SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU’s transition to carbon neutrality. SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU’s journey to a sustainable future.The documents uploaded here are part of WP2 whereby novel, interdisciplinary teams were provided funding to undertake activities to develop a policy recommendation related to EU Green Deal policy. Each of these policy recommendations, and the activities that inform them, will be written-up as a chapter in an edited book collection. Three books will make up this edited collection - one on climate, one on energy and one on mobility. As part of writing a chapter for the SSH CENTRE book on ‘Mobility’, we set out to analyse the sentiment of users on Twitter regarding shared and active mobility modes in Brussels. This involved us collecting tweets between 2017-2022. A tweet was collected if it contained a previously defined mobility keyword (for example: metro) and either the name of a (local) politician, a neighbourhood or municipality, or a (shared) mobility provider. The files attached to this Zenodo webpage is a csv files containing the tweets collected.”.

Context

The following information can also be found at https://www.kaggle.com/davidwallach/financial-tweets. Out of curosity, I just cleaned the .csv files to perform a sentiment analysis. So both the .csv files in this dataset are created by me.

Anything you read in the description is written by David Wallach and using all this information, I happen to perform my first ever sentiment analysis.

"I have been interested in using public sentiment and journalism to gather sentiment profiles on publicly traded companies. I first developed a Python package (https://github.com/dwallach1/Stocker) that scrapes the web for articles written about companies, and then noticed the abundance of overlap with Twitter. I then developed a NodeJS project that I have been running on my RaspberryPi to monitor Twitter for all tweets coming from those mentioned in the content section. If one of them tweeted about a company in the stocks_cleaned.csv file, then it would write the tweet to the database. Currently, the file is only from earlier today, but after about a month or two, I plan to update the tweets.csv file (hopefully closer to 50,000 entries.

I am not quite sure how this dataset will be relevant, but I hope to use these tweets and try to generate some sense of public sentiment score."

Content

This dataset has all the publicly traded companies (tickers and company names) that were used as input to fill the tweets.csv. The influencers whose tweets were monitored were: ['MarketWatch', 'business', 'YahooFinance', 'TechCrunch', 'WSJ', 'Forbes', 'FT', 'TheEconomist', 'nytimes', 'Reuters', 'GerberKawasaki', 'jimcramer', 'TheStreet', 'TheStalwart', 'TruthGundlach', 'Carl_C_Icahn', 'ReformedBroker', 'benbernanke', 'bespokeinvest', 'BespokeCrypto', 'stlouisfed', 'federalreserve', 'GoldmanSachs', 'ianbremmer', 'MorganStanley', 'AswathDamodaran', 'mcuban', 'muddywatersre', 'StockTwits', 'SeanaNSmith'

Acknowledgements

The data used here is gathered from a project I developed : https://github.com/dwallach1/StockerBot

Inspiration

I hope to develop a financial sentiment text classifier that would be able to track Twitter's (and the entire public's) feelings about any publicly traded company (and cryptocurrency)

The dataset has three sentiments namely, negative, neutral, and positive. It contains two fields for the tweet and label.

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. The first 9 weeks of data (from January 1st, 2020 to March 11th, 2020) contain very low tweet counts as we filtered other data we were collecting for other research purposes, however, one can see the dramatic increase as the awareness for the virus spread. Dedicated data gathering started from March 11th to March 29th which yielded over 4 million tweets a day.

The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (70,569,368 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (13,535,912 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the statistics-full_dataset.tsv and statistics-full_dataset-clean.tsv files.

More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter)

As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data. The need to be hydrated to be used.

because of COVID-19

Our dataset comprises 1000 tweets, which were taken from Twitter using the Python programming language. The dataset was stored in a CSV file and generated using various modules. The random module was used to generate random IDs and text, while the faker module was used to generate random user names and dates. Additionally, the textblob module was used to assign a random sentiment to each tweet.

This systematic approach ensures that the dataset is well-balanced and represents different types of tweets, user behavior, and sentiment. It is essential to have a balanced dataset to ensure that the analysis and visualization of the dataset are accurate and reliable. By generating tweets with a range of sentiments, we have created a diverse dataset that can be used to analyze and visualize sentiment trends and patterns.

