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66 results for “discourse”
Salvaging the Internet Hate Machine: Using the discourse of extremist online subcultures to identify emergent extreme speech
<p>This dataset accompanies a paper submitted to the WebSci 20 conference. In this paper, we present a lexicon of 'extreme speech' that may be used to detect hate speech and extreme speech on online platforms. We outline a cross-disciplinary research protocol through which this lexicon is initially extracted from a corpus of 3,335,265 posts from 4chan's /pol/ sub-forum using a hybrid method comprising word2vec modeling and subsequent snowballing of nearest neighbours of a small initial expert seed list of extreme language. The choice of corpus is significant, as 4chan is a space of rapid language innovation and obscure extreme vernacular, complicating generalised approaches. Our lexicon detects significantly more extreme posts within a corpus from a more mainstream platform (Reddit) than another popular lexicon, Hatebase, with similar accuracy. Our lexicon and the method of its creation thus provide a contribution to the study of the toxicity of online subcultures similar to 4chan, as well as more mainstream platforms. As we demonstrate, the lexicon allows for more effective detecting of extreme speech in these spaces. This method and the lexicon have further been made available through an open-source web tool for the study of online social platforms, 4CAT. The computational methods and lexicon on offer here can thus be used by a wide academic audience, fostering interdisciplinary approaches to the study of online hate and extreme speech. </p> <p>The dataset comprises the following items:</p> <ul> <li>The 4chan corpus from which the extreme speech lexicon was generated (posts from /pol/, 1 October 2019 - 1 November 2019)</li> <li>The Reddit corpus used to verify and test the lexicon (posts from the_donald, theredpill, politics and chapotraphouse, 1 October 2019 - 1 November 2019)</li> <li>The word2vec model from which the extreme speech lexicon was generated</li> <li>The extreme speech lexicon that was generated</li> </ul>
Accelerating Digital Skills for Music Researchers - Processing Text-Based Corpora for Musical Discourse Analysis - Episode 5
<p>Dataset containing four .xlsx and .csv files for the exercises in Episode 5 of the <a href="https://acceleratingdigitalskills.github.io/Processing-Text-Based-Corpora/">Processing Text-Based Corpora for Musical Discourse Analysis</a> lesson of the <a href="https://acceleratingdigitalskills.org/">Accelerating Digital Skills for Music Researchers</a> project. The original data was collected from <a href="https://boomkat.com/">Boomkat.com</a> with permission.</p>
Student discourse in small-group collaborative contexts in real-world higher education
<p>This dataset contains anonymised student discourse in small-group collaborative contexts in real-world higher education settings.</p> <p>There are 28 sessions, composed of 10799 utterances.</p> <p>The columns consist of 13 columns:</p> <ul> <li>session: the session number</li> <li>start: the starting time of the utterance</li> <li>end: the ending time of the utterance</li> <li>speaker: anonymised speaker name</li> <li>content: the content of the utterance</li> <li>ep: the episode number of the utterance. An episode refers to a single topic of discussion with multiple utterances.</li> <li>C: a binary value indicating the presence or absence of a cognitive challenge, where 0 means no challenge, and 1 means there is a cognitive challenge.</li> <li>E: a binary value indicating the presence or absence of an emotional/motivational challenge, where 0 means no challenge, and 1 means there is an emotional/motivational challenge.</li> <li>M: a binary value indicating the presence or absence of a metacognitive challenge, where 0 means no challenge, and 1 means there is a metacognitive challenge.</li> <li>T: a binary value indicating the presence or absence of a technical/other challenge, where 0 means no challenge, and 1 means there is a technical/other challenge.</li> <li>TA: a binary value indicating the presence or absence of the regulatory process "task analysis," where 0 means no regulation and 1 means task analysis is present.</li> <li>MC: a binary value indicating the presence or absence of the regulatory process "monitoring/control," where 0 means no regulation and 1 means monitoring/control is present.