In this dataset, you will find thousands of Facebook, Twitter, and Instagram posts from US politicians, along with their classification on the respective social media platform

This dataset comes from https://data.world/crowdflower/classification-of-pol-social.

US Election 2024 Social Media Sentiment Dataset

The US Election 2024 Social Media Sentiment Dataset captures 100 authentic, anonymized posts from X (formerly Twitter) collected during November 5-6, 2024, coinciding with the US Presidential Election's critical period. This dataset reflects real-time public opinions, emotions, and discussions surrounding the election, focusing on candidates (Donald Trump, Kamala Harris), voting processes, and media narratives. Sourced via X's official API, the data ensures compliance with platform policies and prioritizes ethical considerations by anonymizing user identities.

Dataset Features

• Size: 100 unique posts (excluding replies and quoted posts to avoid redundancy).

• Attributes:

  • Post ID: Unique identifier for each post.
  • Author: Anonymized author name (e.g., Author_#ID).
  • Username: Anonymized handle (e.g., User_#ID).
  • Timestamp: Post creation time (GMT).
  • Text: Full post content, preserved verbatim (HTML-escaped for compatibility).
  • Engagement Metrics: Likes, reposts, replies, quotes, bookmarks, and views.
  • Hashtags: Comma-separated list of extracted hashtags (e.g., #USElection2024, #VotedForTrump).
  • Media: Indicator for presence of images/videos (Yes/No).
  • Timeframe: November 5-6, 2024, covering election night and early result announcements.
  • Language: Primarily English, with potential for multilingual expansion.

Potential Applications

• Sentiment Analysis: Develop machine learning models to classify sentiments (e.g., pro-Trump, pro-Harris, neutral) using NLP tools like VADER or BERT.
• Topic Modeling: Identify key election themes (e.g., voter turnout, media bias) via techniques like LDA.
• Network Analysis: Analyze user interactions through engagement metrics to map influence networks.
• Time-Series Analysis: Track sentiment or hashtag trends over the election period.

Collection Methodology

Posts were collected using X's API with targeted queries (e.g., "#USElection2024", "Trump", "Harris" -filter:replies) and a minimum engagement filter (min_faves:1) to ensure relevance. The dataset was cleaned to remove sensitive information (e.g., full URLs where non-essential) while retaining original text for analysis. The collection focused on the latest posts to capture real-time reactions.

Ethical Considerations

• Adheres to X’s developer terms, ensuring ethical data use.
• User identities anonymized to comply with privacy standards.
• Licensed under CC0 (public domain) for open access on Kaggle.
• Potential biases include a focus on high-engagement posts and English-language content; users are encouraged to expand with broader queries.

Recommendations for Kaggle

• Include 2-3 sample Jupyter notebooks (e.g., exploratory data analysis with Pandas, sentiment visualization with Matplotlib) to enhance usability.
• Expand the dataset using similar API queries for larger scale (e.g., 10k+ posts).
• Add derived features like sentiment scores or topic labels for enriched analysis.

This dataset is a valuable resource for data scientists, political researchers, and students studying social media’s impact on the 2024 US Presidential Election. It provides a snapshot of public discourse, ideal for NLP, social network analysis, and trend detection.

US Politicians Social Media Messages

5000 Social Media Messages from US Politicians

By CrowdFlower [source]

About this dataset

This dataset provides an insightful look at thousands of social media messages from US Senators and other American politicians. Contributors studied their content to classify the messages according to audience - either national or constituent - bias, as well as its actual substance (informational, announcement of a media appearance, an attack on another candidate, etc). This dataset is a valuable tool for uncovering insight into the political dynamics in modern America by exploring topics such as partisan and neutral/bipartisan message leanings among different audiences. With 5000 rows of data points from various sources at your fingertips, you can get definitive answers about what type of reactions different types of political messages produce. Get ready for a never before seen view on the world

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to uncover political bias in social media communication among US Senators and other American politicians. The data contains 5000 social media messages, which have been classified into audience, bias and message content.

To get started with the dataset, it is important to familiarize yourself with the columns of data that can be found in the dataset. These columns include: - ‘_golden’ (Boolean): Indicates whether the row is a golden row or not - ‘_unit_state’ (String): State of the unit - ‘_trusted_judgements’ (Integer): Number of trusted judgments for this row - ‘_last_judgement’ (Timestamp): When was last judgment was made for this post? - ‘audience’ (String): Who is the intended audience for this post? National or constituency? - 'bias' (String): What type of bias does this message convey? Neutral/bipartisan or biased/partisan?
- 'orig_golden' (Boolean): Indicates whether the row is a golden row or not
- 'audience_gold'(String) : Audience type which has been set as gold standard

• 'bias gold'(String) Bias type which has been set as gold standard

• embed(String) : Embed code of post

• label( String ) : Label assigned to post based on its sentiment

• message gold( String ) : Message content which has been set as golden value

• Source( String ) : Source from where information originated from

• text( String ) : Text used in message by author
  Once you are familiar with these columns, you will then want to explore different ways in which you can analyze and utilize your data. For example, you may want to create visualizations such as heat maps that show partisan and bipartisan messages across various geographies or states, analyze usage patterns by time or day of week etc., chart changes in message tone over time at specific accounts etc. You may also want to look at trends by political parties to see if some topics are more popular than others among certain groups. And finally utilize topic modelling techniques such as LDA model etc., to determine key topics present across multiple tweets added between different accounts and analyze each group's opinion specifically on those topics

Research Ideas

• Developing an automated classification system using machine learning algorithms to accurately classifying audience, bias, and message content of social media messages.
• Identifying topics or issues that are being most talked/discussed on political social media accounts by tracking the messages with certain tags/label over time through data analysis.
• Clustering tweets based on different characteristics like user behavior and sentiment to better understand how people interact with politically-charged content on social media platforms and draw insights into the evolution of public opinion around important topics such as elections or laws changes

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: Political-media-DFE.csv | Column name | Description | |:-----------------------|:---------------------------------------------------------------------------------------------------------------------------------------------...

A dataset of social media posts (tweets, Facebook posts, etc.) along with sentiment scores (positive, negative, neutral). The data covers a variety of topics such as politics, entertainment, and health. Columns: Post ID, Date, Platform, Topic (e.g., Politics, Entertainment), Sentiment Score (1 = Positive, -1 = Negative, 0 = Neutral), Text Content.

This dataset, from Crowdflower's Data For Everyone Library, provides text of 5000 messages from politicians' social media accounts, along with human judgments about the purpose, partisanship, and audience of the messages.

How was it collected?

Contributors looked at thousands of social media messages from US Senators and other American politicians to classify their content. Messages were broken down into audience (national or the tweeter’s constituency), bias (neutral/bipartisan, or biased/partisan), and finally tagged as the actual substance of the message itself (options ranged from informational, announcement of a media appearance, an attack on another candidate, etc.)

Acknowledgments

Data was provided by the Data For Everyone Library on Crowdflower.

Our Data for Everyone library is a collection of our favorite open data jobs that have come through our platform. They're available free of charge for the community, forever.

Inspiration

Here are a couple of questions you can explore with this dataset:

• what words predict partisan v. neutral messages?
• what words predict support messages v. attack messages?
• do politicians use Twitter and Facebook for different purposes? (e.g., Twitter for attack messages, Facebook for policy messages)?

The Data

The dataset contains one file, with the following fields:

• _unit_id: a unique id for the message
• _golden: always FALSE; (presumably whether the message was in Crowdflower's gold standard)
• _unit_state: always "finalized"
• _trusted_judgments: the number of trusted human judgments that were entered for this message; an integer between 1 and 3
• _last_judgment_at: when the final judgment was collected
• audience: one of national or constituency
• audience:confidence: a measure of confidence in the audience judgment; a float between 0.5 and 1
• bias: one of neutral or partisan
• bias:confidence: a measure of confidence in the bias judgment; a float between 0.5 and 1
• message: the aim of the message. one of: -- attack: the message attacks another politician
  -- constituency: the message discusses the politician's constituency
  -- information: an informational message about news in government or the wider U.S.
  -- media: a message about interaction with the media
  -- mobilization: a message intended to mobilize supporters
  -- other: a catch-all category for messages that don't fit into the other
  -- personal: a personal message, usually expressing sympathy, support or condolences, or other personal opinions
  -- policy: a message about political policy
  -- support: a message of political support
• message:confidence: a measure of confidence in the message judgment; a float between 0.5 and 1
• orig_golden: always empty; presumably whether some portion of the message was in the gold standard
• audience_gold: always empty; presumably whether the audience response was in the gold standard
• bias_gold: always empty; presumably whether the bias response was in the gold standard
• bioid: a unique id for the politician
• embed: HTML code to embed this message
• id: unique id for the message WITHIN whichever social media site it was pulled from
• label: a string of the form "From: firstname lastname (position from state)"
• message_gold: always blank; presumably whether the message response was in the gold standard
• source: where the message was posted; one of "facebook" or "twitter"
• text: the text of the message