In addition to generating the tweets, we have also prepared a visual representation of the data sets. This visualization provides an overview of the key features of the dataset, such as the frequency distribution of the different sentiment categories, the distribution of tweets over time, and the user names associated with the tweets. This visualization will aid in the initial exploration of the dataset and enable us to identify any patterns or trends that may be present.

This dataset is a processed version of the Sentiment140 corpus, containing 1.6 million tweets with binary sentiment labels. The original data has been cleaned, tokenized, and prepared for natural language processing (NLP) and machine learning tasks. It provides a rich resource for sentiment analysis, text classification, and other NLP applications. The dataset includes the full processed corpus (train-processed.csv) and a smaller sample of 10,000 tweets (train-processed-sample.csv) for quick experimentation and model prototyping. Key Features:

1.6 million labeled tweets Binary sentiment classification (0 for negative, 1 for positive) Preprocessed and tokenized text Balanced class distribution Suitable for various NLP tasks and model architectures

Citation If you use this dataset in your research or project, please cite the original Sentiment140 dataset: Go, A., Bhayani, R. and Huang, L., 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1(2009), p.12.

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. The first 9 weeks of data (from January 1st, 2020 to March 11th, 2020) contain very low tweet counts as we filtered other data we were collecting for other research purposes, however, one can see the dramatic increase as the awareness for the virus spread. Dedicated data gathering started from March 11th to March 22nd which yielded over 4 million tweets a day.

The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (40,823,816 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (7,479,940 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the statistics-full_dataset.tsv and statistics-full_dataset-clean.tsv files.

More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter)

As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data. The need to be hydrated to be used.

Dataset Card for Twitter Dataset: Tesla

  Dataset Summary

This dataset contains all the Tweets regarding #Tesla or #tesla till 12/07/2022 (dd-mm-yyyy). It can be used for sentiment analysis research purpose or used in other NLP tasks or just for fun. It contains 10,000 recent Tweets with the user ID, the hashtags used in the Tweets, and other important features.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/twitter-dataset-tesla.

This dataset contains 653 996 tweets related to the Coronavirus topic and highlighted by hashtags such as: #COVID-19, #COVID19, #COVID, #Coronavirus, #NCoV and #Corona. The tweets' crawling period started on the 27th of February and ended on the 25th of March 2020, which is spread over four weeks.

The tweets were generated by 390 458 users from 133 different countries and were written in 61 languages. English being the most used language with almost 400k tweets, followed by Spanish with around 80k tweets.

The data is stored in as a CSV file, where each line represents a tweet. The CSV file provides information on the following fields:

Author: the user who posted the tweet

Recipient: contains the name of the user in case of a reply, otherwise it would have the same value as the previous field

Tweet: the full content of the tweet

Hashtags: the list of hashtags present in the tweet

Language: the language of the tweet

Relationship: gives information on the type of the tweet, whether it is a retweet, a reply, a tweet with a mention, etc.

Location: the country of the author of the tweet, which is unfortunately not always available

Date: the publication date of the tweet

Source: the device or platform used to send the tweet

The dataset can as well be used to construct a social graph since it includes the relations "Replies to", "Retweet", "MentionsInRetweet" and "Mentions".

This dataset consists on 5234 news events obtained from Twitter. The file tweets.csv.gz (available upon request via email to the authors) contains a CSV file, called tweets.csv, with all the tweets IDs corresponding to each event in events.csv. The format of each line of the file is the following:tweet_id, event_idWhere:tweet_id is an long number indicating the Twitter ID of the given tweet. Using the Twitter REST API it is possible to retrieve all the information about the given tweet.event_id corresponds to the event ID of the given tweet. The file events.csv.gz contains a CSV file, called events.csv with all the news events captured from Twitter since August, 2013 until June, 2014. The format of each line of the file is the following:

event_ID,date,total_keywords,total_tweets,keywords

Where:

event_ID is an integer which identifies the corresponding event. There are 5234 events, then event_ID ranges from 1 to 5234. date is the date of the event or connected component. The format is YYYY-MM-DD. total_keywords is an integer indicating how many keywords are in the event or connected component. total_tweets is an integer indicating how many tweets belongs to this event. keywords is a string containing total keywords keywords. There is a semicolon between two keywords.

