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 Pro

Contents

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.

Dataset published by Kaggle user Gabriel Preda. Collection using the Python package Tweepy on COVID-19 Vaccine related tweets from 2020. The dataset was updated daily (twice a day) up until January 2022. The initial dataset only scraped tweets relating to the Pfizer/BioNTech vaccine. The dataset was later updated to include tweets relating to additional vaccines such as Sinopharm, Sinovac, Moderna, Oxford/AstraZeneca, Covaxin, and Sputnik V vaccines.

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

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

Context

Collected recent tweets about the COVID-19 vaccines used in the entire world on large scale, as follows:

• Pfizer/BioNTech;
• Sinopharm;
• Sinovac;
• Moderna;
• Oxford/AstraZeneca;
• Covaxin;
• Sputnik V.

Content

Starting with the step of loading the data using pandas, some basic data frame operations allow us to see that, for each tweet, all of the following information is available:

information about the user who tweeted

• user_name: Twitter handle
• user_location: where in the world the person tweets from (NOTE: there is no validation here… “your bed” is technically acceptable)
• user_description: user-written biography
• user_created: when they created their Twitter account
• user_followers: number of followers
• user_friends: number of accounts the user is following
• user_favourites: number of tweets the user has liked
• user_verified: indicates if the user is a well-known figure (boolean)

information about the tweet itself

• id: indexing value for Twitter API
• date: a DateTime object in the form of YYYY-MM-DD HH:MM:SS
• text: the tweet itself (MOST IMPORTANT)
• hashtags: list of hashtags used in the tweet (without ‘#’ character)
• source: which device was used for the tweet
• retweets: number of retweets received at the time the data was collected
• favorites: number of likes received at the time the data was collected
• is_retweet: indicates if the tweet is original or a retweet (boolean)

Data collection

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

If you find this dataset useful, please consider upvoting ❤️

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