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Author: Cheema, Gullal S. × Author: Ewerth, Ralph × Has files: Yes × Type: Text × Subject: Konferenzschrift ×

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    On the Role of Images for Analyzing Claims in Social Media
    (Aachen, Germany : RWTH Aachen, 2021) Cheema, Gullal S.; Hakimov, Sherzod; Müller-Budack, Eric; Ewerth, Ralph
    Fake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.
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    TIB's visual analytics group at MediaEval '20: Detecting fake news on corona virus and 5G conspiracy
    (Aachen, Germany : RWTH Aachen, 2020) Cheema, Gullal S.; Hakimov, Sherzod; Ewerth, Ralph; Hicks, Steven
    Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifi-cally, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The Fak-eNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection.