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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 – 10 of 10 1 EMBEDDIA tools output example corpus of Estonian, Croatian and Latvian news articles 1.0 Freienthal, Linda; Pelicon, Andraž; Martinc, Matej. - : Ekspress Meedia Group, 2022. : Styria Media Group, 2022 BASE Show details 2 Retweet communities reveal the main sources of hate speech Evkoski, Bojan; Pelicon, Andraž; Mozetič, Igor... In: PLoS One (2022) BASE Show details 3 Slovenian Twitter dataset 2018-2020 1.0 Evkoski, Bojan; Pelicon, Andraž; Mozetič, Igor. - : Jožef Stefan Institute, 2021 BASE Show details 4 Italian YouTube Hate Speech Corpus Cinelli, Matteo; Pelicon, Andraž; Mozetič, Igor; Quattrociocchi, Walter; Kralj Novak, Petra; Zollo, Fabiana. - : Jožef Stefan Institute, 2021 Abstract: We present an Italian YouTube dataset manually annotated for hate speech types and targets. The comments to be annotated were sampled from the Italian YouTube comments on videos about the Covid-19 pandemic in the period from January 2020 to May 2020. Two sets were annotated: a training set with 59,870 comments (IMSyPP_IT_YouTube_comments_train.csv) and an evaluation set with 10,536 comments (IMSyPP_IT_YouTube_comments_evaluation.csv). The dataset was annotated by 8 annotators with each comment being annotated by two annotators. It was used to train a classification model for hate speech types detection that is publicly available at the following URL: https://huggingface.co/IMSyPP/hate_speech_it. The dataset consists of the following fields: ID_Commento - YouTube ID of the comment ID_Video - YouTube ID of the video under which the comment was posted Testo - text of the comment Tipo - type of hate speech Target - the target of hate speech Additionally, we have included the Italian YouTube data (SR_YT_comments.csv) which was collected in the same period as the training data and was annotated using the aforementioned model. The automatically labeled data was used to analyze the relationship between hate speech and misinformation on Italian YouTube. The results of this analysis are presented in the associated paper. The analyzed data are represented with the following fields: ID_Commento - YouTube ID of the comment Label - automatically assigned label by the model is_questionable - the type of channel where the comment was collected from; the channels could either be categorized as spreading reliable or questionable information. Keyword: hate speech; misinformation; YouTube URL: http://hdl.handle.net/11356/1450 BASE Hide details 5 Latvian user comment dataset 1.0 Shekhar, Ravi; Purver, Matthew; Pollak, Senja. - : Ekspress Meedia Group, 2021 BASE Show details 6 Ekspress user comment dataset 1.0 Shekhar, Ravi; Pollak, Senja; Pelicon, Andraž. - : Ekspress Meedia Group, 2021 BASE Show details 7 24sata news comment dataset 1.0 Shekhar, Ravi; Pranjic, Marko; Pollak, Senja. - : Styria Media Group, 2021 BASE Show details 8 SimLex-999 Slovenian translation SimLex-999-sl 1.0 Pollak, Senja; Vulić, Ivan; Pelicon, Andraž. - : University of Ljubljana, 2021 BASE Show details 9 Investigating cross-lingual training for offensive language detection Pelicon, Andraž; Shekhar, Ravi; Škrlj, Blaž... In: PeerJ Comput Sci (2021) BASE Show details 10 Sentiment Annotated Dataset of Croatian News Pelicon, Andraž; Pranjić, Marko; Miljković, Dragana. - : Jožef Stefan Institute, 2020 BASE Show details

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