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34 results for “hate”
Data from: Why hate the good guy? Antisocial punishment of high cooperators is greater when people compete to be chosen
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Supplementary material to 'Automatic Identification of Hate Speech – A Case-Study of Alt-Right YouTube Videos'
<p>The associated files have been created for and is analysed in a fortcoming article entitled <em>Automatic Identification of Hate Speech – A Case-Study of Alt-Right YouTube Videos'. </em>The material is divided into six tables as follows:</p> <table> <tbody> <tr> <td>Sentence top 5%</td> <td>The 19th 20-quantile predicted most hateful sentences</td> </tr> <tr> <td>Sentence bottom 5%</td> <td>The bottom 20-quantile predicted moste hatefull sentences (the least likely to contain hatespeech)</td> </tr> <tr> <td>Paragraphs</td> <td>Prediction and annotation of paragraphs</td> </tr> <tr> <td>Video top 10%</td> <td>Titles of the top decile predicted hateful videos</td> </tr> <tr> <td>Video bottom 10%</td> <td>Titles of the bottom decile predicted hateful videos</td> </tr> <tr> <td>Video bottom 10% - Alt right</td> <td>Titles of the bottom decile predicted hateful videos without History</td> </tr> </tbody> </table> <p>The data is uploaded in two formats:</p> <p><strong>Excel file: </strong>Automatic_Detection_of_Hate_Speech_a_Case-Study_of_Alt-Right_Videos.xlsx contains all six tables in one file, with a supplementary <em>codebook. </em></p> <p><strong>Tab Separated Values (TSV):</strong> Each file correspond to a single sheet from the excel file, and are named accordingly. UTF-8 Encoded.<strong><br></strong></p>
Profiling Hate Speech Spreaders on Twitter
<p><strong>Task</strong></p> <p>Hate speech (HS) is commonly defined as any communication that disparages a person or a group on the basis of some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics. Given the huge amount of user-generated contents on Twitter, the problem of detecting, and therefore possibly contrasting the HS diffusion, is becoming fundamental, for instance for fighting against misogyny and xenophobia. To this end, in this task, we aim at identifying possible hate speech spreaders on Twitter as a first step towards preventing hate speech from being propagated among online users.</p> <p>After having addressed several aspects of author profiling in social media from 2013 to 2020 (fake news spreaders, bot detection, age and gender, also together with personality, gender and language variety, and gender from a multimodality perspective), this year we aim at investigating if it is possible to discriminate authors that have shared some hate speech in the past from those that, to the best of our knowledge, have never done it.</p> <p>As in previous years, we propose the task from a <strong>multilingual</strong> perspective:</p> <ul> <li>English</li> <li>Spanish</li> </ul> <p><strong>NOTE:</strong> Although we recommend participating in both languages (English and Spanish), it is possible to address the problem just for one language.</p> <p>Award</p> <p>We are happy to announce that the best performing team at the 9th International Competition on Author Profiling will be awarded 300,- Euro sponsored by <a href="https://www.symanto.net/"><strong>Symanto</strong></a></p> <p><strong>Data</strong></p> <p><strong>Input</strong></p> <p>The uncompressed dataset consists of a folder per language (en, es). Each folder contains:</p> <ul> <li>An XML file per author (Twitter user) with 100 tweets. The name of the XML file corresponding to the unique author id.</li> <li>A truth.txt file with the list of authors and the ground truth.</li> </ul> <p>The format of the XML files is:</p> <pre> <author lang="en"> <documents> <document>Tweet 1 textual contents</document> <document>Tweet 2 textual contents</document> ... </documents> </author> </pre> <p>The format of the truth.txt file is as follows. The first column corresponds to the author id. The second column contains the truth label.