This dataset provides a comprehensive collection of public sentiment and discourse related to Indian politics. The entries cover a wide range of opinions, news, social media posts, and other forms of public communication. Each entry is meticulously labeled with a sentiment score, capturing the polarity of the opinion from strongly negative to strongly positive.

This dataset is structured to facilitate detailed sentiment analysis and examination of political sentiments in India.

Use Cases

This dataset is ideal for:

Sentiment Analysis: Researchers can use this dataset to train and evaluate sentiment analysis models specifically tailored to the political context in India. Trend Analysis: Analysts can track the evolution of public sentiment over time, identifying key events that influenced public opinion. Political Studies: Scholars can investigate the relationship between public sentiment and political events, figures, and policies in India. Natural Language Processing (NLP): NLP practitioners can leverage this dataset for various tasks such as text classification, opinion mining, and more.

📊 Dataset Description: Social Media Content, Engagement & Moderation:

~This dataset contains 40 social media posts collected from multiple platforms (Twitter, Facebook, Instagram, YouTube, TikTok). It provides a detailed view of how different types of content perform, how users engage with them, and how moderation systems respond.

🔑 Key Features:

~**Platform & Content:** Includes post type (Tweet, Story, Video, etc.), unique IDs, and timestamps.

~**User Information:** Follower counts and verification status.

~**Content Metadata:** Text, category, language, country, length, media type, and presence of external links.

~**Engagement Metrics:** Like, share, and comment counts, along with an overall engagement score.

Trust & Safety Signals:

~Misinformation Flag

~Fact-Check Source

~Moderation Action (e.g., Approved, Warning Label, Demonetized, Removed)

NLP & Behavioral Features:

~Sentiment Score (positive/negative tone)

~Toxicity Score (harassment/offensive likelihood)

~Political Leaning (Neutral, Liberal, Conservative, Conspiracy)

~Topic Tags (e.g., climate, vaccine, election, 5G)

~Virality Indicators: Viral score estimating likelihood of content going viral.

📌 Example Use Cases:

~**Fake News & Misinformation Research** – Train ML models to detect misinformation.

~**Content Moderation Systems** – Study how platforms label, remove, or demonetize harmful content.

~**NLP & Sentiment Analysis** – Analyze toxicity, bias, and sentiment across platforms.

~**Trend Analysis** – Compare engagement across topics (climate change, vaccines, elections, 5G).

~**Political Bias Detection** – Explore correlations between political leaning, engagement, and moderation.

📂 Dataset Size:

~40 posts

~25 features

~This dataset is a synthetic but realistic representation of social media activity. It can be useful for machine learning, data analysis, and visualization projects related to misinformation, user engagement, and platform moderation.

https://techcrunch.com/wp-content/uploads/2015/10/twitter-politics.png" alt="">

• Social media is becoming a key medium through which we communicate with each other: it is at the center of the very structures of our daily interactions. Yet this infiltration is not unique to interpersonal relations. Political leaders, governments, and states operate within this social media environment, wherein they continually address crises and institute damage control through platforms such as Twitter.

• With the proliferation of the internet into mass masses, social media is emerging as a potential way of communication. It provides a direct channel to politicians for communicating, connecting, and engaging with the public. The power of social media, especially Twitter and Facebook has been proved by its successful application during recent US presidential elections and Arabian countries' revolts. In India too, as the general election is about to knock at the door during early 2014, political parties and leaders are trying to harness the power of social media.

