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24 results for “Telegram”
Messages posted on 25 pro-Bolsonaro Telegram groups during August 2022
<p>This dataset comprises 195,567 message IDs shared by 6,802 unique Telegram users on 25 pro-Bolsonaro public Telegram groups during August 2022, when the presidential campaign for the 2022 Brazilian elections started. </p><p>The message ID provided by Telegram API is unique only for the group or channel where it was posted. For this reason, the dataset provides the message ID and the group username (@). </p>
A Computational Analysis of Telegram's Narrative Affordances
<p><strong>Overview</strong></p> <p>Anonymized message classification data and actantial analyses of public Telegram channels pertaining to the paper "A Computational Analysis of Telegram's Narrative Affordances". </p> <p><strong>Message classification data</strong></p> <p>All files are included in the zipped folder 'narrative_affordances_data.zip'</p> <p>Each file contains the message classification data for a single Telegram channel. Numbered files are included for each of the six datasets (1 combined, 5 thematic) discussed in the paper. </p> <p><strong>Actantial analysis</strong></p> <p>Frequency lists of retrieved actants are included in the zipped folder 'overview_of_actants.zip'</p> <p> </p>
An Inductive Analysis of the Kremlin's Weaponization of Digital Diplomacy on Telegram
<p>This dataset accompanies the paper "An Inductive Analysis of the Kremlin's Weaponization of Digital Diplomacy on Telegram" </p> <p>The file '4cat_data.csv' contains the original data for the 129 Telegram channels analysed in the paper, as scraped through the 4cat capture and analysis toolkit. </p> <p>The file embassy_topic_model_70.zip contains the topic model that was created from the Telegram messages, along with additional documentation and visualizations of the model. </p>
Decoded telegrams of surface weather observations in the Southern Ocean on board the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains decoded reports of surface weather observations in the Southern Ocean made aboard the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE), in the austral summer of 2016/2017. The observations were made by a meteorologist on board the ship and sent as telegrams encoded with World Meteorological Organisation (WMO) FM 13 code form. Posteriorly, the telegrams were decoded to produce this dataset.</p> <p>The telegrams were recorded on a 3-hourly basis and contain information about present and past weather; latitude, longitude, speed and direction of the ship; visibility; type, height and amount of clouds; speed and direction of wind; air, dew-point and sea-surface temperatures; barometric pressure; period, height and direction of waves; concentration of sea ice; etc. For more information on this type of report, please see the WMO Manual on Codes (WMO-No.306), Volume I.1, Part A. The original encoded files have been published separately (Veledin and Gorodetskaya, 2020; DOI 10.5281/zenodo.3734884).</p> <p>Data files have undergone no processing or quality-checking, therefore, if the variable you need is available in another ACE dataset, it is recommended to use the quality-checked data. Quality-checked meteorological data collected during ACE have been published separately (Landwehr et al., 2019; DOI 10.5281/zenodo.3379590).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE_telegram_decoded_YYYY_MM_DD_HHh.txt, data file, ASCII comma-separated</li> <li>README.txt, metadata, text file</li> <li>data_file_header.txt, metadata, text file</li> <li>Telegram_code_format.pdf, metadata, portable document format</li> </ul> <p><strong>Dataset license</strong></p> <p>This set of decoded meteorological telegrams are made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/</p>
Original telegrams of surface weather observations in the Southern Ocean on board the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains original reports of surface weather observations in the Southern Ocean made aboard the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE), in the austral summer of 2016/2017. The observations were made by a meteorologist on board the ship and encoded with FM 13–XIV Ext. SHIP code form (Report of surface observation from a sea station). The encoded telegrams were sent to the World Meteorological Organisation (WMO) and archived on the ship.</p> <p>The telegrams were usually recorded on a 3-hourly basis (at 0000, 0300, 0600, 0900, 1200, 1500, 1800, 2100 UTC) and on some dates on a 6-hourly basis. They contain information about present and past weather; latitude, longitude, speed and direction of the ship; visibility; type, height and amount of clouds; speed and direction of wind; air, dew-point and sea-surface temperatures; barometric pressure; period, height and direction of waves; concentration of sea ice; information about land ice. Data coverage is from November 2016 until April 2017, with some data missing from February and March.</p> <p>For more information on this type of report, please see the WMO Manual on Codes (WMO-No.306), Volume I.1, Part A:<br> https://library.wmo.int/index.php?lvl=notice_display&id=13617#.XoHf4oW3Ih4</p> <p>Data files have undergone no processing or quality-checking, therefore, if the variable you need is available in another ACE dataset, it is recommended to use the quality-checked data. These telegrams have been decoded and are available in another related dataset.</p> <p><strong>Dataset contents</strong></p> <ul> <li>UBXH3mmdd.HHh, data file, text file</li> <li>README.txt, metadata, text file</li> </ul> <p><strong>Dataset license</strong></p> <p>This set of original meteorological telegrams are made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/</p>