The files cluster_labels.txt and time_resolutions.txt contain the cluster labels for each event and the time resolutions learned from all events, respectively.

cluster_labels.txt contains one integer number per line, from 0 to 19. In line i, the cluster label in that line corresponds to the event ID number i. time_resolutions.txt contains one floating point number per line, indicating the time resolution learned for all events, in minutes. There are 20 numbers in the file, one per line, in increasing order, with at most 13 decimal numbers after the point.

Version 48 of the dataset. Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets. The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (948,493,362 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (238,771,950 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/ More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688) As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used. This dataset will be updated bi-weekly at least with additional tweets, look at the github repo for these updates. Release: We have standardized the name of the resource to match our pre-print manuscript and to not have to update it every week.

This dataset contains ~11M tweets related to the 2019 United Kingdom General Election, published and collected between November 12, 2019, and December 12, 2019. In addition, we provide nodes and edges of the superspreader user similarity network, as described in the paper below.

Please, refer to the paper below for more details.

Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., & Tesconi, M. (2021). Coordinated Behavior on Social Media in 2019 UK General Election. In 15th International Conference on Web and Social Media. AAAI.

In detail, the dataset consists of:

• tweet-ids.csv.zip: a CSV file with the column "tweet_id," containing 11,264,820 tweet IDs related to the 2019 United Kingdom General Election, published and collected between November 12, 2019, and December 12, 2019.
• superspreader-nodes.csv.zip: a CSV file with the columns "id" and "cluster," relating to the nodes of the superspreader user similarity network mentioned in the paper.
• superspreader-edges.csv.zip: a CSV file with the columns "source," "target," and "weight," relating to the edges of the superspreader user similarity network mentioned in the paper.

Context and Methodology:

This dataset was created as part of a sentiment analysis project using enriched Twitter data. The objective was to train and test a machine learning model to automatically classify the sentiment of tweets (e.g., Positive, Negative, Neutral).
The data was generated using tweets that were sentiment-scored with a custom sentiment scorer. A machine learning pipeline was applied, including text preprocessing, feature extraction with CountVectorizer, and prediction with a HistGradientBoostingClassifier.

Technical Details:

The dataset includes five main files:

• test_predictions_full.csv – Predicted sentiment labels for the test set.

• sentiment_model.joblib – Trained machine learning model.

• count_vectorizer.joblib – Text feature extraction model (CountVectorizer).

• model_performance.txt – Evaluation metrics and performance report of the trained model.

• confusion_matrix.png – Visualization of the model’s confusion matrix.

The files follow standard naming conventions based on their purpose.
The .joblib files can be loaded into Python using the joblib and scikit-learn libraries.
The .csv,.txt, and .png files can be opened with any standard text reader, spreadsheet software, or image viewer.
Additional performance documentation is included within the model_performance.txt file.

Additional Details:

• The data was constructed to ensure reproducibility.

• No personal or sensitive information is present.

• It can be reused by researchers, data scientists, and students interested in Natural Language Processing (NLP), machine learning classification, and sentiment analysis tasks.

This collection contains close to 1M tweets from Russian government agencies, embassies and other government institutions harvested from 2017-02-13 until 2022-07-26. Tweet dates span from February 2010 to May 2022. The meta zipfile contains harvesting and seed metadata. Seeds used in this collection are listed in the meta/seeds.json file. Harvests runs are documented in meta/harvests.json (please note that harvests may have been paused for certain time periods). Data files are arranged by collection year and data has been flattened to the CSV format containing the columns created_at (tweet date), id (tweet id), user_id (twitter user id), user_screen_name (screen_name) and full_text (tweet text). 2017.csv 88 Mb 2018.csv 37 Mb 2019.csv 43 Mb 2020.csv 44 Mb 2021.csv 5 Mb 2022.csv 48 Mb If you need access to the high resolution json data containing the full contents of the tweet use twarc to hydrate from the tweet ids in the CSV files. Note that the hydrated files will include fewer tweets as it will not contain tweets that have been deleted, or tweets by accounts that have been deleted, suspended, or protected. This data was collected using Social Feed manager by George Washington University Libraries. (2016). Social Feed Manager. Zenodo.

