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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 – 13 of 13 1 Retweet communities reveal the main sources of hate speech Evkoski, Bojan; Pelicon, Andraž; Mozetič, Igor... In: PLoS One (2022) BASE Show details 2 Slovenian Twitter dataset 2018-2020 1.0 Evkoski, Bojan; Pelicon, Andraž; Mozetič, Igor. - : Jožef Stefan Institute, 2021 BASE Show details 3 Italian YouTube Hate Speech Corpus Cinelli, Matteo; Pelicon, Andraž; Mozetič, Igor. - : Jožef Stefan Institute, 2021 BASE Show details 4 Slovenian Twitter hate speech dataset IMSyPP-sl Kralj Novak, Petra; Mozetič, Igor; Ljubešić, Nikola. - : Jožef Stefan Institute, 2021 BASE Show details 5 English YouTube Hate Speech Corpus Ljubešić, Nikola; Mozetič, Igor; Cinelli, Matteo. - : Jožef Stefan Institute, 2021 BASE Show details 6 Tweets about impact investing Kralj Novak, Petra; de Amicis, Luisa; Mozetič, Igor. - : Jožef Stefan Institute, 2018 BASE Show details 7 Brexit stance annotated tweets Grčar, Miha; Cherepnalkoski, Darko; Mozetič, Igor; Kralj Novak, Petra. - : Jožef Stefan Institute, 2017 Abstract: The corpus contains over 4.5 million tweets (tweet IDs) automatically labeled by a machine learning program with stance regarding Brexit: Positive (supporting Brexit), Negative (opposing Brexit), or Neutral (uncommitted). The Brexit referendum was held on June 23, 2016, to decide whether the UK should leave or remain in the EU. In the weeks before the referendum, starting on May 12, the UK geo-located Brexit-related tweets were continuously collected resulting in a dataset of around 4.5 million (4,508,440) tweets from almost one million (998,054) users. A large sample of the collected tweets (35,000) was manually labeled for the stance of their authors regarding Brexit: Positive (supporting Brexit), Negative (opposing Brexit), or Neutral (uncommitted). The labeled tweets were used to train a classifier which then automatically labeled all the remaining tweets. The corpus contains tweet ids and stance labels. The tweets are grouped into files one hour per file. In each file, one row represents one entry (twitter_id, sentiment_label). Lines are ordered by the tweet time. The data collection, annotation, model training and performance estimation is described in detail in: Miha Grčar, Darko Cherepnalkoski, Igor Mozetič, Petra Kralj Novak: Stance and influence of Twitter users regarding the Brexit referendum. Computational Social Networks 4/6. 2017. http://dx.doi.org/10.1186/s40649-017-0042-6 Keyword: Brexit; computer-mediated communication; stance; Twitter URL: http://hdl.handle.net/11356/1135 BASE Hide details 8 Dataset of European Parliament roll-call votes and Twitter activities MEP 1.0 Cherepnalkoski, Darko; Karpf, Andreas; Mozetič, Igor. - : Jožef Stefan Institute, 2016 BASE Show details 9 Twitter sentiment for 15 European languages Mozetič, Igor; Grčar, Miha; Smailović, Jasmina. - : Jožef Stefan Institute, 2016 BASE Show details 10 Emoji Sentiment Ranking 1.0 Kralj Novak, Petra; Smailović, Jasmina; Sluban, Borut. - : Jožef Stefan Institute, 2015 BASE Show details 11 Sentiment of Emojis ... Novak, Petra Kralj; Smailović, Jasmina; Sluban, Borut. - : arXiv, 2015 BASE Show details 12 Sentiment of Emojis Kralj Novak, Petra; Smailović, Jasmina; Sluban, Borut. - : Public Library of Science, 2015 BASE Show details 13 Extraction of Temporal Networks from Term Co-Occurrences in Online Textual Sources Popović, Marko; Štefančić, Hrvoje; Sluban, Borut. - : Public Library of Science, 2014 BASE Show details

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