Content

The tweets have the #Politics hashtag. The collection started on 24/7/2021, and will be updated on a daily basis.

Information regarding the data

The data totally consists of 1 lakh+ records with 13 columns. The description of the features is given below | No |Columns | Descriptions | | -- | -- | -- | | 1 | user_name | The name of the user, as they’ve defined it. | | 2 | user_location | The user-defined location for this account’s profile. | | 3 | user_description | The user-defined UTF-8 string describing their account. | | 4 | user_created | Time and date, when the account was created. | | 5 | user_followers | The number of followers an account currently has. | | 6 | user_friends | The number of friends an account currently has. | | 7 | user_favourites | The number of favorites an account currently has | | 8 | user_verified | When true, indicates that the user has a verified account | | 9 | date | UTC time and date when the Tweet was created | | 10 | text | The actual UTF-8 text of the Tweet | | 11 | hashtags | All the other hashtags posted in the tweet along with #Politics | | 12 | source | Utility used to post the Tweet, Tweets from the Twitter website have a source value - web | | 13 | is_retweet | Indicates whether this Tweet has been Retweeted by the authenticating user. |

Inspiration

You can use this data to dive into the subjects that use this hashtag, look to the geographical distribution, evaluate sentiments, and look at trends.

• all_musk_posts.csv - Elon Musk's tweets from his official account (@elonmusk) from the very beginning till April 13, 2025.
• musk_quote_tweets.csv - the original tweets that Elon Musk quote-tweeted to his official account (@elonmusk) from the very beginning till April 13, 2025.

I scraped Elon Musk's tweets and combined it with other datasets published on Kaggle in different years: - All Elon Musk's Tweets - tweets from Bill Gates, Elon Musk and Ed Lee - Elon Musk Tweets, 2010 to 2017 - Elon Musk Tweets (2021-2023)

The business magnate Elon Musk initiated an acquisition of the American social media company Twitter, Inc. on April 14, 2022, and concluded it on October 27, 2022. Musk had begun buying shares of the company in January 2022, becoming its largest shareholder by April with a 9.1 percent ownership stake. (Wikipedia)

By early 2024, Musk had become a vocal and financial supporter of Donald Trump. (Washington Post)

The data was collected and combined for the publication Poster boy: Six instances of Kremlin disinformation amplified through Elon Musk’s social network (The Insider, 2025-03-12). Below are two visualisations based of this data.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4728018%2F698e901a8dec9a84d7d5d5799427da42%2Ffile-efae4a0f8b8c46becfa2a845a8b6ac17.jpg?generation=1742660891320296&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4728018%2Ff98f3e2b42d9201cee3a37d1d3b5fa24%2Ffile-e0d66dd19064cc2c1ff91eeb34ee8157.jpg?generation=1742660914839024&alt=media" alt="">

Content of the dataset all_musk_posts.csv:

id - ID of the tweet by elonmusk

url - link to a tweet (on x.com)

twitterUrl - link to a tweet (on twitter.com)

fullText - text of the tweet

retweetCount - number of retweets

replyCount - number of replies

likeCount - number of likes

quoteCount - number of quotes

viewCount - number of views

createdAt - timestamp, UTC

bookmarkCount - number of bookmarks

isReply - boolean, True if the post is a reply

inReplyToId - ID of the original tweet if that's a reply

conversationId - conversation ID

inReplyToUserId - ID of the user that received a reply

inReplyToUsername - current username of the user that received a reply

isPinned - boolean, True if the post was pinned

isRetweet - boolean, True if the post is a retweet

isQuote - boolean, True if the post is a quote

isConversationControlled - conversation marked as "controlled", only selected users can reply

possiblySensitive - conversation marked as "sensitive"

