Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine Annotation Study

被引:0
|
作者
Anna De Liddo
Ágnes Sándor
Simon Buckingham Shum
机构
[1] The Open University,Knowledge Media Institute
[2] Xerox Research Centre Europe,undefined
来源
Computer Supported Cooperative Work (CSCW) | 2012年 / 21卷
关键词
collective intelligence; discourse; human annotation; knowledge mapping; machine annotation; learning; sensemaking; network visualization; social software; social annotation;
D O I
暂无
中图分类号
学科分类号
摘要
We propose the concept of Contested Collective Intelligence (CCI) as a distinctive subset of the broader Collective Intelligence design space. CCI is relevant to the many organizational contexts in which it is important to work with contested knowledge, for instance, due to different intellectual traditions, competing organizational objectives, information overload or ambiguous environmental signals. The CCI challenge is to design sociotechnical infrastructures to augment such organizational capability. Since documents are often the starting points for contested discourse, and discourse markers provide a powerful cue to the presence of claims, contrasting ideas and argumentation, discourse and rhetoric provide an annotation focus in our approach to CCI. Research in sensemaking, computer-supported discourse and rhetorical text analysis motivate a conceptual framework for the combined human and machine annotation of texts with this specific focus. This conception is explored through two tools: a social-semantic web application for human annotation and knowledge mapping (Cohere), plus the discourse analysis component in a textual analysis software tool (Xerox Incremental Parser: XIP). As a step towards an integrated platform, we report a case study in which a document corpus underwent independent human and machine analysis, providing quantitative and qualitative insight into their respective contributions. A promising finding is that significant contributions were signalled by authors via explicit rhetorical moves, which both human analysts and XIP could readily identify. Since working with contested knowledge is at the heart of CCI, the evidence that automatic detection of contrasting ideas in texts is possible through rhetorical discourse analysis is progress towards the effective use of automatic discourse analysis in the CCI framework.
引用
收藏
页码:417 / 448
页数:31
相关论文
共 12 条
  • [1] Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine Annotation Study
    De Liddo, Anna
    Sandor, Agnes
    Shum, Simon Buckingham
    COMPUTER SUPPORTED COOPERATIVE WORK-THE JOURNAL OF COLLABORATIVE COMPUTING AND WORK PRACTICES, 2012, 21 (4-5): : 417 - 448
  • [2] Vulnerability Discovery in Network Systems Based on Human-Machine Collective Intelligence
    Han, Ye
    Chen, Jianfeng
    Rao, Zhihong
    Wang, Yifan
    Liu, Jie
    INTELLIGENT HUMAN SYSTEMS INTEGRATION 2020, 2020, 1131 : 453 - 458
  • [3] CONCEPTUAL FRAMEWORK OF A HUMAN-MACHINE COLLECTIVE INTELLIGENCE ENVIRONMENT FOR DECISION SUPPORT
    Smirnov, Alexander
    Ponomarev, Andrew
    Levashova, Tatiana
    Shilov, Nikolay
    COMPTES RENDUS DE L ACADEMIE BULGARE DES SCIENCES, 2022, 75 (01): : 102 - 109
  • [4] Decision Support Based on Human-Machine Collective Intelligence: Major Challenges
    Smirnov, Alexander
    Ponomarev, Andrew
    INTERNET OF THINGS, SMART SPACES, AND NEXT GENERATION NETWORKS AND SYSTEMS, NEW2AN 2019, RUSMART 2019, 2019, 11660 : 113 - 124
  • [5] COHUMAIN: Building the Socio-Cognitive Architecture of Collective Human-Machine Intelligence
    Gonzalez, Cleotilde
    Admoni, Henny
    Brown, Scott
    Woolley, Anita Williams
    TOPICS IN COGNITIVE SCIENCE, 2023,
  • [6] Including Collective Intelligence in Human-Machine Interactive Decision-Making under Time Constraints
    Sasaki, Hideyasu
    2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2011, : 1609 - 1614
  • [7] Human-machine collaboration in intelligence analysis: An expert evaluation
    Toniolo, Alice
    Cerutti, Federico
    Norman, Timothy J.
    Oren, Nir
    Allen, John A.
    Srivastava, Mani
    Sullivan, Paul
    INTELLIGENT SYSTEMS WITH APPLICATIONS, 2023, 17
  • [8] Methodology for Multi-Aspect Ontology Development: Ontology for Decision Support Based on Human-Machine Collective Intelligence
    Smirnov, Alexander
    Levashova, Tatiana
    Ponomarev, Andrew
    Shilov, Nikolay
    IEEE ACCESS, 2021, 9 (09): : 135167 - 135185
  • [9] Context-aware Knowledge Management for Socio-Cyber-Physical Systems: New Trends towards Human-machine Collective Intelligence
    Smirnov, Alexander
    Shilov, Nikolay
    Ponomarev, Andrew
    PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT (KDIR), VOL 1, 2020, : 5 - 17
  • [10] Bridging human and machine learning for the needs of collective intelligence development
    Gavriushenko, Mariia
    Kaikova, Olena
    Terziyan, Vagan
    INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING (ISM 2019), 2020, 42 : 302 - 306