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66 results for “discourse”
Documents used in the PLANET4B analysis of biodiversity discourse by environmental NGOs
<p>These files include press releases that have been published on the internet by European environmental NGOs, and which were used in the PLANET4B project analysis of the discourse on biodiversity.</p>
Documents used in the PLANET4B analysis of biodiversity discourse by political parties
<p>These files include press releases that have been published on the internet by European political parties, and which were used in the PLANET4B project analysis of the discourse on biodiversity.</p>
Documents used in the PLANET4B D1.1 analysis of biodiversity discourse by news outlets - 2010 and 2022 Data
<p>Data used to analyse biodiversity discourse in news outlet as part of Deliverable D1.1. of the Planet4B Project.</p>
Replication data and code for: Environmental discourse exhibits consistency and variation across spatial scales on Twitter
<p>Social media platforms, such as Twitter, are an increasingly important source of information and are forums for discourse within and between interest groups. Research highlights how social media communities have amplified movements such as the Arab Spring, #MeToo, and Black Lives Matter. But environmental digital discourse remains underexplored. In the present article, we apply automated text analysis to 200,000 Twitter users in several countries following leading environmental nongovernmental organizations. Some issues such as public action to decarbonize society or species conservation were discussed more intensely than agriculture or marine conservation. Our results illustrate where environmental discourse diverges and converges on Twitter across countries, states, and characteristics, such as political ideology. Using the coterminous United States as a case study, we observed that the prominence of issues varies across states and, in some cases, covaries with political ideology across counties. Our findings show paths forward to characterizing environmental priorities across many issues at unprecedented scale and extent. In this repository, we provide data and code to reproduce the results in the main text of this manuscript. This replication code and dataset accompany this manuscript: https://doi.org/10.1093/biosci/biac051</p>
Discourse Analysis of Spanish Geographical Indications
<p>The data were collected through a questionnaire administered between November 2019 and August 2020. The dataset presents 44 statements organized by columns, in accordance with the distribution of the grid used in the Q methodology.</p>
Defining and identifying Discourse Markers in spontaneous speech. A corpus-based and experimental proposal
<p>The paper has a twofold goal: (i) to identify the cathegory of Discourse Markers (DM) and their different specific functions; (ii) to validate the proposal with a perceptual experiment. First, we propose how to identify DM and their different functions in spontaneous speech. Both the identification of the categorycategory of DM and that of specific DM functions are based on prosodic criteria. In order to identify a DM, speech segmentation is crucial, since DMs necessarily appear isolated in a prosodic unit. Besides prosodic isolation, DMs do not feature pragmatic and prosodic autonomy, but depend on the illocutionary unit of the utterance. Than we show that a same lexical item can fulfill more functions, while the formal cues that convey the function are prosodic ones. The last part of the chapter is devoted to presenting the methodology and the results of a perceptual experiment that tests the theoretical hypothesis by asking the listeners to recognize three different functions by means of prosodic cues only.</p>
Defining and identifying Discourse Markers in spontaneous speech: a corpus-based and experimental proposal
<p>Audio files used as examples in the paper.</p>
Discourse - wooden statue
Discourse Autor: Alex Stalenberg Staffed: 2004 Material: Wood Place: Brno dam, Czech Republic Sculpture symposium - Brno dam The two figures in the silhouettes connected to the jing-jang sign are the symbol of unity and harmony. Choosing the way of understanding and mutual cooperation among people can lead to a sense of satisfaction and victory. This is what the figures suggest, looking out of the way is the sun above their heads, the emptiness in the opposite direction. At the top of both characters, birds have nests inside the sculpture. Photogrammetry from 326 photos. Source: Objaverse 1.0 / Sketchfab
Narrative Discourse Treatment Development
ClinicalTrials.gov study NCT05008419. IPD Sharing: YES. Countries: 1. Publications: 0.
Replication data and code for: Environmental discourse exhibits consistency and variation across spatial scales on Twitter
Open the record for dataset details and reuse information.