</p> <pre> b2d5748083d6fdffec6c2d68d4d4442d:::0 2bed15d46872169dc7deaf8d2b43a56:::0 8234ac5cca1aed3f9029277b2cb851b:::1 5ccd228e21485568016b4ee82deb0d28:::0 60d068f9cafb656431e62a6542de2dc0:::1 ... </pre> <p><strong>Output</strong></p> <p>Your software must take as input the absolute path to an unpacked dataset, and has to output for each document of the dataset a corresponding XML file that looks like this:</p> <pre> <author id="author-id" lang="en|es" type="0|1" /> </pre> <p>The naming of the output files is up to you. However, we recommend using the author-id as filename and "XML" as an extension.</p> <p><strong>IMPORTANT!</strong> Languages should not be mixed. A folder should be created for each language and place inside only the files with the prediction for this language.</p> <p><strong>Evaluation</strong></p> <p>The performance of your system will be ranked by accuracy. For each language, we will calculate individual accuracies in discriminating between the two classes. Finally, we will average the accuracy values per language to obtain the final ranking.</p> <p><strong>Related Work</strong></p> <ul> <li>[1] Valerio Basile, Cristina Bosco, Elisabetta Fersini, Dora Nozza, Viviana Patti, Francisco Rangel, Paolo Rosso, Manuela Sanguinetti (2019). <a href="http://personales.upv.es/prosso/resources/BasileEtAl_SemEval19.pdf">SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter. </a>Proc. SemEval 2019</li> <li>[2] Fabio Poletto, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, Viviana Patti (2020). <a href="https://link.springer.com/article/10.1007/s10579-020-09502-8">Resources and benchmark corpora for hate speech detection: a systematic review. </a>Language Resources & Evaluation. https://doi.org/10.1007/s10579-020-09502-8</li> <li>[3] Paula Fortuna, Sérgio Nunes (2018). <a href="https://dl.acm.org/doi/10.1145/3232676">A survey on automatic detection of hate speech in text. </a>ACM Computing Surveys (CSUR) 51.4</li> <li>[4] Maria Anzovino, Elisabetta Fersini, Paolo Rosso (2018). <a href="https://link.springer.com/chapter/10.1007/978-3-319-91947-8_6">Automatic Identification and Classification of Misogynistic Language on Twitter. </a>In: Proc. 23rd Int. Conf. on Applications of Natural Language to Information Systems, NLDB-2018, Springer-Verlag, LNCS(10859), pp. 57-64</li> <li>[5] Elisabetta Fersini, Paolo Rosso, Maria Anzovino (2018). <a href="http://personales.upv.es/prosso/resources/FersiniEtAl_IberEval18.pdf">Overview of the task on automatic misogyny identification at IberEval 2018. </a>Proc. IberEval 2018</li> <li>[6] Elisabetta Fersini, Dora Nozza, Paolo Rosso (2018). <a href="http://personales.upv.es/prosso/resources/FersiniEtAl_Evalita18.pdf">Overview of the Evalita 2018 task on automatic misogyny identification (AMI). Proc. </a>EVALITA 2018</li> <li>[7] Cristina Bosco, Felice Dell'Orletta, Fabio Poletto, Manuela Sanguinetti, Maurizio Tesconi (2018). <a href="https://pdfs.semanticscholar.org/3eae/e4b2b8d9c7de52ba2386c73bb30097ec111c.pdf">Overview of the EVALITA 2018 hate speech detection task. </a>Proc. EVALITA 2018</li> <li>[8] Samuel Caetano da Silva, Thiago Castro Ferreira, Ricelli Moreira Silva Ramos, Ivandre Paraboni (2020). <a href="https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3478">Data-driven and psycholinguistics motivated approaches to hate speech detection. </a>Computación y Sistemas, 24(3): 1179–1188</li> <li>[9] Stiven Zimmerman, Udo Kruschwitz, Cris Fox (2018). <a href="https://www.aclweb.org/anthology/L18-1404.pdf">Improving hate speech detection with deep learning ensembles. </a>In Proc. of the Eleventh Int. Conf. on Language Resources and Evaluation (LREC 2018)</li> <li>[10] Francisco Rangel, Anastasia Giachanou, Bilal Ghanem, Paolo Rosso. <a href="http://ceur-ws.org/Vol-2696/paper_267.pdf">Overview of the 8th Author Profiling Task at PAN 2020: Profiling Fake News Spreaders on Twitter. </a>In: L. Cappellato, C. Eickhoff, N. Ferro, and A. Névéol (eds.) CLEF 2020 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings.CEUR-WS.org, vol. 