Content of the dataset musk_quote_tweets.csv:

orig_tweet_id - ID of the original tweet by that @elonmusk quote-tweeted

orig_tweet_created_at - timestamp of the original tweet, UTC

orig_tweet_text - text of the original tweet, UTC

orig_tweet_url - link to the original tweet (on x.com)

orig_tweet_twitter_url - link to the original tweet (on twitter.com)

orig_tweet_username - current (March 2025) username of the account that posted the original tweet

orig_tweet_retweet_count - number of retweets for the original tweet

orig_tweet_reply_count - number of replies for the original tweet

orig_tweet_like_count - number of likes for the original tweet

orig_tweet_quote_count - number of quotes for the original tweet

orig_tweet_view_count - number of views for the original tweet

orig_tweet_bookmark_count - number of bookmarks for the original tweet

musk_tweet_id - ID of the quote-tweet by elonmusk

musk_quote_tweet - text of the quote-tweet by elonmusk

musk_quote_retweet_count - number of retweets for the quote-tweet by elonmusk

musk_quote_reply_count - number of replies for the quote-tweet by elonmusk

musk_quote_like_count- number of likes for the quote-tweet by elonmusk

musk_quote_quote_count- number of quotes for the quote-tweet by elonmusk

musk_quote_view_count - number of views for the quote-tweet by elonmusk

musk_quote_bookmark_count - number of bookmarks for the quote-tweet by elonmusk

musk_quote_created_at - timestamp of the quote-tweet by elonmusk, UTC

Acknowledgements

I do not own this data however I scraped this data for educational purposes ONLY. Please do not violate any...

Overview:

This dataset contains posts and interactions from Donald J. Trump's Truth Social account, specifically during his 2024 U.S. Presidential election campaign. Each post entry provides detailed information, including the post content, number of replies, shares, likes, and metadata such as post date, media URLs (if available), and account details. The data offers a rich source for analyzing political messaging, engagement metrics, and audience reactions during the campaign period.

Use Cases:

• Sentiment Analysis: The dataset can be used to analyze public sentiment toward Trump's campaign, identifying patterns in positive or negative reactions to different posts.
• Political Messaging Analysis: Researchers can study the nature of Trump's political communication strategies, including the themes and issues emphasized during the 2024 campaign.
• Engagement Metrics: By analyzing the number of likes, replies, and shares, this dataset allows for a detailed understanding of public engagement with Trump's posts over time.
• Media Influence Study: With data on video and image URLs, this dataset could be used to assess the impact of multimedia on audience reactions and interaction.

Source:

The posts are sourced directly from Trump's official Truth Social profile, capturing interactions that are publicly available.

Limitations:

The dataset may not include every post or interaction due to scraping limitations, and some interactions might lack context or additional details that could affect interpretability.

License:

This dataset is intended for research and analysis purposes. Please ensure that any use of the data complies with Truth Social's terms of service and applicable copyright laws.

This dataset contains 1,500 of political misinformation analyzed for the presence and effect of metaphor types on belief formation and emotional response. The data were collected across various media sources including social media posts, political advertisements, and news articles. Each record includes information on metaphor type (e.g., fear-based, nationalistic, artistic, cognitive), the political topic, emotional tone, and user engagement metrics. The dataset is complemented by responses from 547 participants, providing scores for belief acceptance and emotional intensity.

This dataset contains information about online content that has been fact-checked for accuracy. Each entry includes the title of the post, an assessment of its truthfulness (either true, false, or in between), a link to the post, and the date on which propaganda related to the post was first identified. The dataset covers a span of time from 2008 to 2022, and includes a wide variety of posts from different sources and on different topics.

This dataset could be used by researchers or analysts interested in evaluating the prevalence of misinformation online, tracking the spread of propaganda, or investigating the accuracy of specific types of content. The fact-checked posts provide a valuable resource for identifying trends in online content and assessing the reliability of information circulating on social media platforms and other online forums. The inclusion of propaganda start dates can also help to provide context and trace the origins of misleading or false information.