</li> <li>RA: a binary value indicating the presence or absence of the regulatory process "reflection/adaptation," where 0 means no regulation and 1 means reflection/adaptation is present.</li> </ul> <p>This annotated dataset was used in the following paper to model challenge moments.</p> <p>For more details on how the dataset was generated, please refer to the paper.</p> <div> <div>Suraworachet, W., Seon, J., & Cukurova, M. (2024). Predicting challenge moments from students’ discourse: A comparison of GPT-4 to two traditional natural language processing approaches. <em>Proceedings of the 14th Learning Analytics and Knowledge Conference</em>, 473–485. <a href="https://doi.org/10.1145/3636555.3636905">https://doi.org/10.1145/3636555.3636905</a></div> <div> </div> </div>
Public Dataset for "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"
<p>Dataset for the "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" paper. </p> <p>The full text of the paper can be found <a href="https://zenodo.org/record/4699959#.YngKatNBy3K">here</a>.</p> <p>The folder "Tweet_IDs" contains the complete list of the 152,514,929 tweet IDs (together with their timestamps) which we used for the analysis in the study: "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"</p> <p>by Nikos Salamanos, Michael J. Jensen, Costas Iordanou and Michael Sirivianos</p> <p>We have split the tweets into separate .zip files based on the date listed in their timestamps.</p> <p>The crawling took place from September 21 to November 7, 2016 (47 days; we did not collect data on 02/10/2016).</p> <p>Each "tweet_day_X.zip" file contains the file "tweet_day_X.csv", where X in [1,2,...,47]. For instance, the file "tweets_day_1.zip" contains the tweets of the 1st day: 09/21/2016.</p> <p>Please cite the paper in any published work that uses any of these resources. </p> <p>@misc{nikos_salamanos_2021_4699959,<br> author = {Nikos Salamanos and<br> Michael J. Jensen and<br> Costas Iordanou and<br> Michael Sirivianos},<br> title = {{Did State-sponsored Trolls Shape the 2016 US <br> Presidential Election Discourse? Quantifying<br> Influence on Twitter}},<br> month = apr,<br> year = 2021,<br> publisher = {Zenodo},<br> version = 3,<br> doi = {10.5281/zenodo.4699959},<br> url = {https://doi.org/10.5281/zenodo.4699959}<br> }</p>
Pandemic Border Discourses Dataset and Codebook - Swiss Case
<p>The Pandemic Border Discourses project identifies and compares the evolution of discourses restricting internal and external mobility in Europe as the Covid-19 pandemic is unfolding. It is designed to show how political actors use discourses to justify their decisions in emergency situations, and analyse whether and how unforeseen systemic pressure disrupts bordering discourses and practices. It contributes to a better understanding of the political, social and economic issues driving policy decision in times of crisis, above all the tension between national interest and transnational solidarity.</p> <p>This coding manual explains our data collection strategy and introduces the variables of the dataset. Building on a core-sentence analysis method, we collect and analyse institutional discourses about mobility during the Covid-19 crisis on Twitter.</p> <p> </p>
The Voter and the Propagandistic Discourse
<p><span>This study revealed patterns and strategies in digital political communication, and highlighted the importance of the clarity, relevance and emotional tone of social media messages in influencing the perception and decisions of the electorate. </span></p>
Twitter Dataset for Pakistani Political Discourse
<p>This is one of the largest dataset of Pakistani Twitter political discourse and consists of more than 49 million tweets, collected during April 2022.</p> <p>Please use the following citation if you use this this dataset: MLA format: Haq, Ehsan-Ul, et al. "A Twitter Dataset for Pakistani Political Discourse." arXiv preprint arXiv:<a href="https://doi.org/10.48550/arXiv.2301.06316">2301.06316</a> (2023), doi: <a href="https://doi.org/10.48550/arXiv.2301.06316">https://doi.org/10.48550/arXiv.2301.06316</a></p> <p>Bibtex: @misc{2301.06316, Author = {Ehsan-Ul Haq and Haris Bin Zia and Reza Hadi Mogavi and Gareth Tyson and Yang K. Lu and Tristan Braud and Pan Hui}, Title = {A Twitter Dataset for Pakistani Political Discourse}, Year = {2023}, Eprint = {arXiv:2301.06316}, }</p> <p>Relevant paper highlight the dataset collection, and any possible changes is accessible at: https://arxiv.org/abs/2301.06316</p>
