Joe Biden's Tweets

Likes, Retweets, Shares, and Conversation Dynamics

By Twitter [source]

About this dataset

At the heart of understanding Joe Biden's successful election campaign were his effective and engaged use of social media. This dataset provides unparalleled insights into how Biden harnessed the power of Twitter to create engaging conversations, share his views on policy issues, and build positive relationships with his followers. Researchers can use this data to observe the likes, retweets, shares, and replies that Biden's posts generated over time to better understand how he connected with people. Explore this dataset to track hourly, daily and weekly activity in order to gain unique insights into how Joe Biden crafted his message using social media platforms. Analyze outlinks for discussion topics relevant for elections or even pull quoted tweets from Twitter users who engage in conversations with him. You'll be able to see first -hand just how influential Joe Biden was with regards to engaging in meaningful dialogue with individuals across America while gaining valuable insight into the powerful impact that digital communication had on this particular political race

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How to use the dataset

This dataset offers researchers, journalists and political analysts a comprehensive understanding of how former Vice President Joe Biden’s social media activity provides insight into his views and opinions on policy, foreign relationships and election dynamics.

Through this dataset, users can identify trends in the number of likes, retweets and replies that are generated by the posts from Joe Biden’s Twitter account. Along with this data users can also observe changes in the quoted Tweets, outlinks mentioned in posts as well as the URLs associated with them.

To make full use of this dataset follow these steps: 1. Begin by exploring the key columns such as content (tweet text), created_at (date/time posted), likeCount (number of likes on tweet), retweetCount (number of retweets on tweet) and replyCount (number of replies to tweet).
2. Using analytical tools explore correlations between variables such as between created_at column and other columns like quoteCount or outlinks to see if certain insights can be drawn depending upon when the post is made or not made by Joe Biden himself or a campaign staff member against variables like type & length of post, medium used etc..
3. Explore which tweets have more reach with higher engagement rates within lesser time frames using variables like retweetedTweet & quotedTweet along side other fields for more interesting insights about what kind messages work better than others for specific times & situations during campaigns. 4. Engage further with observed patterns to identify further links leading to interesting conclusions about outreach related activity during campaigning periods using analysis methods like data visualisations across time lines linking multiple tweets together + finding geographic regions where Joe Biden has most followers etc..
Finally never forget that proper application (& comparison) through hypothesis testing is essential when dealing with large datasets while correlating facts across multiple channels - especially dealing with topics related to politics involving a public figure being analyzed through their own tweets!

Research Ideas

• Analyzing the sentiment of Joe Biden's tweet text and how it changes over time.
• Tracking engagement with different topics to understand which issues are most important to him and his followers.
• Comparing tweet engagement dynamics between Joe Biden and other prominent political figures for research comparison studies

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: JoeBiden.csv | Column name | Description | |:-------------------|:-----------------------------------------------------------------------------------------------------------------------| ...

CodeThis figshare repository hosts a collection of tools and scripts for Twitter data analysis, focusing on Election Prediction using sentiment analysis and tweet processing. The repository includes four key files:twitter_data_collection.py: This Python script is designed for collecting tweets from Twitter in JSON format. It provides a robust method for gathering data from the Twitter platform.EP.ipynb: EP.ipynb" is designed for sentiment analysis and tweet processing. It features three sentiment analysis methods: VADER, BERT, and BERTweet. It includes a US states dictionary for geolocating and categorizing tweets by state, providing sentiment analysis results in both volumetric and percentage formats. Furthermore, it offers time-series analysis options, particularly on a monthly basis. It also includes a feature for filtering COVID-19-related tweets. Additionally, it conducts election analysis at both state and country levels, giving insights into public sentiment and engagement regarding political elections.Datasetbiden and trump.csv Files:The "biden.csv" and "trump.csv" files together constitute an extensive dataset of tweets related to two prominent U.S. political figures, Joe Biden and Donald Trump. These files contain detailed information about each tweet, including the following key attributes:create_date: The date the tweet was created.id: A unique identifier for each tweet.tweet_text: The actual text content of the tweet.user_id: The unique identifier for the Twitter user who posted the tweet.user_name: The name of the Twitter user.user_screen_name: The Twitter handle of the user.user_location: The location provided by the user in their Twitter profile.state (location): The U.S. state associated with the user's provided location.text_clean: The tweet text after preprocessing, making it suitable for analysis.Additionally, sentiment analysis has been applied to these tweets using two different methods:VADER Sentiment Analysis: Each tweet has been assigned a sentiment score and a sentiment category (positive, negative, or neutral) using VADER sentiment analysis. The sentiment scores are provided in the "Vader_score" column, and the sentiment categories are in the "Vader_sentiment" column.BERTweet Sentiment Analysis: The files also feature sentiment labels assigned using the BERTweet sentiment analysis method, along with associated sentiment scores. The sentiment labels can be found in the "Sentiment" column, and the cleaned sentiment labels are available in the "Sentiment_clean" column.This combined dataset offers a valuable resource for exploring sentiment trends, conducting research on public sentiment, and analyzing Twitter users' opinions related to Joe Biden and Donald Trump. Researchers, data analysts, and sentiment analysis practitioners can utilize this data for a wide range of studies and projects.This repository serves as a resource for collecting, processing, and analyzing Twitter data with a focus on sentiment analysis. It offers a range of tools and datasets to support research and experimentation in this area.