TigerDroppings.com, founded in 2001 by LSU alumnus Brian Fiegel, is among the most notable and active of any college sports forums on the internet, and its popularity hasn’t declined even as major social media platforms have come to dominate online spaces for discussion. The site’s userbase consists primarily of Louisiana residents and LSU graduates, though fans of other schools in the Southeastern Conference also frequent the site. The users on these sites fit a very specific demographic and they have little diversity. In a survey of users from numerous college sports forums, including TigerDroppings, it was found that 87% of users were male and 90% were white. Additionally, 76% had at least an undergraduate degree and 42% of users had a household income of $100,000 or greater. In November 2015, TigerDroppings had 129,244 users and now, seven years later, has 256,692 registered users; the site is still growing as fast as it did in the 2000s. There is a reason for this — these very specific demographics of the userbase are able to communicate in a way they otherwise couldn’t on Facebook or Twitter. Given these demographics, the forum takes on an overwhelmingly conservative tone in the opinions and sentiments regularly expressed. To put it simply, I couldn’t imagine a dataset that better encapsulates the psyche and mindset of white conservative men in Louisiana. Comprising almost 14 million political posts from 2014 to the present, it profiles the rise of Trumpism and the cataclysmic shifts seen in American politics in recent years.

Early in the site’s history, their off-topic board the “OT Lounge” was created and is the most popular board on the site, followed closely by their “Politics” board. Unlike many other similar forums, TigerDroppings relies solely on advertising to generate revenue, and all boards are free to view and create posts on. Only an email is required to sign up and all posts are anonymous; users are only outwardly identifiable by their chosen screennames. The functionality of the site has largely gone unchanged since its founding. Users can start a thread on a particular board and replies by other users are appended to the thread; there is no visible hierarchy to replies on threads, unlike platforms like Reddit, and it is very rudimentary by current standards. On every single reply in a thread there is an upvote and a downvote button; next to each button, their respective values are displayed, publicly showing the popularity of a user’s post. Users have been informally voting on political opinions and sentiments constantly, which I believe is rich for analyzing the rise of specific attitudes and rhetoric used among this demographic.

Attached to each post in the dataset are several pieces of metadata: upvotes, downvotes, username, date of post, date of thread creation, URLs from links contained in the post, URLs to images in the post, text from blockquotes, and the position of the post in its respective thread. Additionally, I was able to gather emails and phone numbers for approx. 3,000 users of the site through the Ticket Marketplace Board, as many users had posted contact info to interact with other users externally. Data from the OT-Lounge was able to be scrapped in its entirety from 2014 to present, though among data from the Politics Board there were some gaps. All data from 2015 was not publicly accessible for unclear reasons. But more interestingly, all threads from November 2, 2020 to January 7, 2021 — the day before the presidential election until the day after the insurrection at the U.S. Capitol — is not publicly accessible at all. I hypothesize there was significant activity talking about election fraud during that period, along with potentially incriminating information about posters who may have participated in the events on January 6th.

In terms of where I want to go from here with this dataset, I am interested in exploring if a model to isolate and predict political trends among this demographic could be feasible, along with exploring what potential uses it has as a tool for electoral politics in Louisiana. If anything, I want to do some anthropological research about this demographic that so clearly to me describes the social, cultural, and political environment I was raised in.

Dataset Overview

This dataset captures election-related discussions on TruthSocial in the lead-up to the 2024 U.S. presidential election. With 1.5 million posts spanning from February 2022 to October 2024, this dataset provides insights into political discourse, community formation, and the spread of information on a prominent alt-tech platform.

Context

TruthSocial, a platform focused on free speech, has attracted users with diverse political views, often leaning conservative. This dataset is ideal for researchers, data scientists, and political analysts interested in studying communication patterns, engagement trends, and sentiment on election-related topics in a less-moderated social media environment.

Usage Notes

This dataset can be utilized for:

• Trend Analysis: Study how certain election-related keywords and hashtags gained traction over time.
• Sentiment and Engagement Analysis: Measure public sentiment and engagement metrics (likes, replies, re-shares) across various posts.
• Community Analysis: Explore patterns in user engagement and community formation.

License

This dataset is intended for research purposes and should be cited appropriately if used in published work.

Techsalerator’s Location Sentiment Data for the United States of America

Techsalerator’s Location Sentiment Data for the United States offers a comprehensive dataset crucial for businesses, researchers, and technology developers. This dataset provides deep insights into location-based sentiment patterns, helping users understand regional and local variations in public opinion across different areas in the U.S.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for the United States

Techsalerator’s Location Sentiment Data for the United States provides structured sentiment analysis across urban, suburban, and rural areas. This dataset is essential for AI development, market research, political analysis, and social studies.