Datasets from the paper "Measuring the relationship between the use of typical Manosphere discourse and the engagement of a user with the pick-up artist community"
<p>Datasets from the paper "Measuring the relationship between the use of typical Manosphere discourse and the engagement of a user with the pick-up artist community", presented at the 24th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL).</p> <p>The dataset consists in 2 files:</p> <ul> <li>tweets.csv: This file contains the ids of the tweets and the user that published the tweet.</li> <li>df_users_seeds.csv: This file contains a table were the first field is the user id; the other columns indicate the seed number and if the user follows that seed (1) or not (0)</li> </ul>
Data set 1 discourse analysis BRAD research project
<p>Discourse analysis data set with excerpts of press articles generated in the coding (coded with keywords ‘Brexit’ and ‘deportations’). This data set connects to the WP3 of the BRAD research project.</p>
Bubble reachers and uncivil discourse in polarized online public sphere comments dataset
<p>This dataset contains comments in Portuguese and English gathered from various sources, such as news websites from Brazil and Canada, social media sites like Facebook and Reddit, e-commerce reviews, Wikipedia comments, among others. Each comment is accompanied by a "toxicity" score provided by the Perspective API.</p> <p><strong>Disclaimer</strong>: This file includes words or language that is considered profane, vulgar or offensive by some readers. Due to the topic studied in this article, quoting offensive language is academically justified, but we nor PLOS in no way endorse the use of these words or the content of the quotes. Likewise, the quotes do not represent the opinions of us or that of PLOS, and we condemn online harassment and offensive language.</p> <p>Column information:</p> <ul> <li><strong>preprocessed_text</strong>: the text after undergoing preprocessing steps;</li> <li><strong>dataset</strong>: the given name of the dataset;</li> <li><strong>source</strong>: the dataset's source name;</li> <li><strong>dataset_source</strong>: a combination of the dataset name with its source to facilitate data aggregation tasks;</li> <li><strong>TOXICITY</strong>: a continuous score between 0.0 and 1.0 provided by the Perspective API.</li> </ul> <p>In addition to the comments, there is a spreadsheet containing analyses referenced in the article associated with this dataset.</p>
Accelerating Digital Skills for Music Researchers - Processing Text-Based Corpora for Musical Discourse Analysis - Episode 2
<p>Dataset containing three subgenre-specific .xlsx files for the exercises in Episode 2 of the <a href="https://acceleratingdigitalskills.github.io/Processing-Text-Based-Corpora/">Processing Text-Based Corpora for Musical Discourse Analysis</a> lesson of the <a href="https://acceleratingdigitalskills.org/">Accelerating Digital Skills for Music Researchers</a> project. The original data was collected from <a href="https://boomkat.com/">Boomkat.com</a> with permission.</p>
The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse
<p>The Reddit Politosphere is a large-scale resource of online political discourse covering more than 600 political discussion groups over a period of 12 years. Based on the <a href="https://doi.org/10.5281/zenodo.3608135">Pushshift Reddit Dataset</a>, it is to the best of our knowledge the largest and ideologically most comprehensive dataset of its type now available. One key feature of the Reddit Politosphere is that it consists of both text and network data. We also release annotated metadata for subreddits and users.</p> <p>Documentation and scripts for easy data access are provided in an associated <a href="https://github.com/valentinhofmann/politosphere">repository</a> on GitHub.</p>
Dataset from "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers" (Sverdljuk et al. 2022)
