Hinkelmann, Knut

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Hinkelmann
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Knut
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Hinkelmann, Knut

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2020 - 20223
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Einstellungen

Autor:in: Ahmed, Sajjad × Enddatum: 2022 × Anfangsdatum: 2020 × Dateien dazu vorhanden?: Nein × Hochschule: Hochschule für Wirtschaft ×

Suchergebnisse

Gerade angezeigt 1 - 3 von 3
  • Vorschaubild nicht verfügbar
    Publikation
    Fact checking: an automatic end to end fact checking system
    (Springer, 2022) Hinkelmann, Knut [in: Combating fake news with computational intelligence techniques]
    04A - Beitrag Sammelband
  • Vorschaubild nicht verfügbar
    Publikation
    Fact checking: detection of check worthy statements through support vector machine and feed forward neural network
    (Springer, 2021) Hinkelmann, Knut [in: Advances in information and communication. Proceedings of the 2021 Future of Information and Communication Conference (FICC)]
    Detection of check-worthy statements is a subtask in the fact-checking process, automation of which would decrease the time and burden required to fact-check a statement. This paper proposes an approach focused on the classification of statements into check-worthy and not check-worthy. For the current paper, a dataset is constructed by consulting different fact-checking organizations. It contains debates and speeches in the domain of politics. Thus, even the ability of check worthy approach is evaluated on this domain. It starts with extracting sentence-level and context features from the sentences, and classifying them based on these features. The feature set and context were chosen after several experiments, based on how well they differentiate check-worthy statements. The findings indicated that the context in the approach gives considerable contribution in the classification, while also using more general features to capture information from the sentences. The results were analyzed by examining all features used, assessing their contribution in classification, and how well the approach performs in speeches and debates separately to detect the check worthy statements to reduce the time and burden of fact checking process.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Development of fake news model using machine learning through natural language processing
    (World Academy of Science, Engineering and Technology, 2020) Hinkelmann, Knut [in: International Journal of Computer and Information Engineering]
    Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.
    01B - Beitrag in Magazin oder Zeitung