Context

We collect recent tweets about Pfizer & BioNTech vaccine.

The data is collected using tweepy Python package to access Twitter API.

Inspiration

Study the subjects of recent tweets about the vaccine made in collaboration by Pfizer and BioNTech, perform various NLP tasks on this data source.

Dataset

This dataset was created by Mahendra Ch

Contents

Context

A collection of tweets related to Covid-19 vaccines with manually annotated sentiments (negative, neutral, positive). Negative sentiment is labeled as 1, neutral as 2, and positive as 3.

Data Collection

Tweet IDs are gathered from a dataset by Gabriel Preda and hydrated to get the full tweet text. The initial dataset included tweets about Pfizer/BioNTech, Sinopharm, Sinovac (both Chinese-produced vaccines), Moderna, Oxford/Astra-Zeneca, Covaxin, and Sputnik V vaccines.

Acknowledgements

Dataset is based on scraped Tweet IDs by Gabriel Preda.

Context

I collect recent tweets about the COVID-19 vaccines used in entire world on large scale, as following: * Pfizer/BioNTech;
* Sinopharm;
* Sinovac;
* Moderna;
* Oxford/AstraZeneca;
* Covaxin;
* Sputnik V.

Data collection

The data is collected using tweepy Python package to access Twitter API. For each of the vaccine I use relevant search term (most frequently used in Twitter to refer to the respective vaccine)

Data collection frequency

Initial data was merged from tweets about Pfizer/BioNTech vaccine. I added then tweets from Sinopharm, Sinovac (both Chinese-produced vaccines), Moderna, Oxford/Astra-Zeneca, Covaxin and Sputnik V vaccines. The collection was in the first days twice a day, until I identified approximatively the new tweets quota and then collection (for all vaccines) stabilized at once a day, during morning hours (GMT).

Inspiration

You can perform multiple operations on the vaccines tweets. Here are few possible suggestions:

• Study the subjects of recent tweets about the vaccine made by various producers;
• Perform various NLP tasks on this data source (topic modelling, sentiment analysis);
• Using the COVID-19 World Vaccination Progress (where we can see the progress of the vaccinations and the countries where the vaccines are administered), you can study the relationship between the vaccination progress and the discussions in social media (from the tweets) about the vaccines.

Twitter Pfizer - AntiCovid

Dataset Description

This dataset contains tweets collected from Twitter concerning the Pfizer COVID-19 vaccine. The primary goal of this dataset is to help researchers, data scientists, and analysts understand public sentiment regarding the Pfizer vaccine. It is structured to support sentiment analysis, subjectivity analysis, and classification tasks.

Column Descriptions

1. id:
2. Description: A unique identifier for each tweet.
   • Type: Integer.

3. Purpose: Used to uniquely identify and differentiate each tweet in the dataset.

4. Text:

   • Description: The content of the tweet in textual form.
   • Type: String.
   • Purpose: Provides the actual content of each tweet. This column can be used for various natural language processing (NLP) tasks such as sentiment analysis, keyword extraction, and topic modeling.

5. Subjectivity:

   • Description: A score between 0 and 1 that represents the degree of subjectivity in the tweet. A score of 0 means the tweet is objective, while a score of 1 indicates it is highly subjective.
   • Type: Float (range: 0 to 1).
   • Purpose: Indicates how much of the tweet is based on personal opinion versus factual information. This can be useful for identifying tweets that are more opinion-driven.

6. Polarity:

   • Description: A sentiment polarity score, ranging from -1 (extremely negative) to 1 (extremely positive).
   • Type: Float (range: -1 to 1).
   • Purpose: Used to determine the emotional tone of the tweet, whether it is negative, neutral, or positive. This column is key for sentiment analysis tasks.

7. Target:

   • Description: A binary variable indicating the overall sentiment of the tweet towards the Pfizer vaccine. A value of 0 indicates a negative sentiment, and a value of 1 indicates a positive sentiment.
   • Type: Integer (0 or 1).
   • Purpose: Serves as the target label for machine learning models. It helps to classify the sentiment of the tweets as positive or negative.

Provenance

This dataset was collected from Twitter using the Twitter API. Tweets containing keywords and hashtags related to the Pfizer vaccine and COVID-19 were gathered over a specified time frame. The subjectivity and polarity scores were calculated using the TextBlob library in Python, which is widely used for natural language processing tasks.

The data has been organized to ensure compatibility with a wide range of NLP and machine learning tasks. Please note that this dataset is anonymized, and any sensitive personal information has been removed to adhere to data privacy standards.

Update Frequency

This dataset is static and will not be updated regularly. However, future versions may be released based on new tweet collections or to improve the quality of the dataset. If there are any significant updates, they will be indicated in the dataset’s metadata tab on Kaggle.

this is the dataset which contain four attribute Username Description Text Hashtags for Pfizer vaccination

This dataset contains Twitter posts containing daily updates of location-based COVID–19 vaccine-related tweets from January 2021 to August 2021.