<p>Contains URNs (identifiers) for the newspapers used in the corpus study "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers".</p> <p>For each subcorpus there is an Excel file containing references to the objects used, together with basic metadata.</p> <p>The corpus definitions can be used in various webapps of the DH-LAB at the National Library of Norway, e.g.:</p> <p><a href="https://beta.nb.no/dhlab/concordances/">https://beta.nb.no/dhlab/concordances/</a></p> <p><a href="https://beta.nb.no/dhlab/collocations/">https://beta.nb.no/dhlab/collocations/</a></p> <p>See more at <a href="https://www.nb.no/dh-lab/">https://www.nb.no/dh-lab/</a></p>
Dataset used in "Exploring Scientific Discourse on Marine Litter in Europe: Review of Sources, Causes and Solutions"
<p>Marine litter is a transboundary environmental issue that affects all the world’s oceans. Marine litter research is a young discipline but one that has exploded during the last five years. However, the increased knowledge of sources and underlying causes to marine litter, as well as knowledge regarding solutions, lack systematic review and synthesis. This study reviews the scientific discourses around plastic marine litter in Europe, and more specifically, in Norway and Denmark, and explores emerging discourse coalitions. Four main thematic storylines on the source-cause-solution causal relationship, as well as two emerging storylines within marine litter research, are found. This study concludes that in order to secure sustainability of solutions and to avoid risk transformation and greenwashing, more interdisciplinary research, including life cycle assessment, is needed.</p> <p>The data set contains three elements:</p> <p>*Full sample* contains all data (both excluded and included articles. Coloums can be sortet and filtered to focus on specific topics. Analysis concept based on PRISMA</p> <p>*Timeline* harvest relevant data from 'full sample' for table 4 in the article. Cross year topic-counts are used for figure 3 in the article.</p> <p>*Storyline-connections* harvest relevant data from 'full sample' for figure 3 in the article</p>
AgoraSpeech: A multi-annotated comprehensive dataset of political discourse through the lens of humans and AI
<blockquote> <p>Political discourse datasets are important for gaining political insights, analyzing communication strategies or social science phenomena. Although numerous political discourse corpora exist, comprehensive, high-quality, annotated datasets are scarce. This is largely due to the substantial manual effort, multidisciplinarity, and expertise required for the nuanced annotation of rhetorical strategies and ideological contexts. In this paper, we present <strong>AgoraSpeech</strong>, a meticulously curated, high-quality dataset of 171 political speeches from six parties during the Greek national elections in 2023. The dataset includes annotations (per paragraph) for six natural language processing (NLP) tasks: text classification, topic identification, sentiment analysis, named entity recognition, polarization and populism detection. A two-step annotation was employed, starting with ChatGPT-generated annotations and followed by exhaustive human-in-the-loop validation. The dataset was initially used in a case study to provide insights during the pre-election period. However, it has general applicability by serving as a rich source of information for political and social scientists, journalists, or data scientists, while it can be used for benchmarking and fine-tuning NLP and large language models (LLMs).</p> </blockquote>
COVID-19 Pandemic Twitter Discourse Full Dataset
<p>This is the full dataset of all tweets collected for the project "The Impact of the COVID-19 Pandemic on Bordering Discourses Regarding Migration and Mobility in Europe". The dataset contains over 75 000. The codebook associated with the dataset can be found here: https://zenodo.org/record/6619705#.Y8-6l3bMKUk </p> <p>Further inquiries can be adressed to Marie-Eve Bélanger (www.marie-eve-belanger-phd.com)</p> <p>Summary of the research project:</p> <p>This research project traces the evolution of migration and mobility control measures and their discursive justification in Europe as the global crisis provoked by the Covid-19 pandemic is unfolding. It is designed to show how multi-level decision-making structures and processes react to emergency situations, and how unforeseen systemic pressures disrupt political narratives about migration and mobility. Drawing on previous research on bordering discourses in Europe, we seek to identify the perturbing impact of an external shock, in this case the Covid-19 pandemic, on established discursive practices in national parliaments and among policy-makers regarding migration, mobility and border controls in Europe.</p>
