PropertyValue
?:about
?:abstract
  • Not only in times of crisis and unrest is the internet and particularly social media being used as a critical source of information for the largest parts of our societies. However, in order for the “social web” to function as such an immediate and trustworthy information ecosystem, large scale and largely automated (computational) methods are required to mitigate the impacts of wrong or misleading information that is purposefully being created and shared online. We talked to Arkaitz Zubiaga about some of the established practices in dealing with misinformation as well as novel challenges arising with the proliferation of large language models. Arkaitz is an Associate Professor and Co-leader of the Social Data Science Lab at the Queen Mary University of London. His work on the development and improvement of natural language processing (NLP) methods for the analysis of online and social media data helps to understand and tackle some of the problematic aspects of online communications, including misinformation. In the interview, Arkaitz talks us through the different steps of his pipeline for detecting misinformation, the roles of experts and journalists in it, and disentangles some terminological confusion between “rumors” and “fake news”. Indira Sen and Leon Fröhling conducted the interview with Arkaitz on June 5, 2023, during the International Conference on Web and Social Media (ICWSM-23) in Limassol, Cyprus. The transcript was edited for clarity and length. (en)
  • Not only in times of crisis and unrest is the internet and particularly social media being used as a critical source of information for the largest parts of our societies. However, in order for the “social web” to function as such an immediate and trustworthy information ecosystem, large scale and largely automated (computational) methods are required to mitigate the impacts of wrong or misleading information that is purposefully being created and shared online. We talked to Arkaitz Zubiaga about some of the established practices in dealing with misinformation as well as novel challenges arising with the proliferation of large language models. Arkaitz is an Associate Professor and Co-leader of the Social Data Science Lab at the Queen Mary University of London. His work on the development and improvement of natural language processing (NLP) methods for the analysis of online and social media data helps to understand and tackle some of the problematic aspects of online communications, including misinformation. In the interview, Arkaitz talks us through the different steps of his pipeline for detecting misinformation, the roles of experts and journalists in it, and disentangles some terminological confusion between “rumors” and “fake news”. Indira Sen and Leon Fröhling conducted the interview with Arkaitz on June 5, 2023, during the International Conference on Web and Social Media (ICWSM-23) in Limassol, Cyprus. The transcript was edited for clarity and length. (de)
?:author
?:citationString
  • Zubiaga, A. (2023). Expert Insights into Studying Misinformation with Natural Language Processing Methods. An Interview with Arkaitz Zubiaga (GESIS Guides to Digital Behavioral Data, 7). Cologne: GESIS – Leibniz Institute for the Social Sciences. (en)
  • Zubiaga, A. (2023). Expert Insights into Studying Misinformation with Natural Language Processing Methods. An Interview with Arkaitz Zubiaga (GESIS Guides to Digital Behavioral Data, 7). Cologne: GESIS – Leibniz Institute for the Social Sciences. (de)
?:dateModified
  • 2023 (xsd:gyear)
?:datePublished
  • 2023 (xsd:gyear)
?:hasFulltext
  • true (xsd:boolean)
?:linksGuide
?:name
  • Expert Insights into Studying Misinformation with Natural Language Processing Methods - An Interview with Arkaitz Zubiaga (en)
  • Expert Insights into Studying Misinformation with Natural Language Processing Methods - An Interview with Arkaitz Zubiaga (de)
?:portalUrl
?:publicationType
  • gesis_guides (en)
?:publisher
?:sourceInfo
  • GESIS-Guides (xsd:string)
rdf:type
?:version
  • 1.0 (de)
  • 1.0 (en)