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Islands and Bridges of Language: Bio-Inspired Structural Analysis of Language Embedding Data
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Between Deterministic and Nondeterministic Quantitative Automata (Invited Talk)
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Boker, Udi. - : LIPIcs - Leibniz International Proceedings in Informatics. 30th EACSL Annual Conference on Computer Science Logic (CSL 2022), 2022
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Latin Lemmatization & POS Tagging. Issues, Resources, Tools ...
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Latin Lemmatization & POS Tagging. Issues, Resources, Tools ...
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Mehrsprachigkeit und Translanguaging in Migrationsstudien: einige methodologische Überlegungen ; Multilingualism and Translanguaging in Migration Studies: Some Methodological Reflections
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In: Forum Qualitative Sozialforschung / Forum: Qualitative Social Research; Bd. 23 Nr. 1 (2022) ; Forum Qualitative Sozialforschung / Forum: Qualitative Social Research; Vol. 23 No. 1 (2022) ; Forum Qualitative Sozialforschung / Forum: Qualitative Social Research; Vol. 23 Núm. 1 (2022) ; 1438-5627 (2022)
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A Corpus-Based Sentence Classifier for Entity–Relationship Modelling
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In: Electronics; Volume 11; Issue 6; Pages: 889 (2022)
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Text Data Augmentation for the Korean Language
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3425 (2022)
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Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification
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In: Sensors; Volume 22; Issue 5; Pages: 1899 (2022)
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FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 3130 (2022)
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eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
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A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
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In: Information; Volume 13; Issue 3; Pages: 120 (2022)
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Multilingualism and Translanguaging in Migration Studies: Some Methodological Reflections
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In: Forum Qualitative Sozialforschung / Forum: Qualitative Social Research ; 23 ; 1 (2022)
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Detecting weak and strong Islamophobic hate speech on social media
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Abstract:
Islamophobic hate speech on social media is a growing concern in contemporary Western politics and society. It can inflict considerable harm on any victims who are targeted, create a sense of fear and exclusion amongst their communities, toxify public discourse and motivate other forms of extremist and hateful behavior. Accordingly, there is a pressing need for automated tools to detect and classify Islamophobic hate speech robustly and at scale, thereby enabling quantitative analyses of large textual datasets, such as those collected from social media. Previous research has mostly approached the automated detection of hate speech as a binary task. However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically informed social science and effective monitoring of social media platforms. Drawing on in-depth conceptual work we build an automated software tool which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%. Our tool enables future quantitative research into the drivers, spread, prevalence and effects of Islamophobic hate speech on social media. ; Engineering and Physical Sciences Research Council
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Keyword:
Big data; Communication; Hate speech; Islamophobia; Machine learning; Natural language processing; Prejudice; Scale; Science; Social media; Support; Twitter
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URL: https://doi.org/10.1080/19331681.2019.1702607 http://hdl.handle.net/10197/12720
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Maastricht University’s Multilingual Speech Translation System for IWSLT 2021
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Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
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