Disinformation Detection: Knowledge Infusion with Transfer Learning and Visualizations

Research output: Chapter in Book or Conference ProceedingsBook chapterpeer-review

Abstract

The automatic detection of disinformation has gained an increased focus by the research community during the last years. The spread of false information can be an issue for political processes, opinion mining and journalism in general. In this dissertation, I propose a novel approach to gain new insights on the automatic detection of disinformation in textual content. Additionally, I will combine multiple research domains, such as fake news, hate speech, propaganda, and extremism. For this purpose, I will create two novel and annotated datasets in German - a large multi-label dataset for disinformation detection in news articles and a second dataset for hate speech detection in social media posts, which both can be used for training the models in the listed domains via transfer learning. With the usage of transfer learning, an extensive data analysis and classification of the presented domains will be conducted. The classification models will be enhanced during and after training using a knowledge graph, containing additional information (i.e. named entities, relationships, topics), to find explicit insights about the common traits or lines of disinformative arguments in an article. Lastly, methods of explainable artificial intelligence will be combined with visualization techniques to understand the models predictions and present the results in a user-friendly and interactive way.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publicationLecture Notes in Computer Science
PublisherSpringer
Pages468-475
Number of pages8
Volume13982
DOIs
Publication statusPublished - 2023
Event45th European Conference on Information Retrieval (ECIR) - Dublin, Ireland
Duration: 2 Apr 20236 Apr 2023

Conference

Conference45th European Conference on Information Retrieval (ECIR)
Country/TerritoryIreland
CityDublin
Period2/04/236/04/23

Research Field

  • Former Research Field - Data Science

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