Digital crowdsourcing and public understandings of the past: citizen historians meet Criminal Characters

© 2020 Australian Historical Association. Criminal Characters is a research project examining both who criminals actually were, and who they were imagined to be, in order to deconstruct historical and contemporary understandings of ‘the criminal’ as a form of social identity. In particular, it aims to deepen public and academic understandings of the characteristics of historical offenders by using crowdsourcing to transcribe the detailed biographic and criminal career information held in Victoria’s prison registers from the 1850s to 1940s. This article will use Criminal Characters as a case study for discussing the challenges and opportunities presented by engaging public volunteers to perform research tasks. It will question the degree to which the terms ‘crowdsourcing’ and ‘citizen science’ can be considered interchangeable, and how digital history projects can be designed to incorporate crowdsourcing in ways that facilitate volunteers becoming ‘citizen historians’ who gain greater historical literacy as a result of their contributions. The benefits of such collaborative processes and knowledge exchange for criminal justice history will be explored

Flud: a hybrid crowd-algorithm approach for visualizing biological networks

Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this paper, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, an online game with a purpose (GWAP) that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Further, we propose a novel hybrid approach for graph layout wherein crowdworkers and a simulated annealing algorithm build on each other's progress. To showcase the effectiveness of Flud, we recruited crowd workers on Amazon Mechanical Turk to lay out complex networks that represent signaling pathways. Our results show that the proposed hybrid approach outperforms state-of-the-art techniques for graphs with a large number of feedback loops. We also found that the algorithmically generated suggestions guided the players when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in human computation games.Comment: This manuscript is currently under revie

Supporting Exploratory Search Tasks Through Alternative Representations of Information

Information seeking is a fundamental component of many of the complex tasks presented to us, and is often conducted through interactions with automated search systems such as Web search engines. Indeed, the ubiquity of Web search engines makes information so readily available that people now often turn to the Web for all manners of information seeking needs. Furthermore, as the range of online information seeking tasks grows, more complex and open-ended search activities have been identified. One type of complex search activities that is of increasing interest to researchers is exploratory search, where the goal involves "learning" or "investigating", rather than simply "looking-up". Given the massive increase in information availability and the use of online search for tasks beyond simply looking-up, researchers have noted that it becomes increasingly challenging for users to effectively leverage the available online information for complex and open-ended search activities. One of the main limitations of the current document retrieval paradigm offered by modern search engines is that it provides a ranked list of documents as a response to the searcher’s query with no further support for locating and synthesizing relevant information. Therefore, the searcher is left to find and make sense of useful information in a massive information space that lacks any overview or conceptual organization. This thesis explores the impact of alternative representations of search results on user behaviors and outcomes during exploratory search tasks. Our inquiry is inspired by the premise that exploratory search tasks require sensemaking, and that sensemaking involves constructing and interacting with representations of knowledge. As such, in order to provide the searchers with more support in performing exploratory activities, there is a need to move beyond the current document retrieval paradigm by extending the support for locating and externalizing semantic information from textual documents and by providing richer representations of the extracted information coupled with mechanisms for accessing and interacting with the information in ways that support exploration and sensemaking. This dissertation presents a series of discrete research endeavour to explore different aspects of providing information and presenting this information in ways that both extraction and assimilation of relevant information is supported. We first address the problem of extracting information – that is more granular than documents – as a response to a user's query by developing a novel information extraction system to represent documents as a series of entity-relationship tuples. Next, through a series of designing and evaluating alternative representations of search results, we examine how this extracted information can be represented such that it extends the document-based search framework's support for exploratory search tasks. Finally, we assess the ecological validity of this research by exploring error-prone representations of search results and how they impact a searcher's ability to leverage our representations to perform exploratory search tasks. Overall, this research contributes towards designing future search systems by providing insights into the efficacy of alternative representations of search results for supporting exploratory search activities, culminating in a novel hybrid representation called Hierarchical Knowledge Graphs (HKG). To this end we propose and develop a framework that enables a reliable investigation of the impact of different representations and how they are perceived and utilized by information seekers