| Challenge: | Hate Speech (HS) against ethnic, religious and national minorities is a growing concern in online discourse. |
| Approach: | They present the German Twitter section of a large (2 billion word) bilingual Social Media corpus for Hate Speech research. |
| Outcome: | The proposed parser achieved F-scores of 97% for morphology and 92% for syntax on a cross-section of tweets. |
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| Challenge: | In corpus linguistics, semantic annotation is a valuable addition to ordinary, morphosyntactic tagging, lemmatization and dependency relations. |
| Approach: | They propose a parsing- and annotation-oriented framenet for German with almost 15,000 frames . they propose valency, syntactic function and semantic noun class as input conditions for frame disambiguation . |
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An Italian Twitter Corpus of Hate Speech against Immigrants (L18-1)
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| Challenge: | a recent study has annotated 6,000 tweets for hate speech against immigrants . the annotation scheme was designed to account for the multiplicity of factors that can contribute to the definition of a hate speech notion . |
| Approach: | They describe a Twitter corpus annotated for hate speech against immigrants . they propose a scheme that includes aggressiveness, offensiveness, irony, stereotype and intensity . |
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A Dataset of Offensive German Language Tweets Annotated for Speech Acts (2022.lrec-1)
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| Challenge: | Using speech act analysis, we analysed 600 offensive and non-offensive tweets in germany . a large body of research exists on the pragmatic characteristics of offensive language . |
| Approach: | They analyze German offensive and non-offensive tweets and use a subset of the 2019 GermEval Shared Task on the Identification of Offensive Language dataset. |
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A Corpus of Turkish Offensive Language on Social Media (2020.lrec-1)
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| Challenge: | Identifying abusive, offensive, aggressive or in general inappropriate language has recently attracted interest of researchers from academic as well as commercial institutions. |
| Approach: | They propose to classify Turkish offensive language corpus using state-of-the-art annotation methods . they find 19 % of tweets contain some type of offensive language . |
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HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection (2022.lrec-1)
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| Challenge: | In Brazil, hate speech is prohibited, however the regulation is not effective due to the difficulty of identifying, quantifying and classifying this kind of online content. |
| Approach: | They propose to annotate a large corpus of Brazilian Instagram comments manually and to use it to detect hate speech and offensive language. |
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Building a Sentiment Corpus of Tweets in Brazilian Portuguese (L18-1)
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| Challenge: | Sentiment analysis is a popular area of Natural Language Processing due to its subjective and semantic characteristics. |
| Approach: | They propose to annotate Brazilian Portuguese sentences manually using a sentiment corpus . they run experiments on polarity classification using six machine learning classifiers . |
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FooTweets: A Bilingual Parallel Corpus of World Cup Tweets (L18-1)
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| Challenge: | a new study analyzes the nature of twitter data and compares it with other social networking websites. |
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An Annotated Corpus for Sexism Detection in French Tweets (2020.lrec-1)
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Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi, Marlène Coulomb-Gully
| Challenge: | Social media networks allow users to share opinions and sentiments, which can cause a large spreading of hatred or abusive messages. |
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Building a Corpus from Handwritten Picture Postcards: Transcription, Annotation and Part-of-Speech Tagging (L18-1)
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| Challenge: | In this paper, we describe the processes and challenges of digitalisation, manual transcription, and manual annotation of over 11,000 postcards. |
| Approach: | They describe the processes and challenges of digitalisation, manual transcription, and manual annotation of over 11,000 postcards written in German and Swiss German. |
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The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse (2022.lrec-1)
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Ritesh Kumar, Shyam Ratan, Siddharth Singh, Enakshi Nandi, Laishram Niranjana Devi, Akash Bhagat, Yogesh Dawer, Bornini Lahiri, Akanksha Bansal, Atul Kr. Ojha
| Challenge: | 59,152 comments are annotated with a hierarchical, fine-grained taget marking aggression and bias of various kinds on social media platforms. |
| Approach: | They propose to annotate a multilingual dataset with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. |
| Outcome: | The proposed dataset contains 59,152 comments in four languages, mostly code-mixed with English. |