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Author: Ewerth, Ralph × Type: bookPart × Subject: Twitter ×

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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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    Check square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features
    (Aachen, Germany : RWTH Aachen, 2020) Cheema, Gullasl S.; Hakimov, Sherzod; Ewerth, Ralph; Cappellato, Linda; Eickhoff, Carsten; Ferro, Nicola; Névéol, Aurélie
    In this digital age of news consumption, a news reader has the ability to react, express and share opinions with others in a highly interactive and fast manner. As a consequence, fake news has made its way into our daily life because of very limited capacity to verify news on the Internet by large companies as well as individuals. In this paper, we focus on solving two problems which are part of the fact-checking ecosystem that can help to automate fact-checking of claims in an ever increasing stream of content on social media. For the first prob-lem, claim check-worthiness prediction, we explore the fusion of syntac-tic features and deep transformer Bidirectional Encoder Representations from Transformers (BERT) embeddings, to classify check-worthiness of a tweet, i.e. whether it includes a claim or not. We conduct a detailed feature analysis and present our best performing models for English and Arabic tweets. For the second problem, claim retrieval, we explore the pre-trained embeddings from a Siamese network transformer model (sentence-transformers) specifically trained for semantic textual similar-ity, and perform KD-search to retrieve verified claims with respect to a query tweet.