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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Matching Tweets With Applicable Fact-Checks Across Languages ... Kazemi, Ashkan; Li, Zehua; Pérez-Rosas, Verónica. - : arXiv, 2022 BASE Show details 2 Exploring Self-Identified Counseling Expertise in Online Support Forums ... Lahnala, Allison; Zhao, Yuntian; Welch, Charles. - : arXiv, 2021 BASE Show details 3 Leveraging Social Media as a Thermometer to Gauge Patient and Caregiver Concerns: COVID-19 and Prostate Cancer Loeb, Stacy; Mihalcea, Rada; Perez-Rosas, Veronica... In: Eur Urol Open Sci (2021) BASE Show details 4 Exploring the Value of Personalized Word Embeddings ... Welch, Charles; Kummerfeld, Jonathan K.; Pérez-Rosas, Verónica. - : arXiv, 2020 BASE Show details 5 Towards Automatic Detection of Misinformation in Online Medical Videos ... Hou, Rui; Pérez-Rosas, Verónica; Loeb, Stacy; Mihalcea, Rada. - : arXiv, 2019 Abstract: Recent years have witnessed a significant increase in the online sharing of medical information, with videos representing a large fraction of such online sources. Previous studies have however shown that more than half of the health-related videos on platforms such as YouTube contain misleading information and biases. Hence, it is crucial to build computational tools that can help evaluate the quality of these videos so that users can obtain accurate information to help inform their decisions. In this study, we focus on the automatic detection of misinformation in YouTube videos. We select prostate cancer videos as our entry point to tackle this problem. The contribution of this paper is twofold. First, we introduce a new dataset consisting of 250 videos related to prostate cancer manually annotated for misinformation. Second, we explore the use of linguistic, acoustic, and user engagement features for the development of classification models to identify misinformation. Using a series of ablation ... Keyword: FOS Computer and information sciences; Machine Learning cs.LG; Machine Learning stat.ML URL: https://arxiv.org/abs/1909.01543 https://dx.doi.org/10.48550/arxiv.1909.01543 BASE Hide details 6 Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) ... Castro, Santiago; Hazarika, Devamanyu; Pérez-Rosas, Verónica. - : arXiv, 2019 BASE Show details 7 Automatic Detection of Fake News ... Pérez-Rosas, Verónica; Kleinberg, Bennett; Lefevre, Alexandra. - : arXiv, 2017 BASE Show details 8 Exploration of Visual, Acoustic, and Physiological Modalities to Complement Linguistic Representations for Sentiment Analysis Pérez-Rosas, Verónica. - : University of North Texas, 2014 BASE Show details

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