets that were processed through that pipe.
A. NuggetLens
Sankey diagram in Fig. 3b shows the overview of progress at a glance. A panelist can analyze a node and its connections by clicking on the node. As a result, all links, including the related edges and nodes, are all highlighted, allowing a user to analyze the development of information concerning the selected node. Sankey diagram also supports flow tracing. For example, as a panelist node is selected, all the documents he has worked on and all the themes he has contributed to will be highlighted. The Sankey diagram also features detail on demand. When hovering the mouse over either a node or an edge, additional information is provided. For each node/link, hovering leads to a customized tooltip shows the basic information about the node/link depending on the category. If a panelist finds a document node interesting, a link to the corresponding document will be provided.
NuggetLens consists of a DocuMap, a Sankey diagram, a Document Preview panel, a zoomable bar chart, and a set of filters, as shown in Fig. 3. To be aligned with the design goals of supporting situation awareness, we adopt a
DocuMap is made up of a collection of parallel heatmaps. A parallel heatmap is a visualization technique that placed the partial representations of the same datasets in parallel [37]. In NuggetLens, each heatmap represents a document and can be
A prioritization mechanism might work to ensure irrelevant questions are ignored (D7) in terms of question-and-answer process. V. S YSTEM D ESIGN Findings identified in the pilot CIR suggest a set of design implications that inform the design of our system. We extended the previous online platform by incorporating an interactive visualization dashboard, NuggetLens, to promote awareness, support collaboration, and improve the usability and efficiency.
246
subdivided vertically into two parts. The larger left part shows the distribution of extracted nuggets within the document, and the smaller right part shows a heatmap that visualizes how individual sensemaking time is allocated across each part of a document. The parallel heatmap enables users to make sense of the patterns and properties of the changing information landscape in order to rapidly assess the task progress. Besides, all members of a group are represented as icons and positioned at the latest focused area by default and users’ names are positioned alongside icons. The zoomable bar chart shows the number of nuggets in an useradjustable time interval. The smaller bar chart shows the overview of the number of nuggets, while a user can select a specific time range through click-and-drag action within the overview bar chart. After the time window is set, the bigger detail bar chart will only show the number of nugget within that time range. Figure 4: DocuMap
The views described above are coordinated. When a document node within Sankey diagram is clicked, the corresponding bar with DocuMap is highlighted, and vice versa. After clicking another category of nodes in Sankey diagram, the bar chart will be updated by showing only activities related to the selected entity. If a user finds a part of documents and clicks that part within in heatmap bar, the document will be opened in a preview panel with word cloud and is direly scrolled to the corresponding region. In addition, the zoomable bar chart serves as time slider as well. B. Awareness Support Through NuggetLens NuggetLens provides a set of features for supporting awareness that is necessary for users to adjust information seeking strategies. Information about the changing information environment and activity context are available through interacting with visualization without having to request explicitly. It creates a sense of presence in asynchronous collaboration with more accurate, accessible and just-in-time context, and supports coordination in terms of division of lab and job pickup. NuggetLens can be used by panelists to evaluate task progress in terms of both processes and products. There is a constant need for evaluating whether the ongoing phase is complete and ready to proceed to the next. If further work needs to be done within the current phase, where spend efforts are spent. DocuMap shows the coverage of extracted nuggets, as well as received attention. If some contents have not been looked at yet, more attention will be made to that place. Or if most of the information space is covered, panelists can proceed with confidence.