Data from: Delisting the Northern Rocky Mountain Gray Wolf from the U.S. Endangered Species Act: An assessment of political discourse over 20 years
Open the record for dataset details and reuse information.
The Viennese Discourse on the Avant-Garde
<p>This dataset was first compiled in course of the dissertation project “The Viennese Discourse on the Avant-Garde” (ger. Der Wiener Diskurs zur Avantgarde) in 2017.</p> <p>For the book publication, the whole data has been updated in 2019. It now contains</p> <ul> <li>a corpus of approximately 300 newspaper articles (docx, pdf, txt)</li> <li>a data file with all 752 annotated statements (dna) and</li> <li>a list of statements (xlsx, csv).</li> </ul> <p>Link to the <a href="https://www.academia.edu/36042781/Defensio_DER_WIENER_DISKURS_ZUR_AVANTGARDE_">presentation</a> held in course of the defensio.</p>
Analysis of Network of Discourse, Nature
<p><strong>Note:</strong> This dataset is also available as a Git repository, currently located at <a href="https://codeberg.org/cpence/nature-debate.charlespence.net">https://codeberg.org/cpence/nature-debate.charlespence.net</a>.</p> <p>This is the raw data accompanying a network analysis of biologists participating in the "biometry-Mendelism" debate, as it occurred in the correspondence pages of the journal <em>Nature,</em> between 1890 and 1915. It accompanies the following book chapter:</p> <p>Pence, Charles H. forthcoming (likely 2021). "How Not to Fight About Theory: The Debate Between Biometry and Mendelism in <em>Nature</em>, 1890–1915." In De Block, A. and Ramsey, G., eds., <em>The Dynamics of Science: Computational Frontiers in History and Philosophy of Science.</em> Pittsburgh, PA: Univ. of Pittsburgh Press.</p>
The Translation of Cultural Referents in African Feminist Discourse
<p>This corpus-based research investigated the problem of translating cultural referents from an African feminist context into English. It was based on the premise that due to the inherent differences in the two cultures, the translation exists in a space of deviation. Hence, cultural referents in Calixthe Beyala's (2000) Comment cuisiner son mari à l'africaine were identified and classified under Santamaria's (2010:522) five ideological categories, namely history, social structure, cultural institutions, social universe and material culture. Thereafter, the strategies for translating them were identified and the space of existence of the translations was investigated. Toury's (1985) Descriptive Translation Studies (DTS) and Lazar's (2005) Feminist Critical Discourse Analysis were applied for data collection and analyses to identify, describe, and explain 20 excerpts which embed cultural referents (five from each ideological category). For literary analyses, post-structuralist, formalist, biographical and sociological criticism were applied. Deconstruction, manipulation and communicative/functional translation theories were amenable to the research, while post-modern and cultural theories were applied for feminist analyses. The findings revealed that the translator mostly used hijacking and oblique translation to render cultural referents from French into English. The translations produced by these strategies were judged, according to Von Flotow's (1998) criteria, to determine if they were shoddy, inaccessible/elitistor theoretically incongruent/hypocritical translations. The conclusion arrived at was that the rendering of cultural referents in the target text was, indeed, a complete deviation due to loss or gain, untranslatability, the problem of equivalence, issues with decoding and recoding as well as lapses attributable to the fundamental linguistic and cultural differences between the source and target languages. This confirmed the assumption that the translation of cultural referents in the novel exists as a heterotopia of deviation. It was, therefore, recommended that to adequately render cultural referents in feminist discourse, precedence should be given to the text-in-situation, that is, extralinguistic elements that condition text interpretation and reformulation, that are conditioned by cultural and linguistic differences between the author and translator.</p>
Datasets for "Discourse-Aware Unsupervised Summarization for Long Scientific Documents"
<p>Datasets used in <a href="https://aclanthology.org/2021.eacl-main.93.pdf">Discourse-Aware Unsupervised Summarization for Long Scientific Documents</a></p>
DATA BASE-OFFENSIVE DISCOURSE ON TWITTER: VOX IN THE CATALAN PARLIAMENTARY ELECTIONS
<p>DATA BASE-OFFENSIVE DISCOURSE ON TWITTER: VOX IN THE CATALAN PARLIAMENTARY ELECTIONS</p>
Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis
<p><strong>Please cite the following paper when using this dataset</strong>:</p> <p>N. Thakur, “Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis”, Proceedings of the 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024), Chengdu, China, October 18-20, 2024 (Paper accepted for publication, Preprint available at: https://arxiv.org/abs/2410.03293)</p> <p> </p> <p><strong>Abstract</strong></p> <p>The outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect and communicate with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19-related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse on Instagram in this context. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. The work presented in this paper aims to address this research gap and presents a novel multilingual dataset of <strong>500,153 Instagram posts about COVID-19 published between January 2020 and September 2024</strong>. This dataset contains Instagram posts in <strong>161 different languages</strong>. After the development of this dataset, multilingual sentiment analysis was performed using VADER and twitter-xlm-roberta-base-sentiment. This process involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset.</p> <p><em><strong>For each of these posts, the Post ID, Post Description, Date of publication, language code, full version of the language, and sentiment label are presented as separate attributes in the dataset.</strong></em></p> <p>The Instagram posts in this dataset are present in <strong>161 different languages</strong> out of which the top 10 languages in terms of frequency are English (343041 posts), Spanish (30220 posts), Hindi (15832 posts), Portuguese (15779 posts), Indonesian (11491 posts), Tamil (9592 posts), Arabic (9416 posts), German (7822 posts), Italian (5162 posts), Turkish (4632 posts)</p> <p>There are <strong>535,021 distinct hashtags in this dataset</strong> with the top 10 hashtags in terms of frequency being #covid19 (169865 posts), #covid (132485 posts), #coronavirus (117518 posts), #covid_19 (104069 posts), #covidtesting (95095 posts), #coronavirusupdates (75439 posts), #corona (39416 posts), #healthcare (38975 posts), #staysafe (36740 posts), #coronavirusoutbreak (34567 posts)</p> <p>The following is a description of the attributes present in this dataset</p> <ul> <li><em><strong>Post ID</strong></em>: Unique ID of each Instagram post</li> <li><em><strong>Post Description</strong></em>: Complete description of each post in the language in which it was originally published</li> <li><em><strong>Date</strong></em>: Date of publication in MM/DD/YYYY format</li> <li><em><strong>Language code</strong></em>: Language code (for example: “en”) that represents the language of the post as detected using the Google Translate API </li> <li><em><strong>Full Language</strong></em>: Full form of the language (for example: “English”) that represents the language of the post as detected using the Google Translate API </li> <li><em><strong>Sentiment</strong></em>: Results of sentiment analysis (using the preprocessed version of each post) where each post was classified as positive, negative, or neutral</li> </ul> <p><strong>Open Research Questions</strong></p> <p>This dataset is expected to be helpful for the investigation of the following research questions and even beyond:</p> <ol> <li>How does sentiment toward COVID-19 vary across different languages?</li> <li>How has public sentiment toward COVID-19 evolved from 2020 to the present?</li> <li>How do cultural differences affect social media discourse about COVID-19 across various languages?</li> <li>How has COVID-19 impacted mental health, as reflected in social media posts across different languages?</li> <li>How effective were public health campaigns in shifting public sentiment in different languages?</li> <li>What patterns of vaccine hesitancy or support are present in different languages?</li> <li>How did geopolitical events influence public sentiment about COVID-19 in multilingual social media discourse?</li> <li>What role does social media discourse play in shaping public behavior toward COVID-19 in different linguistic communities?</li> <li>How does the sentiment of minority or underrepresented languages compare to that of major world languages regarding COVID-19?</li> <li>What insights can be gained by comparing the sentiment of COVID-19 posts in widely spoken languages (e.g., English, Spanish) to those in less common languages?</li> </ol> <p>All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data for manuscript: "Prevalence of prejudice denoting words in news media discourse: a chronological analysis"