1492 Telegram channels related with Russian - Ukrainian War.
<p>List of 1492 containing media outlets, oficial governmental accounts, influencers and popular channels related with the Russian-Ukranian War.<br><br>Data include the Channel Names, the Language, Country and Political View of each channel.</p>
Post Office Telegram Motorbike
Quick photogrammetry scan of a motorbike used trialled for delivering telegrams. Now in the Postal Museum, London. Date: 1933 164 photos taken in January 2019 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Dataset | Human mobility messages on Telegram
<p>This dataset comprises six sets of IDs of messages about human mobility shared on thematic Telegram public groups and channels. The themes of the groups and channels from where the messages were extracted: African continent, Climate action, Conspiracism, Iranian, Nationalism, Slovakian.</p> <p>Messages were queried using TeleCatch, an Open Scource tool that enables visualizing, filtering, and extracting Telegram messages data and image files.</p> <p>The keywords used to query Telegram grouops and channels and generate the datasets were: displacement, migrant, migration, diaspora, immigrant, emigration, “brain drain”, remittance, xenophobia, multicultural, border control, asylum, refugee, deport, “human traffic”, resettle, IDP, “border agency”, IOM, UNHCR.</p> <p>The message ID provided by Telegram API is unique only for the group or channel where it was posted. For this reason, the dataset provides the message ID and the group and/or username (@). </p> <p><strong>Technical info </strong></p> <p>The Python script includes a functionality to identify the exclusion or blockage of Telegram groups and/or channels. In addition to generating the messages output file, the script produces a log file. This log file records the status of each Telegram group, providing insights into their current operational state.</p> <p><strong>Other info</strong></p> <p>This dataset was created during the 2024 Digital Methods Initiative Summer School for the <strong>Uncovering visual narratives about human mobility on Telegram </strong>project.<br><br>2024 Digital Methods Initiative Summer School URL: https://wiki.digitalmethods.net/Dmi/SummerSchool2024</p>
Telegram digits dataset
<p>This dataset is MNIST-like, containing digitized handwritten characters extracted from electoral telegrams during the General Elections of Santa Fe, Argentina, in the year 2021. The dataset offers a valuable resource for researchers and practitioners in the field of character recognition, particularly in the context of electoral data analysis. Each sample in the dataset represents a single digit, ranging from 0 to 9, handwritten by different individuals participating in the electoral process. The dataset aims to facilitate the development and evaluation of machine learning and computer vision algorithms for character recognition tasks.</p> <p>It contains 170718 images, splitted in train (119502), validation (25608) and test (25608).</p> <p>This dataset is part of master's thesis in data science which aims to build an Optical Character Recognition (OCR) system using domain adaptation techniques titled "Classification of digits written in the telegrams of legislative elections in Santa Fe using Domain Adaptation techniques".</p>
Telegram_Italian_NoVax
<p>Data obtained through telegram API on the Italian "Put Down the Covid mask" group. Data have been anonymized for privacy. <br>The group itself no longer exists, after it was shut down by the administrator himself. </p> <p><strong>Variables present:</strong><br><em>Interaction_ID</em><br><em>Acc_ID</em><br><em>Text</em><br><em>Time</em><br><em>Reaction</em><br><em>Reply</em></p> <p> </p> <p><strong>Accout used for deradicalisation are:<br></strong><em><span>297039<br></span><span>477475<br></span><span>318812<br></span><span>476878</span></em></p> <p> </p>
Digital Resistance Telegram
Digital Resistance Support Source: Objaverse 1.0 / Sketchfab
Eles não querem que você saiba: ciência distorcida no Telegram
<p>Material suplementar do artigo "Eles não querem que você saiba: ciência distorcida no Telegram".</p> <p>Resumo: Plataformas digitais e aplicativos de mensagens estão entre as fontes de informação sobre ciência e saúde mais acessadas pelos brasileiros. Em 2023, o consumo de vídeos online alcançou 99,63% da população nacional. Nesse contexto, foram analisados vídeos do YouTube compartilhados entre os anos de 2017 e 2019 em um grupo do Telegram dedicado à promoção da chamada “Mineral Miracle Solution” (MMS), visando compreender como a ciência circula no aplicativo. Oitenta vídeos foram avaliados por meio de análise de conteúdo manual. Nem todos os vídeos disseminaram desinformações sobre saúde, mas 74% da amostra analisada sugeriram que se pautam pela ciência ao atribuir uma aparência científica ao seu conteúdo. Todos os vídeos que mencionaram instituições de pesquisa, por exemplo, o fizeram para validar algum tratamento alternativo “milagroso” ou criticar algum dos supostos “inimigos da saúde”, como parasitas, metais pesados e vacinas. A pesquisa demonstra que, apesar das políticas de moderação, conteúdos que desinformam sobre ciência e saúde, publicados entre cinco e sete anos atrás, ainda podem ser facilmente acessados pelo público e continuam sendo fonte de lucro para seus produtores.</p>
Telegram Messenger Support for Smoking Cessation After Heart Attack
ClinicalTrials.gov study NCT07230249. IPD Sharing: YES. Countries: 1. Publications: 6.