Sankey diagram provides information about each panelist’s contributions to documents and themes by a certain moment, enabling communication in context (D3). When initiating a contact with a collaborator, a panelist can know the collaborator’s latest progression; or when replying a collaborator in an asynchronous setting, the panelist is able to know the collaborator’s status when posting chatting messages. NuggetLens allows users to explore information nugget extraction activities in both spatial (DocuMap) and temporal (time sliders) dimension (D4, D5, D6), it could be super useful for job pick-up and handover by building links among elements and making rationale behind actions more explicit. No matter a panelist need to delegate his work to somebody else or he need to pick up someone else’ work, he can easily do so by interacting with NuggetLens to learn about the activity history and resulting outcomes. C. Discussion Supporting awareness through interactive visual interfaces to visual representations of the information environment and activity context has a lot of potentials to reduce the barriers of collaboration in collaborative information seeking. Individuals have control on when to access awareness information and how to use them, therefore presenting little disruptions to the main work. It was clear that users can interpret the visualizations provided by NuggetLens and derive insight about their task status. However, it remains unclear whether NuggetLens eventually results in any improvement in the quality of the extracted nuggets. We also observed that the level of motivation and commitment to the CIR tasks can make a huge difference in the outcome In addition, different participants have various contribution pattern. Due to the nature of the task, all participants are selfmotivated. However, some of them prefer to make explicit contributions (as much coverage as possible) while others tend to focus on certain aspects in more depth. Future systems are expected be able to capture and represent various types of contributions. VI. C ONCLUSION This study explores the collaboration needs within a special form of knowledge crystallization process. A formative study was conducted and the design implications derived from both the findings and literature led to a prototype application. While this work advances the understanding of collaboration in nugget extraction tasks and leads to some promising findings and a workable system, further experiments with panelists in real CIR setting are required to demonstrate the effectiveness of our design. This work, if successfully accomplished, will enable a more effective analysis of complex community issues and thus provide a better approach of opinion-formation among communities.
247
ACKNOWLEDGMENTS We would like to acknowledge funding support from National Science Foundation under award # IIS-1211059.
[20] A. Karunakaran, M. C. Reddy, and P. R. Spence, “Toward a model of collaborative information behavior in organizations,” Journal of the American Society for Information Science and Technology, vol. 64, no. 12, pp. 2437–2451, dec 2013. [21] C. Shah, “Collaborative information seeking: From what?and why?to how?and so what?,” in Collaborative Information Seeking. Springer, 2015, pp. 3–16.
R EFERENCES [1] S. Chambers, “Deliberation and mass democracy,” in Deliberative Systems: Deliberative Democracy at the Large Scale. Cambridge University Press, 2012, pp. 52–71. [2] T. Nabatdni, Democracy in motion: evaluating the practice and impact of deliberative civic engagement. Oxford; New York: Oxford University Press, 2012. [3] J. Gastil and R. Richards, “Making Direct Democracy Deliberative through Random Assemblies,” Politics & Society, vol. 41, no. 2, pp. 253–281, 2013. [4] J. N. Kropczynski, G. Cai, and J. M. Carroll, “Understanding the Roles of Artifacts in Democratic Deliberation from the Citizens’ Initiative Review,” Journal of Social Media for Organizations, vol. 3, no. 2, pp. 1–22, 2015. [5] D. Hicks, “The Promise(s) of Deliberative Democracy,” Rhetoric & Public Affairs, vol. 5, no. 2, pp. 223–260, 2002. [6] D. Fisher, R. DeLine, M. Czerwinski, and S. Drucker, “Interactions with big data analytics,” Interactions, vol. 19, no. 3, pp. 50–59, 2012. [7] S. London, “Collaboration and community,” Retrieved July, vol. 12, p. 2011, 1995. [8] M. J. Eppler and S. Kernbach, “Supporting knowledge-intensive collaboration through interactive dynamic diagrams,” in 2015 International Conference on Collaboration Technologies and Systems (CTS), vol. 1. Cambridge: IEEE, jun 2015, pp. 272–279. [9] D. O. Case, Looking for information: A survey of research on information seeking, needs and behavior. Emerald Group Publishing, 2012. [10] T. D. Wilson, “Human information behavior,” Informing science, vol. 3, no. 2, pp. 49–56, 2000. [11] D. Ellis, “A behavioural approach to information retrieval system design,” Journal of documentation, vol. 45, no. 3, pp. 171–212, 1989. [12] C. C. Kuhlthau, “Inside the search process: Information seeking from the user’s perspective,” Journal of the American society for information science, vol. 42, no. 5, p. 361, 1991. [13] D. Ellis, “Modeling the information-seeking patterns of academic researchers: A grounded theory approach,” The Library Quarterly, pp. 469–486, 1993. [14] T. D. Wilson, “On user studies and information needs,” Journal of documentation, vol. 37, no. 1, pp. 3–15, 1981. [15] ——, “Models in information behaviour research,” Journal of documentation, vol. 55, no. 3, pp. 249–270, 1999. [16] B. Dervin, “Sense-making theory and practice: an overview of user interests in knowledge seeking and use,” Journal of knowledge management, vol. 2, no. 2, pp. 36–46, 1998. [17] A. Foster, “A nonlinear model of information-seeking behavior,” Journal of the American society for information science and technology, vol. 55, no. 3, pp. 228–237, 2004.