With an existing Twitter account, we applied for Developer Access and were granted access to Twitter Academic Researcher API which allows for over 10 million tweets per month. Then, we created an application to generate the API credentials (access tokens) from Twitter. The access token was used in Python (v3.6) script to authenticate and establish a connection to the Twitter database. To get goe-tagged vaccine-related tweets, we used the python script we developed to perform a historical search (archive search) of vaccine-related keywords with place country South Africa (ZA). By goe-tagged tweets, we refer to Twitter posts with a know location. These vaccine-related keywords include but are not limited to the vaccine, anti-vaxxer, vaccination, AstraZeneca, Oxford-AstraZeneca, IChooseVaccination, VaccineToSaveSouthAfrica, JohnsonJohnson, and Pfizer. The keywords were selected from the trending topic during the period of discussion. A complete list of the keywords is shown below:

Oxford-AstraZeneca, AstraZeneca, JohnsonJohnson, Vaccine, BioNTech, anti-vaccine, jab, Vaccination, Covax, Vaccine Rollout, Sputnik, VaccineToSaveSouthAfrica, IChooseVaccination, TeachersVaccine, AstraZeneca vaccine, Pfizer, J & J, Johonson & Johnson, Moderna, VaccinesWork, VacciNation, Vaccine, Steriod, COVIDvaccine, covax, VaccineEquity, VaccineReady, Jab OR PfizerGang, Scamdemic, Plandemic, Scaredemic, COVID-19, coronavirus, SARS-CoV-2, anti-vaxxers, jab, Pfizer, BioNTech, JJ, Vaccine, JohnsonJohnson Vaccine, Vaccine Rollout, J & J, Sputnik, COVAX, CoronaVac

The preferred language of the tweet is English.

Context

Pfizer Vaccine Tweets is the motivation of gathering this datasets.

Content

This datasets include posts that contains Pfizer hashtag on Instagram. 'id' column equal to Instagram post id. 'text' column is caption of posts. 'accessibility_caption' column is generated automatic lay by Instagram's AI. 'edge_media_preview_like' number of likes. 'edge_media_to_comment_count' number of comments. zero for 'comments_disabled' column means that user allow others for commenting. 'taken_at_timestamp' = timestamp

**Dates: **Nov 15 to Dec 16, 2021 Total Records: estimated 3,000,000 **Search Query: **vaccine OR vaccinemandate OR "vaccine mandate" OR pfizer OR moderna OR mRNA Data source: Twitter public API **Notes: **Data downloaded in CSV format and unedited for research use. Collection method: Netlytic

Please cite data: Li, E Rosalie. Dec 2021. Vaccine tweets 1-30. Hoaxlines disinformation database from Novel Science. https://www.kaggle.com/hoaxlines/vaccine-tweets

A twitter dataset curated for Social Media Analysis employing NLP techniques.

Description

We searched Twitter to retrieve tweets containing specific keywords related to COVID-19 from a set of Twitter accounts and news sources. We limited the retrieved tweets to those in English, excluding retweets, and posted between February, 2020, and November, 2021.

Sample code to extract tweets

query = '(@GOVUK OR @CMO_England OR @ASTRAZENECAUK OR @UKHSA OR @DHSCgovuk OR @BBCNews OR @moderna_tx OR @NHSuk OR @BorisJohnson OR @pfizer)'
query += ' (#CovidVaccine OR #COVID19Vaccine OR vaccine OR vaccination OR vax OR moderna OR AstraZenca OR Biontech OR JNJ)'
query += ' lang:en -is:retweet -is:verified until:2021-11-30 since:2022-02-01'
tweets_list2 = []
for i,tweet in enumerate(sntwitter.TwitterSearchScraper(query).get_items()):
   if i>50000:
     break
   tweets_list2.append([tweet.date, tweet.id, tweet.content, 
             tweet.user.username,tweet.user.location, 
             tweet.likeCount, tweet.retweetCount,tweet.replyCount])

Notebooks for reference

Covid-19 Tweets: NLP Social Media Analysis

Context

Winter Storm Uri in February 2021 caused havoc across the United States and specifically to Texas involving mass power outages, water and food shortages, and dangerous weather conditions.

This dataset consists of 23K+ tweets during the crisis week. Data is filtered to mostly include the tweets from influencers (users having more than 5000 followers) however there is a small subset of tweets from other users as well.

My notebook - https://www.kaggle.com/rajsengo/eda-texas-winterstrom-2021-tweets

Acknowledgements

• https://www.kaggle.com/gpreda/pfizer-vaccine-tweets - For the inspiration
• https://github.com/dataquestio/twitter-scrape - Reference utility to scrape twitter

Inspiration

Apply NLP techniques to undestand user sentiments about the crisis management