Top 5 Key Data Fields

• Location of Sentiment – Identifies the geographic location where the sentiment was recorded, helping researchers analyze sentiment variations across regions.
• Sentiment Score – Measures the positive or negative sentiment expressed in a specific location, useful for gauging public opinion on various topics.
• Time of Sentiment – Records the exact time and date when the sentiment was captured, helping to track trends over time, such as during elections or major events.
• Sentiment Source – Categorizes the source of sentiment data, including social media posts, customer reviews, surveys, and news articles.
• Sentiment Context – Provides insights into the context surrounding the sentiment, helping to understand the cause behind positive or negative responses (e.g., political events, economic factors).

Top 5 Location Sentiment Trends in the United States

• Political Sentiment Shifts – Election years show significant changes in sentiment, with variations across states, influencing campaign strategies and policy decisions.
• Economic Influence on Sentiment – Economic downturns or booms can significantly affect sentiment, particularly in regions reliant on specific industries like agriculture or manufacturing.
• Urban vs. Rural Sentiment Differences – Sentiment trends often differ between urban and rural areas, with urban centers focusing on issues like housing, healthcare, and public services, while rural areas tend to emphasize agricultural policies and infrastructure.
• Impact of Social Movements – Events like protests, social justice movements, and activism impact sentiment, with regional differences reflecting local engagement and issues.
• Disaster-Related Sentiment – Natural disasters and their aftermath, such as hurricanes, wildfires, and floods, lead to changes in public sentiment, influencing recovery and support strategies.

Top 5 Applications of Location Sentiment Data in the United States

• Market Research – Businesses use sentiment data to assess regional customer perceptions, helping to tailor marketing strategies for different areas.
• Political Campaigns – Political analysts and candidates use sentiment data to gauge public opinion, identify key issues, and shape campaign messages.
• Crisis Management – Sentiment analysis helps organizations understand public sentiment during crises (e.g., natural disasters, pandemics) and tailor responses accordingly.
• Urban Planning – Local governments use sentiment data to identify public concerns and priorities, guiding urban development and policy-making.
• Consumer Behavior Analytics – Retailers and service providers leverage sentiment data to track customer feedback and adjust products or services based on regional preferences.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for the United States, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Location of Sentiment
• Sentiment Score
• Time of Sentiment
• Sentiment Source
• Sentiment Context
• Sentiment Type (Positive, Negative, Neutral)
• Demographic Information (Age, Gender, etc.)
• Topic Categorization (Political, Economic, Social, etc.)
• Sentiment Trends Over Time
• Contact Information

For detailed insights into location-based sentiment patterns across the United States, Techsalerator’s dataset is an invaluable resource for researchers, marketers, political analysts, and urban planners.

Context

During the 2016 US presidential election, the phrase “fake news” found its way to the forefront in news articles, tweets, and fiery online debates the world over after misleading and untrue stories proliferated rapidly. BuzzFeed News analyzed over 1,000 stories from hyperpartisan political Facebook pages selected from the right, left, and mainstream media to determine the nature and popularity of false or misleading information they shared.

Content

This dataset supports the original story “Hyperpartisan Facebook Pages Are Publishing False And Misleading Information At An Alarming Rate” published October 20th, 2016. Here are more details on the methodology used for collecting and labeling the dataset (reproduced from the story):

More on Our Methodology and Data Limitations

“Each of our raters was given a rotating selection of pages from each category on different days. In some cases, we found that pages would repost the same link or video within 24 hours, which caused Facebook to assign it the same URL. When this occurred, we did not log or rate the repeat post and instead kept the original date and rating. Each rater was given the same guide for how to review posts:

• “*Mostly True*: The post and any related link or image are based on factual information and portray it accurately. This lets them interpret the event/info in their own way, so long as they do not misrepresent events, numbers, quotes, reactions, etc., or make information up. This rating does not allow for unsupported speculation or claims.

• “*Mixture of True and False*: Some elements of the information are factually accurate, but some elements or claims are not. This rating should be used when speculation or unfounded claims are mixed with real events, numbers, quotes, etc., or when the headline of the link being shared makes a false claim but the text of the story is largely accurate. It should also only be used when the unsupported or false information is roughly equal to the accurate information in the post or link. Finally, use this rating for news articles that are based on unconfirmed information.