2696</li> <li>[11] Francisco Rangel and Paolo Rosso. <a href="http://ceur-ws.org/Vol-2380/paper_263.pdf">Overview of the 7th Author Profiling Task at PAN 2019: Bots and Gender Profiling in Twitter. </a>In: L. Cappellato, N. Ferro, D. E. Losada and H. Müller (eds.) CLEF 2019 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings.CEUR-WS.org, vol. 2380</li> <li>[12] Francisco Rangel, Paolo Rosso, Martin Potthast, Benno Stein. <a href="http://ceur-ws.org/Vol-2125/invited_paper_15.pdf">Overview of the 6th author profiling task at pan 2018: multimodal gender identification in Twitter.</a> In: CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 2125.</li> <li>[13] Francisco Rangel, Paolo Rosso, Martin Potthast, Benno Stein. <a href="http://ceur-ws.org/Vol-1866/invited_paper_11.pdf">Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter.</a> In: Cappellato L., Ferro N., Goeuriot L, Mandl T. (Eds.) CLEF 2017 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1866.</li> <li>[14] Francisco Rangel, Paolo Rosso, Ben Verhoeven, Walter Daelemans, Martin Pottast, Benno Stein. <a href="http://ceur-ws.org/Vol-1609/16090750.pdf">Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre Evaluations.</a> In: Balog K., Capellato L., Ferro N., Macdonald C. (Eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1609, pp. 750-784</li> <li>[15] Francisco Rangel, Fabio Celli, Paolo Rosso, Martin Pottast, Benno Stein, Walter Daelemans. <a href="http://personales.upv.es/prosso/resources/RangelEtAl_PAN15.pdf%22">Overview of the 3rd Author Profiling Task at PAN 2015.</a>In: Linda Cappelato and Nicola Ferro and Gareth Jones and Eric San Juan (Eds.): CLEF 2015 Labs and Workshops, Notebook Papers, 8-11 September, Toulouse, France. CEUR Workshop Proceedings. ISSN 1613-0073, http://ceur-ws.org/Vol-1391/,2015.</li> <li>[16] Francisco Rangel, Paolo Rosso, Irina Chugur, Martin Potthast, Martin Trenkmann, Benno Stein, Ben Verhoeven, Walter Daelemans. <a href="http://ceur-ws.org/Vol-1180/CLEF2014wn-Pan-RangelEt2014.pdf">Overview of the 2nd Author Profiling Task at PAN 2014.</a> In: Cappellato L., Ferro N., Halvey M., Kraaij W. (Eds.) CLEF 2014 Labs and Workshops, Notebook Papers. CEUR-WS.org, vol. 1180, pp. 898-827.</li> <li>[17] Francisco Rangel, Paolo Rosso, Moshe Koppel, Efstatios Stamatatos, Giacomo Inches. <a href="http://ceur-ws.org/Vol-1179/CLEF2013wn-PAN-RangelEt2013.pdf">Overview of the Author Profiling Task at PAN 2013.</a> In: Forner P., Navigli R., Tufis D. (Eds.)Notebook Papers of CLEF 2013 LABs and Workshops. CEUR-WS.org, vol. 1179</li> <li>[18] Francisco Rangel and Paolo Rosso <a href="https://ojs.letras.up.pt/ojs/index.php/LLLD/article/download/6119/5761">On the Implications of the General Data Protection Regulation on the Organisation of Evaluation Tasks. </a>In: Language and Law / Linguagem e Direito, Vol. 5(2), pp. 80-102</li> <li>[19] Francisco Rangel, Marc Franco-Salvador, Paolo Rosso <a href="https://arxiv.org/abs/1705.10754">A Low Dimensionality Representation for Language Variety Identification. </a>In: Postproc. 17th Int. Conf. on Comput. Linguistics and Intelligent Text Processing, CICLing-2016, Springer-Verlag, Revised Selected Papers, Part II, LNCS(9624), pp. 156-169 (arXiv:1705.10754)</li> </ul>
Datasets for "Auditing Elon Musk's Impact on Hate Speech and Bots"
<p>Datasets for the publication "Auditing Elon Musk's Impact on Hate Speech and Bots" [1].</p> <p><strong>File information:</strong></p> <ul> <li>baseline_tweet_ids_2022.csv, hate_tweet_ids_2022.csv: List of IDs and their corresponding dates from the "baseline" and "hate" samples of tweets used in the publication, respectively. The former is used to create the number of baseline tweets each day (‘baseline_freq.csv’) while the latter is used to create the number of hate tweets each day (‘hate_freq.csv'). We share the date a tweet was made as well as its tweet ID from which you can find the original tweet’s URL <a href="https://developer.twitter.com/en/blog/community/2020/getting-to-the-canonical-url-for-a-post">with the help of this web page</a>. As you explore these data, you may notice in a minority of cases hate tweets that are not hateful or, alternatively, baseline tweets that are hateful. This is a product of our filtering method used to collect and analyze tweets at scale. We always look forward to hearing your suggestions to improve the tweet filtering process.