<p>This data set contains frequency counts of target words in 27 million news and opinion articles from 47 popular news media outlets in the United States. The target words are listed in the associated manuscript and are mostly words that denote some type of prejudice. A few additional words not denoting prejudice are also available since they are used in the manuscript for illustration purposes.</p> <p>The textual content of news and opinion articles from the outlets listed in Figure 4 of the main manuscript is available in the outlet's online domains and/or public cache repositories such as Google cache (https://webcache.googleusercontent.com), The Internet Wayback Machine (https://archive.org/web/web.php), and Common Crawl (https://commoncrawl.org). We used derived word frequency counts from these sources. Textual content included in our analysis is circumscribed to articles headlines and main body of text of the articles and does not include other article elements such as figure captions.</p> <p>Targeted textual content was located in HTML raw data using outlet specific xpath expressions. Tokens were lowercased prior to estimating frequency counts. To prevent outlets with sparse text content for a year from distorting aggregate frequency counts, we only include outlet frequency counts from years for which there is at least 1.25 million words of article content from an outlet. This threshold was chosen to maximize inclusion in our analysis of outlets with sparse amounts of articles text per year such as Reason, Alternet or The American Spectator. </p> <p>Yearly frequency usage of a target word in an outlet in any given year was estimated by dividing the total number of occurrences of the target word in all articles of a given year by the number of all words in all articles of that year. This method of estimating frequency accounts for variable volume of total article output over time.</p> <p>The list of compressed files in this data set is listed next:</p> <p>-analysisScripts.rar contains the analysis scripts used in the main manuscript </p> <p>-articlesContainingTargetWords.rar contains counts of target words in outlets articles as well as total counts of words in articles</p> <p>-cableNews.rar contains prevalence of target words in TV cable news. Data is from Stanford Cable TV News Analyzer (https://tvnews.stanford.edu/)</p> <p>-surveyData.rar contains longitudinal survey data used in the manuscript and links to original sources</p> <p>Usage Notes</p> <p>In a small percentage of articles, outlet specific XPath expressions failed to properly capture the content of the article due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. As a result, the total and target word counts metrics for a small subset of articles are not precise. In a random sample of articles and outlets, manual estimation of target words counts overlapped with the automatically derived counts for over 90% of the articles.</p> <p>Most of the incorrect frequency counts were minor deviations from the actual counts such as for instance counting the word "Facebook" in an article footnote encouraging article readers to follow the journalist’s Facebook profile and that the XPath expression mistakenly included as the content of the article main text. Some additional outlet-specific inaccuracies that we could identify occurred in "The Hill" and "Newsmax" news outlets where XPath expressions had some shortfalls at precisely capturing articles’ content. For "The Hill", in years 2007-2009, XPath expressions failed to capture the complete text of the article in about 40% of the articles. This does not necessarily result in incorrect frequency counts for that outlet but in a sample of articles’ words that is about 40% smaller than the total population of articles words for those three years. In the case of "NewsMax", the issue was that for some articles, XPath expressions captured the entire text of the article twice. Notice that this does not result in incorrect frequency counts. If a word appears x times in an article with a total of y words, the same frequency count will still be derived when our scripts count the word 2x times in the version of the article with a total of 2y words. To conclude, in a data analysis of 27 million articles, we cannot manually check the correctness of frequency counts for every single article and hundred percent accuracy at capturing articles’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our frequency metrics are representative of word prevalence in print news media content (see Figure 1 and Figure 2 of main manuscript for supporting evidence).</p>
HYEEG Discourse in Psychosis: A Neurobehavioural Study
ClinicalTrials.gov study NCT06978803. IPD Sharing: YES. Countries: 1. Publications: 27.
Spoken Discourse Biomarker of PD Cognitive Impairment
ClinicalTrials.gov study NCT05909163. IPD Sharing: Not stated. Countries: 1. Publications: 8.
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OpenNeuro
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