• “*Mostly False*: Most or all of the information in the post or in the link being shared is inaccurate. This should also be used when the central claim being made is false.

• “*No Factual Content*: This rating is used for posts that are pure opinion, comics, satire, or any other posts that do not make a factual claim. This is also the category to use for posts that are of the “Like this if you think...” variety.

“In gathering the Facebook engagement data, the API did not return results for some posts. It did not return reaction count data for two posts, and two posts also did not return comment count data. There were 70 posts for which the API did not return share count data. We also used CrowdTangle's API to check that we had entered all posts from all nine pages on the assigned days. In some cases, the API returned URLs that were no longer active. We were unable to rate these posts and are unsure if they were subsequently removed by the pages or if the URLs were returned in error.”

Acknowledgements

This dataset was originally published on GitHub by BuzzFeed News here: https://github.com/BuzzFeedNews/2016-10-facebook-fact-check

Inspiration

Here are some ideas for exploring the hyperpartisan echo chambers on Facebook:

• How do left, mainstream, and right categories of Facebook pages differ in the stories they share?

• Which types of stories receive the most engagement from their Facebook followers? Are videos or links more effective for engagement?

• Can you replicate BuzzFeed’s findings that “the least accurate pages generated some of the highest numbers of shares, reactions, and comments on Facebook”?

Start a new kernel

To use this dataset on your research paper use the following reference.

@artical{s13102024ijcatr13101005,
Title = "Comparing Political Inclination Classification on Twitter Posts using Naive Bayes, SVM, and XGBoost",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "13",
Issue ="10",
Pages ="62 - 65",
Year = "2024",
Authors ="Shashank Shree Neupane, Atish Shakya, Bishan Rokka, Sagar Acharya"}

The details of the article is:

International Journal of Computer Applications Technology and Research Volume 13–Issue 10, 62 – 65, 2024, ISSN:-2319–8656 DOI:10.7753/IJCATR1310.1005

The link to article: https://ijcat.com/archieve/volume13/issue10/ijcatr13101005

The dataset contains the twitter post of nepali political leader who are on political parties. The dataset can be used to know the inclination of people towards a political party with their post on the social media such as X (formerly twitter).

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 | |:-------------------|:-----------------------------------------------------------------------------------------------------------------------| ...

Source

This folder contains network data for relationships between President Donald Trump and other people, which was originally compiled by John Templon, Anthony Cormier, Alex Campbell, and Jeremy Singer-Vine as part of a larger project of mapping "TrumpWorld" for BuzzFeed News.

The full dataset, which you can access as a Google Sheet or on GitHub also includes information about organizations and agencies. The data was compiled by culling from "public records, news reports, and other sources on the Trump family, his Cabinet picks, and top advisers," as well as via crowdsourced tips and information from the public (if you have any more, you can contribute them here).

Data & Methodology

Nodes: 303; unimodal

Edges: 366; unweighted; undirected

The nodes csv contains 303 different people, and the edges csv contains 366 unweighted relationships between those people, such as friendship, business partner, donor, parent/child, cabinet member, spouse, and more. This information about the relationships, along with the sources from which they were identified, is included as a column (and potential attribute) in the edges csv.

As stated above, the data was compiled by culling from "public records, news reports, and other sources on the Trump family, his Cabinet picks, and top advisers," as well as via crowdsourced tips.

*In order to make this data more accessible for basic social network analysis, I have condensed the network to only the top 303 most connected people in TrumpWorld.

Background & Significance

The relationships between President Donald Trump and other people lend themselves well to basic social network analysis because, as Templon, Cormier, Campbell, and Singer-Vine suggest, "No American president has taken office with a giant network of businesses, investments, and corporate connections like that amassed by Donald J. Trump."

Further, the social network analysis of some of the most powerful people in the nation and the world might help contribute to what Lauren Klein called for at the end of her 2018 MLA talk "Distant Reading After Moretti"--that is, computational analysis "trained on power."

Acknowlegement

Foto von Marco Zuppone auf Unsplash

Dataset

This dataset was created by Anjay23

Released under CC0: Public Domain

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