</li> <li>baseline_freq.csv, hate_freq.csv: Number of collected tweets per day for the baseline and hate samples, respectively. The file 'freq_data.py' is used to calculate these frequencies from the raw data. Feel free to consult this if you have questions about how the frequencies are calculated (or if you want to change how the data are aggregated). <strong>Use these to recreate Figure 2 from Hickey et al [1].</strong></li> <li>user_hate_levels_per_day.csv: CSV file with dates (YYYY-MM-DD format) and the mean proportion of slurs used by hateful users each day from October 1st to November 30th, 2022. <strong>Use these data to recreate Figure 1 from Hickey et al [1]. </strong>See the Methods section of Hickey et al [1]. for details.</li> <li>hate_keywords.txt: Words used to query the Twitter Academic API for hate tweets.</li> <li>unfiltered_tweets_containing_hate_words.csv: All tweets with hate words collected with values for Perspective API attributes.</li> </ul> <p><strong>Reference:</strong></p> <p>1. Hickey, D., Schmitz, M., Fessler, D.M.T, Smaldino, P., Muric, G., & Burghardt, K. Auditing Elon Musk's Impact on Hate Speech and Bots. In Proceedings of the 17th International AAAI Conference on Web and Social Media, (2023).</p>
SWAHILI AND CODE-SWITCHED ENGLISH-SWAHILI POLITICAL HATE SPEECH DETECTION TEXTUAL DATASET
<p>This dataset consist of swahili and code switched English-Swahili tweets labeled for hate speech type,the target and language.</p> <p>The dataset can be accessed upon request from the authors via email addresses:</p> <p> -nodhianbo@gmail.com</p> <p> -endeshanelly@gmail.com</p> <p> -mscci00083@student.maseno.ac.ke</p>
Hateful Messages: A Conversational Data Set of Hate Speech produced by Adolescents on Discord
<p>With the rise of social media, a rise of hateful content can be observed. Even though the understanding and definitions of hate speech varies, platforms, communities, and legislature all acknowledge the problem. Therefore, adolescents are a new and active group of social media users. The majority of adolescents experience or witness online hate speech. Research in the field of automated hate speech classification has been on the rise and focuses on aspects such as bias, generalizability, and performance. To increase generalizability and performance, it is important to understand biases within the data. This research addresses the bias of youth language within hate speech classification and contributes by providing a modern and anonymized hate speech youth language data set consisting of 88.395 annotated chat messages. The data set consists of publicly available online messages from the chat platform Discord. ~6,42\% of the messages were classified by a self-developed annotation schema as hate speech. For 35.553 messages, the user profiles provided age annotations setting the average author age to under 20 years old.</p>
Data for Reported user-generated online hate speech: The 'ecosystem', frames, and ideologies
<p>This is the dataset for the article entitled Reported user-generated online hate speech: The 'ecosystem', frames, and ideologies. The same dataset is provided in two different formats: comma-separated values (.csv) and Excel format (.xlsx). A basic legend to the data is provided separately in the corresponding PDF document. An extended legend to the data is available at: <strong><a href="https://doi.org/10.5281/zenodo.6656185">https://doi.org/10.5281/zenodo.6656185</a></strong>.</p>
Dataset: Understanding and Detecting Hateful Content using Contrastive Learning