This is the Twitter Parliamentarian Database: a database consisting of parliamentarian names, parties and twitter ids from the following countries: Austria, Belgium, France, Denmark, Spain, Finland, Germany, Greece, Italy, Malta, Poland, Netherlands, United Kingdom, Ireland, Sweden, New Zealand, Turkey, United States, Canada, Australia, Iceland, Norway, Switzerland, Luxembourg, Latvia and Slovenia. In addition, the database includes the European Parliament.The tweet ids from the politicans' tweets have been collected from September 2017 - 31 October 2019 (all_tweet_ids.csv). In compliance with Twitter's policy, we only store tweet ids, which can be re-hydrated into full tweets using existing tools. More information on how to use the database can be found in the readme.txt.It is recommended that you use the .csv files to work with the data, rather than the SQL tables. Information on the relations in the SQL database can be found in the Database codebook.pdf.Update:The tweet ids for 2021 have been added as '2021.csv'Update #2:The tweet ids for 2020 have been added as '2020.csv'The last party table has been added as 'parties_2021_04_28.csv'The last members table has been added as 'members_2021_04_28.csv'

IMPORTANT (02-Apr-2024)

Kaggle has fixed the issue with gzip files and Version 510 should now reflect properly working files

IMPORTANT (28-Mar-2024)

Please use the version 508 of the dataset, as 509 is broken. See link below of the dataset that is properly working https://www.kaggle.com/datasets/bwandowando/ukraine-russian-crisis-twitter-dataset-1-2-m-rows/versions/508

Context

The context and history of the current ongoing conflict can be found https://en.wikipedia.org/wiki/2022_Russian_invasion_of_Ukraine.

Announcement

[Jun 16] (🌇Sunset) Twitter has finally pulled the plug on all of my remaining TWITTER API accounts as part of their efforts for developers to migrate to the new API. The last tweets that I pulled was dated last Jun 14, and no more data from Jun 15 onwards. It was fun til it lasted and I hope that this dataset was able and will continue to help a lot. I'll just leave the dataset here for future download and reference. Thank you all!

[Apr 19] Two additional developer accounts have been permanently suspended, expect a lower throughtput in the next few weeks. I will pull data til they ban my last account.

[Apr 08] I woke up this morning and saw that Twitter has banned/ permanently suspended 4 of my developer accounts, I have around a few more but it is just a matter of time till all my accounts will most likely get banned as well. This was a fun project that I maintained for as long as I can. I will pull data til my last account gets banned.

[Feb 26] I've started to pull in RETWEETS again, so I am expecting a significant amount of throughput in tweets again on top of the dedicated processes that I have that gets NONRETWEETS. If you don't want RETWEETS, just filter them out.

[Feb 24] It's been a year since I started getting tweets of this conflict and had no idea that a year later this is still ongoing. Almost everyone assumed that Ukraine will crumble in a matter of days, but it is not the case. To those who have been using my dataset, i hope that I am helping all of you in one way or another. Ill do my best to maintain updating this dataset as long as I can.

[Feb 02] I seem to be getting less tweets as my crawlers are getting throttled, i used to get 2500 tweets per 15 mins but around 2-3 of my crawlers are getting throttling limit errors. There may be some kind of update that Twitter has done about rate limits or something similar. Will try to find ways to increase the throughput again.

[Jan 02] For all new datasets, it will now be prefixed by a year, so for Jan 01, 2023, it will be 20230101_XXXX.

[Dec 28] For those looking for a cleaned version of my dataset, with the retweets removed from before Aug 08, here is a dataset by @@vbmokin https://www.kaggle.com/datasets/vbmokin/russian-invasion-ukraine-without-retweets

[Nov 19] I noticed that one of my developer accounts, which ISNT TWEETING ANYTHING and just pulling data out of twitter has been permanently banned by Twitter.com, thus the decrease of unique tweets. I will try to come up with a solution to increase my throughput and signup for a new developer account.

[Oct 19] I just noticed that this dataset is finally "GOLD", after roughly seven months since I first uploaded my gzipped csv files.

[Oct 11] Sudden spike in number of tweets revolving around most recent development(s) about the Kerch Bridge explosion and the response from Russia.

[Aug 19- IMPORTANT] I raised the missing dataset issue to Kaggle team and they confirmed it was a bug brought by a ReactJs upgrade, the conversation and details can be seen here https://www.kaggle.com/discussions/product-feedback/345915 . It has been fixed already and I've reuploaded all the gzipped files that were lost PLUS the new files that were generated AFTER the issue was identified.

