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MUSIED
- opendatalab.com
zipUpdated Feb 5, 2023+ more versions
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EmailClick to copy linkLink copiedCitePeking University (2023). MUSIED [Dataset]. https://opendatalab.com/OpenDataLab/MUSIEDzip(35559885 bytes)Available download formatsDataset updatedFeb 5, 2023Dataset provided byPeking UniversityLicensehttps://choosealicense.com/licenses/agpl-3.0/https://choosealicense.com/licenses/agpl-3.0/
DescriptionWe propose a new largescale Chinese event detection dataset based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.
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General conversation speech datasets in English for Wedding Planning
- data.macgence.com
mp3Updated Jul 31, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteMacgence (2024). General conversation speech datasets in English for Wedding Planning [Dataset]. https://data.macgence.com/dataset/general-conversation-speech-datasets-in-english-for-wedding-planningmp3Available download formatsDataset updatedJul 31, 2024Dataset authored and provided byMacgenceLicensehttps://data.macgence.com/terms-and-conditionshttps://data.macgence.com/terms-and-conditions
Time period covered2025Area coveredWorldwideVariables measuredOutcome, Call Type, Transcriptions, Audio Recordings, Speaker Metadata, Conversation TopicsDescriptionPlan memorable weddings with Macgence's English conversation dataset. Ideal for AI, linguistics, and event tech, ensuring relevance and impactful insights!
Data from: PHEME dataset of rumours and non-rumours
- figshare.com
bz2Updated May 30, 2023+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteArkaitz Zubiaga; Geraldine Wong Sak Hoi; Maria Liakata; Rob Procter (2023). PHEME dataset of rumours and non-rumours [Dataset]. http://doi.org/10.6084/m9.figshare.4010619.v1bz2Available download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.4010619.v1Dataset updatedMay 30, 2023Dataset provided byfigshareAuthorsArkaitz Zubiaga; Geraldine Wong Sak Hoi; Maria Liakata; Rob ProcterLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThis dataset contains a collection of Twitter rumours and non-rumours posted during breaking news. The five breaking news provided with the dataset are as follows:* Charlie Hebdo: 458 rumours (22.0%) and 1,621 non-rumours (78.0%).* Ferguson: 284 rumours (24.8%) and 859 non-rumours (75.2%).* Germanwings Crash: 238 rumours (50.7%) and 231 non-rumours (49.3%).* Ottawa Shooting: 470 rumours (52.8%) and 420 non-rumours (47.2%).* Sydney Siege: 522 rumours (42.8%) and 699 non-rumours (57.2%).The data is structured as follows. Each event has a directory, with two subfolders, rumours and non-rumours. These two folders have folders named with a tweet ID. The tweet itself can be found on the 'source-tweet' directory of the tweet in question, and the directory 'reactions' has the set of tweets responding to that source tweet.This dataset was used in the paper 'Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media' for rumour detection. For more details, please refer to the paper.License: The annotations are provided under a CC-BY license, while Twitter retains the ownership and rights of the content of the tweets.
PHEME dataset for Rumour Detection and Veracity Classification
- figshare.com
application/gzipUpdated May 30, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteElena Kochkina; Maria Liakata; Arkaitz Zubiaga (2023). PHEME dataset for Rumour Detection and Veracity Classification [Dataset]. http://doi.org/10.6084/m9.figshare.6392078.v1application/gzipAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.6392078.v1Dataset updatedMay 30, 2023AuthorsElena Kochkina; Maria Liakata; Arkaitz ZubiagaLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThis dataset contains a collection of Twitter rumours and non-rumours posted during breaking news.
The data is structured as follows. Each event has a directory, with two subfolders, rumours and non-rumours. These two folders have folders named with a tweet ID. The tweet itself can be found on the 'source-tweet' directory of the tweet in question, and the directory 'reactions' has the set of tweets responding to that source tweet. Also each folder contains ‘annotation.json’ which contains information about veracity of the rumour and ‘structure.json’, which contains information about structure of the conversation.
This dataset is an extension of the PHEME dataset of rumours and non-rumours (https://figshare.com/articles/PHEME_dataset_of_rumours_and_non-rumours/4010619), it contains rumours related to 9 events and each of the rumours is annotated with its veracity value, either True, False or Unverified.
This dataset was used in the paper 'All-in-one: Multi-task Learning for Rumour Verification'. For more details, please refer to the paper.
Code using this dataset can be found on github (https://github.com/kochkinaelena/Multitask4Veracity).
License: The annotations are provided under a CC-BY license, while Twitter retains the ownership and rights of the content of the tweets.
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We propose a new largescale Chinese event detection dataset based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.