Dataset on the online cryptocurrency discussion on Twitter, Telegram, and Discord
<p>This Dataset is described in <em><strong>Charting the Landscape of Online Cryptocurrency Manipulation</strong></em>. <strong><em>IEEE Access (2020)</em></strong>, a study that aims to map and assess the extent of cryptocurrency manipulations within and across the online ecosystems of Twitter, Telegram, and Discord. Starting from tweets mentioning cryptocurrencies, we leveraged and followed invite URLs from platform to platform, building the invite-link network, in order to study the invite link diffusion process.</p> <p>Please, refer to the paper below for more details.</p> <p>Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M. & Ferrara, E. (2020). Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020).</p> <p>This dataset is composed of: </p> <ul> <li>~16M tweet ids shared between March and May 2019, mentioning at least one of the 3,822 cryptocurrencies (cashtags) provided by the CryptoCompare public API;</li> <li>~13k nodes of the invite-link network, i.e., the information about the Telegram/Discord channels and Twitter users involved in the cryptocurrency discussion (e.g., id, name, audience, invite URL);</li> <li>~62k edges of the invite-link network, i.e., the information about the flow of invites (e.g., source id, target id, weight).</li> </ul> <p>With such information, one can easily retrieve the content of channels and messages through Twitter, Telegram, and Discord public APIs.</p> <p>Please, refer to the README file for more details about the fields.</p>
SM-FEEL-BG: 4060 Twitter, Telegram and ChatGPT texts
<p>This is the largest SM-FEEL-BG dataset, containing all Twitter, Telegram posts manually annotated + the 310 ChatGPT-generated texts </p>
TELEGRAM PIRACY
<p>INTERVIEWS WITH COPYRIGHT PIRATES ON TELEGRAM </p>
Evaluating TESLA-G, a Gamified, Telegram-delivered, Quizzing Platform for Surgical Education in Medical Students
ClinicalTrials.gov study NCT05520671. IPD Sharing: NO. Countries: 0. Publications: 30.
Enhancing Anatomy Education for First-Year Medical Students Through a Telegram® Channel
ClinicalTrials.gov study NCT06673420. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
TRACES Bulgarian Telegram Dataset Annotated with Linguistic Markers of Lies
<p><strong>This dataset has been created within Project TRACES (more information: https://traces.gate-ai.eu/). The dataset contains 8791 anonymized Telegram social media posts, written in Bulgarian. The dataset is annotated with general information (named entities, part-of-speech tags, sentence length, etc.) and specific markers signaling details and can be used for general purposes or for building lies, manipulation, and disinformation detection applications. </strong></p> <p><strong>Note: this dataset is not fact-checked, the social media messages have been retrieved via keywords. For fact-checked datasets, see our other datasets.</strong></p> <p><strong>The social media posts have been collected via Telegram Desktop in June-July 2022.</strong></p> <p><strong>Explanations of which fields can be used as markers of lies (or of intentional disinformation) are provided in our forthcoming paper: </strong></p> <p>Irina Temnikova, Silvia Gargova, Ruslana Margova, Veneta Kireva, Ivo Dzhumerov, Tsvetelina Stefanova and Hristiana Nikolaeva (2023) New Bulgarian Resources for Detecting Disinformation. 10th Language and Technology<br> Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics (LTC'23). Poznań. Poland.</p>
TRACES Telegram and Twitter Dataset with Bulgarian Journalists Manual Annotations of True/Untrue and Disinformation/Not and Automatic Annotations for Markers of Lies
<p>TRACES dataset of 4083 Twitter and Telegram posts automatically annotated for markers of lies and manually by Bulgarian journalists for containing true/untrue information and disinformation or not.</p> <p>Each message has been annotated by usually 3 (in under 10 cases by 2 annotators). The annotators came from different media, in order to obtain various views. They were asked to not get biased and were assured that their identities will not be revealed. </p> <p><strong>The dataset is a subset of these other datasets:</strong></p> <p>https://zenodo.org/record/7614247</p> <p>https://zenodo.org/record/7614318</p> <p>https://zenodo.org/record/7614357</p> <p>https://zenodo.org/record/7614294</p> <p> </p> <p><strong>It has been annotated following these Annotation Guidelines:</strong></p> <p>https://zenodo.org/record/7706743</p>
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