[22] M. C. Reddy and B. J. Jansen, “A model for understanding collaborative information behavior in context: A study of two healthcare teams,” Information Processing & Management, vol. 44, no. 1, pp. 256–273, jan 2008. [23] S. Poltrock, J. Grudin, S. Dumais, R. Fidel, H. Bruce, and A. M. Pejtersen, “Information seeking and sharing in design teams,” in Proceedings of the 2003 international ACM SIGGROUP conference on Supporting group work. ACM, 2003, pp. 239–247. [24] M. R. Morris and E. Horvitz, “Searchtogether: an interface for collaborative web search,” in Proceedings of the 20th annual ACM symposium on User interface software and technology. ACM, 2007, pp. 3–12. [25] P. Hansen and K. J¨arvelin, “Collaborative Information Retrieval in an information-intensive domain,” Information Processing & Management, vol. 41, no. 5, pp. 1101–1119, sep 2005. [26] S. a. Paul and M. R. Morris, “CoSense: Enhancing Sensemaking for Collaborative Web Search,” in Proceedings of the 27th international conference on Human factors in computing systems - CHI 09. New York, New York, USA: ACM Press, 2009, p. 1771. [27] C. Shah, “Coagmento-a collaborative information seeking, synthesis and sense-making framework,” Integrated demo at CSCW, pp. 6–11, 2010. [28] R. Gonz´alez-Ib´an˜ ez and C. Shah, “Coagmento: A system for supporting collaborative information seeking,” proceedings of the American Society for Information Science and Technology, vol. 48, no. 1, pp. 1–4, 2011. [29] A. Wexelblat and P. Maes, “Footprints: history-rich tools for information foraging,” in Proceedings of the SIGCHI conference on Human Factors in Computing Systems. ACM, 1999, pp. 270–277. [30] Y. Kammerer, R. Nairn, P. Pirolli, and E. H. Chi, “Signpost from the masses: learning effects in an exploratory social tag search browser,” in Proceedings of the 27th international conference on Human factors in computing systems - CHI 09. New York, New York, USA: ACM Press, 2009, p. 625. [31] D. R. Millen, J. Feinberg, and B. Kerr, “Dogear: Social bookmarking in the enterprise,” in Proceedings of the SIGCHI conference on Human Factors in computing systems. ACM, 2006, pp. 111–120. [32] M. A. Hearst, “What’s missing from collaborative search?” Computer, vol. 47, no. 3, pp. 58–61, Mar. 2014. [33] Y. Tao and A. Tombros, “Investigating Collaborative Sensemaking Behavior in Collaborative Information Seeking,” Computer, vol. 47, no. 3, pp. 38–45, mar 2014. [34] P. Pirolli and S. Card, “Information foraging.” Psychological review, vol. 106, no. 4, p. 643, 1999. [35] C. Shah, “Effects of awareness on coordination in collaborative information seeking,” Journal of the American Society for Information Science and Technology, vol. 64, no. 6, pp. 1122–1143, jun 2013.
[18] J. Krikelas, “Information-seeking behavior: Patterns and concepts.” Drexel library quarterly, vol. 19, no. 2, pp. 5–20, 1983.
[36] P. Riehmann, M. Hanfler, and B. Froehlich, “Interactive sankey diagrams,” in Information Visualization, 2005. INFOVIS 2005. IEEE Symposium on. IEEE, 2005, pp. 233–240.
[19] J. Foster, “Collaborative information seeking and retrieval,” Annual Review of Information Science and Technology, vol. 40, no. 1, pp. 329– 356, sep 2007.
[37] R. M. Podowski, B. Miller, and W. W. Wasserman, “Visualization of complementary systems biology data with parallel heatmaps,” IBM journal of research and development, vol. 50, no. 6, pp. 575–581, 2006.
248