<p> </p> <p>This is the dataset released with the <a href="https://arxiv.org/abs/2201.08387">paper</a> titled: "<strong>Understanding and Detecting Hateful Content using Contrastive Learning</strong>".</p> <p>We release our dataset in four CSV files. These files contain the textual and visual dataset and the textual and visual ground truth we obtained after our manual annotations. For a detailed description of every <strong><em>column </em></strong>in the CSV structure, along with the type of the <strong><em>value</em></strong>, please read the readme.pdf file provided with this dataset.</p> <p>The images are stored in the zip files. </p> <p>If you find our dataset useful, please cite our paper:</p> <pre><code>@inproceedings{gonzalez2023understanding, title={Understanding and Detecting Hateful Content using Contrastive Learning}, author={Gonz{\'a}lez-Pizarro, Felipe and Zannettou, Savvas}, booktitle={17th International AAAI Conference On Web And Social Media (ICWSM), 2023}, year={2023} } </code></pre> <p>In case of questions, please do not hesitate to contact us: felipegp[at]cs.ubc.ca (<a href="https://gonzalezf.github.io">https://gonzalezf.github.io</a>)</p>
Hate speech and personal attack dataset in French social media
<p>This dataset contains 29109 French tweet ids and corresponding annotations for Hate Speech label and 39109 French tweet ids and corresponding annotations for Personal attack label. The creation of this dataset was part of the project DACHS “A Data-driven Approach to Countering Hate Speech” funded by the Rights, Equality and Citizenship Programme of the European Union.</p>
Hate speech and personal attack dataset in Spanish social media
<p>This dataset contains 37688 Spanish tweet ids and corresponding annotations for Hate Speech label and 37688 Spanish tweet ids and corresponding annotations for Personal attack label. The creation of this dataset was part of the project DACHS “A Data-driven Approach to Countering Hate Speech” funded by the Rights, Equality and Citizenship Programme of the European Union.</p>
Hate speech and personal attack dataset in German social media
<p>This dataset contains 43735 German tweet ids and corresponding annotations for Hate Speech label and 43734 German tweet ids and corresponding annotations for Personal attack label. The creation of this dataset was part of the project DACHS “A Data-driven Approach to Countering Hate Speech” funded by the Rights, Equality and Citizenship Programme of the European Union.</p>
Hate speech and personal attack dataset in English social media
<p>This dataset contains 92022 English tweet ids and corresponding annotations for Hate Speech label and 90892 English tweet ids and corresponding annotations for Personal attack label. The creation of this dataset was part of the project DACHS “A Data-driven Approach to Countering Hate Speech” funded by the Rights, Equality and Citizenship Programme of the European Union.</p>
Hate speech and personal attack dataset in Greek social media
<p>This dataset contains 61481 Greek tweet ids and corresponding annotations for Hate Speech label and 61481 Greek tweet ids and corresponding annotations for Personal attack label. The creation of this dataset was part of the project DACHS “A Data-driven Approach to Countering Hate Speech” funded by the Rights, Equality and Citizenship Programme of the European Union.</p>
ProHaters - Proactive Profiling of Hate Speech Spreaders
<p>In the framework of the ProHaters - Proactive Profiling of Hate Speech Spreaders project funded by CDTI under grant IDI-20210776, the language resources for stance, polarization, hate speech, and bots have been created and annotated.</p> <p>These datasets cover English, German, and Spanish, including different dialects of English and Spanish.</p> <p>The datasets were created based on publicly available data collected from open sources and/or social media platforms, following their redistribution policies and the General Data Protection Regulation.</p> <p>The created datasets will be used for report writing and building planned tools and prototypes.</p>
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