dataset_eastern_german_crisis_discourse_1976-86
<p>This dataset has been considered suitable for exploring and analysing the Eastern German Crisis Discourse from 1976 to 1986, and consists of important speeches contained in five volumes of the protocol of the party congress of the Socialist Unity Party of Germany (SED). The speeches have been digitized by scanning them into PDF files and then converting them into machine-readable TEXT files using OCR software. These TEXT files have been processed with TagAnt (v.2.0.4 Windows 10 64-bit), an annotation software. According to the data returned from AntConc (v.4.0.5 Windows 10 64-bit) the dataset contains four corpora: a main corpus with a total of 184,750 tokens and 16,143 types, a 'corpus A' with 70,533 tokens and 11,964 types, a 'corpus B' with 65,757 tokens and 11,967 types, and a 'corpus C' with 48,460 tokens and 8,145 types.</p>
Dataset of the linguistic analysis of the Eastern German Crisis Discourse from 1976 to 1986
<p>The dataset consists of a ZIP file with the speeches contained in the five volumes of the protocol of the party congress of the Socialist Unity Party of Germany (SED) between 1976 and 1986. The texts have been digitized as PDF files and then converted into machine-readable TEXT files using an OCR software. These TEXT files have been parsed with TagAnt (v. 2.0.4 Windows 10 64-bit), an annotation software. According to the data returned by AntConc (v. 4.0.5 Windows 10 64-bit), four corpora have been created: a main corpus with a total of 184,750 tokens and 16,143 types, a 'corpus A' with 70,533 tokens and 11,964 types, a 'corpus B' with 65,757 tokens and 11,967 types, and a 'corpus C' with 48,460 tokens and 8,145 types. The main corpus includes all speeches from the five volumes, while the three additional corpora have been created based on specific criteria or topics. The 'corpus A' and 'corpus B' have similar token counts and types, and likely differ based on a specific subset of speeches or themes. The 'corpus C' is the smallest corpus, with a focus on a specific aspect of the discourse. This dataset is suitable for exploring and analyzing the Eastern German Crisis Discourse from 1976 to 1986, particularly for scholars who may be interested in political and historical analysis.</p>
Social media data from online discourse on Nigeria's budget
<p>The social media data was obtained by extracting online discourse on Nigeria's 2013 budget. This was done as a component of the media analysis on the case study investigating the use of the online national budget of Nigeria which is part of the 'Exploring the Emerging Impacts of Open Data in Developing Countries' (ODDC) research project.</p>
Analyzing Linguistic Patterns in the Social Media Discourse of Juan Guaidó and Nicolás Maduro during the 2019 Political Conflict in Venezuela - research data
<p>In this paper, the political conflict in Venezuela in 2019 is approached from a corpus linguistic point of view. The conflict between Juan Guaidó, the speaker of the parliament, and Nicolás Maduro, who won the internationally unrecognized 2018 presidential election, escalated on January 23, 2019, when Guaidó proclaimed himself the legitimate president of Venezuela. By comparatively analyzing the tweets of the two politicians three months before and after January 23, 2019, a corpus-based discourse analysis will be conducted to investigate whether and to what extent linguistic patterns (especially most frequent words and their co-occurrences, as well as n-grams) change within the respective social media communication of these political opponents. The analysis reveals changes in linguistic patterns, especially with respect to co-occurrences and n-grams, detected in the corpus data, and demonstrates that politicians use Twitter to present themselves, in the case of Guaidó, as the representative of the people wanting to lead Venezuela into a democratic future, and, in the case of Maduro, as the only legitimate president and defender of Venezuela against internal and external threats.</p>
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