ABSTRACT
Traditional news outlets as carriers and distributors of information have been challenged by online social networks with regards to their gate-keeping function. We believe that only a combined effort of people and machines will be able to curb so-called “fake news” at scale in a decentralized Web. In this position paper, we propose an approach to design social machines that coordinate human- and machine-driven credibility assessment of information on a decentralized Web. To this end, we defined a fact-checking process that draws upon ongoing efforts for tackling disinformation on the Web, and we formalized this process as a multi-agent organisation for curating W3C Web Annotations. We present the current state of our prototypical implementation in the form of a browser plugin that builds on the Hypothesis annotation platform and the JaCaMo multi-agent platform. Our social machines would span across the Web to enable collaboration in form of public discourse, thereby increasing the transparency and accountability of information.
- Alexandre Alaphilippe, Alexis Gizikis, 2019. Automated tackling of disinformation. Panel for the Future of Science and Technology (2019).Google Scholar
- Tim Berners-Lee and Mark Fischetti. 2001. Weaving the Web: The original design and ultimate destiny of the WWW by its inventor. DIANE Publishing Company.Google Scholar
- Alessandro Bessi, Mauro Coletto, George Davidescu, 2015. Science vs conspiracy: Collective narratives in the age of misinformation. PloS one 10, 2 (2015), e0118093.Google ScholarCross Ref
- Olivier Boissier, Rafael Bordini, 2013. Multi-agent oriented programming with JaCaMo. Science of Computer Programming 78, 6 (2013), 747–761.Google ScholarDigital Library
- Amit Chopra and Munindar Singh. 2016. From social machines to social protocols: Software engineering foundations for sociotechnical systems. In Proc. of the 25th Int. Conf. on WWW. Int. WWW Conferences Steering Committee, 903–914.Google ScholarDigital Library
- Miriam Fernandez and Harith Alani. 2018. Online misinformation: Challenges and future directions. In Comp. Proc. of TWC2018. 595–602.Google ScholarDigital Library
- Simson L Garfinkel. 2008. Wikipedia and the Meaning of Truth. Technology Review 111, 6 (2008), 84–86.Google Scholar
- Lucas Graves. 2018. Understanding the promise and limits of automated fact-checking. Reuters Institute, University of Oxford 2 (2018).Google Scholar
- Jim Hendler and Tim Berners-Lee. 2010. From the Semantic Web to social machines: A research challenge for AI on the WWW. Artificial Intelligence 174, 2 (2010), 156–161.Google ScholarDigital Library
- Jomi Hübner, Jaime Sichman, and Olivier Boissier. 2007. Developing organised MAS using the MOISE+ model: programming issues at the system and agent levels. Int. J. of Agent-Oriented Software Engineering 1, 3-4(2007), 370–395.Google ScholarDigital Library
- Nicholas Jennings and Michael Wooldridge. 1998. Applications of Intelligent Agents. In Agent Technology: Foundations, Applications, and Markets, Nicholas Jennings and Michael Wooldridge (Eds.). Springer, Berlin, Heidelberg, 3–28.Google Scholar
- Miriam Metzger, Andrew Flanagin, Keren Eyal, 2003. Credibility for the 21st Century. Annals of the Int. Communication Assoc. 27, 1 (2003), 293–335.Google ScholarCross Ref
- Dave Murray-Rust, Ognjen Scekic, 2014. A collaboration model for community-based software development with social machines. 10th IEEE Int. Conf. on Collaborative Computing (2014), 84–93.Google ScholarCross Ref
- Petros Papapanagiotou, Alan Davoust, Dave Murray-Rust, 2018. Social Machines for All. In Proc. of the 17th Int. Conf. on Autonomous Agents and MAS (Stockholm, Sweden) (AAMAS ’18). Int. Foundation for Autonomous Agents and MAS, 1208–1212.Google Scholar
- Georg Rehm, Julian Moreno-Schneider, and Peter Bourgonje. 2018. Automatic and Manual Web Annotations in an Infrastructure to handle Fake News and other Online Media Phenomena. Proc. of the 11th Int. Conf. on Language Resources and Evaluation.Google Scholar
- Robert Sanderson. 2017. Web Annotation Protocol. W3C Recommendation. World Wide Web Consortium (W3C). https://www.w3.org/TR/annotation-protocol/Google Scholar
- Robert Sanderson, Paolo Ciccarese, and Benjamin Young. 2017. Web Annotation Vocabulary. W3C Recommendation. World Wide Web Consortium (W3C). https://www.w3.org/TR/annotation-vocab/Google Scholar
- Jennifer Skeem and Christopher Lowenkamp. 2016. Risk, Race, and Recidivism: Predictive Bias And Disparate Impact. Criminology 54, 4 (2016), 680–712.Google Scholar
- Paul Smart and Nigel Shadbolt. 2015. Social machines. In Encyclopedia of Information Science and Technology, Third Edition. IGI Global, 6855–6862.Google Scholar
- James Thorne and Andreas Vlachos. 2018. Automated fact checking: Task formulations, methods and future directions. arXiv preprint arXiv:1806.07687(2018).Google Scholar
- Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science 359, 6380 (2018), 1146–1151.Google Scholar
- Byron C Wallace. 2015. Computational irony: A survey and new perspectives. Artificial Intelligence Review 43, 4 (2015), 467–483.Google ScholarDigital Library
- Gerhard Weiss. 2000. MAS: a modern approach to distributed artificial intelligence. MIT press.Google Scholar
- Alexandra-Madalina Zarafin, Antoine Zimmermann, and Olivier Boissier. 2012. Integrating Semantic Web Technologies and MAS: A Semantic Description of Multi-Agent Organizations. In Proc. of the First Int. Conf. on Agreement Technologies(CEUR WS), Vol. 918. 296–297.Google Scholar
- Amy Zhang, Martin Robbins, Ed Bice, 2018. A Structured Response to Misinformation. In Comp. of The Web Conf. 2018. ACM Press.Google ScholarDigital Library
Index Terms
- Designing Social Machines for Tackling Online Disinformation
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