[Aug 17] Seems the latest version of my dataset lost around 100+ files, good thing this dataset is versioned so one can just go back to the previous version(s) and download them. Version 188 HAS ALL THE LOST FILES, I wont be reuploading all datasets as it will be tedious and I've deleted them already in my local and I only store the latest 2-3 days.

[Aug 10] 3/5 of my Python processes errored out and resulted to around 10-12 hours of NO data gathering for those processes thus the sharp decrease of tweets for Aug 09 dataset. I've applied an exception/ error checking to prevent this from happening.

[Aug 09] Significant drop in tweets extracted, but I am now getting ORIGINAL/ NON-RETWEETS.

[Aug 08] I've noticed that I had a spike of Tweets extracted, but they are literally thousands of retweets of a single original tweet. I also noticed that my crawlers seem to deviate because of this tactic being used by some Twitter users where they flood Twitter w...

If you use the dataset, cite the paper: https://doi.org/10.1016/j.eswa.2022.117541

The most comprehensive dataset to date regarding climate change and human opinions via Twitter. It has the heftiest temporal coverage, spanning over 13 years, includes over 15 million tweets spatially distributed across the world, and provides the geolocation of most tweets. Seven dimensions of information are tied to each tweet, namely geolocation, user gender, climate change stance and sentiment, aggressiveness, deviations from historic temperature, and topic modeling, while accompanied by environmental disaster events information. These dimensions were produced by testing and evaluating a plethora of state-of-the-art machine learning algorithms and methods, both supervised and unsupervised, including BERT, RNN, LSTM, CNN, SVM, Naive Bayes, VADER, Textblob, Flair, and LDA.

The following columns are in the dataset:

➡ created_at: The timestamp of the tweet. ➡ id: The unique id of the tweet. ➡ lng: The longitude the tweet was written. ➡ lat: The latitude the tweet was written. ➡ topic: Categorization of the tweet in one of ten topics namely, seriousness of gas emissions, importance of human intervention, global stance, significance of pollution awareness events, weather extremes, impact of resource overconsumption, Donald Trump versus science, ideological positions on global warming, politics, and undefined. ➡ sentiment: A score on a continuous scale. This scale ranges from -1 to 1 with values closer to 1 being translated to positive sentiment, values closer to -1 representing a negative sentiment while values close to 0 depicting no sentiment or being neutral. ➡ stance: That is if the tweet supports the belief of man-made climate change (believer), if the tweet does not believe in man-made climate change (denier), and if the tweet neither supports nor refuses the belief of man-made climate change (neutral). ➡ gender: Whether the user that made the tweet is male, female, or undefined. ➡ temperature_avg: The temperature deviation in Celsius and relative to the January 1951-December 1980 average at the time and place the tweet was written. ➡ aggressiveness: That is if the tweet contains aggressive language or not.

Since Twitter forbids making public the text of the tweets, in order to retrieve it you need to do a process called hydrating. Tools such as Twarc or Hydrator can be used to hydrate tweets.

From conspiracy theories to fake cures and fake treatments, COVID-19 has become a hot-bed for the spread of misinformation online. It is more important than ever to identify methods to debunk and correct false information online. Detection and characterization of misinformation requires an availability of annotated datasets. Most of the published COVID-19 Twitter datasets are generic, lack annotations or labels, employ automated annotations using transfer learning or semi-supervised methods, or are not specifically designed for misinformation. Annotated datasets are either only focused on "fake news", are small in size, or have less diversity in terms of classes.

Here, we present a novel Twitter misinformation dataset called "CMU-MisCov19" with 4573 annotated tweets over 17 themes around the COVID-19 discourse. We also present our annotation codebook for the different COVID-19 themes on Twitter, along with their descriptions and examples, for the community to use for collecting further annotations. Further details related to the dataset, and our analysis based on this dataset can be found at https://arxiv.org/abs/2008.00791. In adherence to the Twitter’s terms and conditions, we do not provide the full tweet JSONs but provide a ".csv" file with the tweet IDs so that the tweets can be rehydrated. We also provide the annotations, and the date of creation for each tweet for the reproduction of the results of our analyses.

Note: If for any reason, you are not able to rehydrate all the tweets, reach out to Shahan Ali Memon at (shahan@nyu.edu).

If you use this data, please cite our paper as follows:

"Shahan Ali Memon and Kathleen M. Carley. Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset, In Proceedings of The 5th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2020), co-located with CIKM, virtual event due to COVID-19, 2020."

2020