Overview

Monitoring political discussion sentiment patterns, candidate perception analysis, voter engagement level tracking, and electoral issue community reaction signal aggregation across platforms

Dataset Contents

• Rows: 6,923
• Columns: 341
• Platforms: theguardian, reddit, mastodon, bluesky, 4chan, 8kun, something_awful, locals, minds, lemmy, substack, medium, telegram, bitchute, odysee, flipboard, wikipedia, wikidata, metafilter, hackernews, lobsters, youtube
• Snapshot period: 2007-08-22 → 2026-09-06

Files

• election-discourse-and-voter-sentiment.csv.gz — 4,736,967 bytes
• election-discourse-and-voter-sentiment.json.gz — 5,781,310 bytes
• election-discourse-and-voter-sentiment.parquet — 7,721,775 bytes
• election-discourse-and-voter-sentiment_model-card.json — 9,995 bytes
• election-discourse-and-voter-sentiment_platforms.json — 945 bytes

Schema

Column	Type	Description
id	id	Unique identifier of the post.
platform	string	Source platform the post was collected from.
author	string	Author or handle that published the post.
url	url	Canonical URL of the post.
published_at	datetime	UTC timestamp the post was published (ISO-8601).
created_at	datetime	UTC timestamp the post was created (ISO-8601).
first_seen	datetime	UTC timestamp the post was first observed (ISO-8601).
last_updated	datetime	UTC timestamp the post record was last updated (ISO-8601).
title	string	Title of the post where the platform has one.
content	string	Full text content of the post.
original_content	string	Original text before any translation.
translated_from	string	Source language a translated post was produced from.
media_type	string	Type of media attached to the post.
language_detected	boolean	Language detected for the post content.
likes	integer	Number of likes the post received.
comments	integer	Number of comments on the post.
shares	integer	Number of shares/reposts of the post.
views	integer	Number of views the post received.
saves	integer	Number of times the post was saved or bookmarked.
reactions	string	Reaction breakdown for the post (JSON object).
engagement_rate	decimal	Engagement rate computed from interactions divided by reach.
score	integer	Platform-native score for the post (e.g. Reddit score).
upvote_ratio	decimal	Ratio of upvotes to total votes.
author_followers	integer	Follower count of the post author.
hashtags	string	Hashtags used in the post (JSON array).
mentions	string	Accounts mentioned in the post (JSON array).
tickers	string	Stock tickers mentioned in the post (JSON array).
ticker_count	integer	Number of distinct stock tickers mentioned.
ticker_mentions	string	Ticker mention counts keyed by ticker (JSON object).
subreddit	string	Subreddit the post was collected from.
domain	string	Domain of the linked source URL.
duration_seconds	integer	Duration of the attached media in seconds.
category	string	Platform-native category label.
is_self	boolean	Whether the post is a self text post.
board	string	Dataset column: board.
thread_id	id	Dataset column: thread_id.
over_18	boolean	Whether the post is marked as mature/NSFW by the platform.
stickied	boolean	Dataset column: stickied.
locked	boolean	Dataset column: locked.
is_fork	boolean	Binary flag: is_fork.
…	…	301 more columns — full schema in the metadata JSON

Methodology & Provenance

Posts were collected from publicly accessible platform surfaces using the query terms and platforms listed above. All enrichment signals (sentiment, emotions, sarcasm, topics, financial signals, quality scores) are produced by deterministic rule-based classifiers — no external model API is called — so identical input always yields identical output. Content is stored as collected; user-generated content remains the property of its original authors.

About Social Intel

This dataset is published by Social Intel — a research platform that collects publicly accessible conversations from 100+ social media surfaces and enriches every post with deterministic signals: sentiment, emotions, sarcasm, topic tags, financial indicators and content-quality scores.

Looking for more?

• Custom dataset requests — need posts about a specific topic, platform or vertical? Tell us your queries and we will collect exactly that data for you: Request a custom dataset
• Pro datasets — full-history exports with 10,000+ records, every enrichment signal, and fre...

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.

This dataset contains a curated collection of Donald Trump’s Truth Social posts related to Iran, military escalation, and Middle East conflict rhetoric, along with the associated comment data collected for those posts.

The dataset is structured in two linked tables:

• posts.csv: source posts, engagement metrics, media flags, and link-card metadata
• comments.csv: replies to those posts, including text, engagement metrics, account-level metadata, and media/link-card indicators

The main purpose of this dataset is to support research and exploratory analysis of:

• public reactions to conflict-related political messaging
• pro-war vs anti-war response patterns
• religious, conspiratorial, celebratory, and media-amplifying rhetoric in replies
• engagement dynamics across posts and comments
• multimodal reactions such as text, image, video, and link-card replies

Files

posts.csv Contains post-level metadata such as post ID, timestamp, cleaned text, engagement counts, media indicators, account fields, and link-card information.

comments.csv Contains comment-level metadata such as comment ID, parent post ID, cleaned text, engagement counts, commenter account fields, media indicators, and link-card information.

Notes

• This dataset is event-centered and focuses specifically on war/conflict-related posts rather than all Truth Social activity.
• The current release is primarily a structured raw dataset and does not include manual stance annotations.
• Some comments may be text-only, while others may contain only media, videos, or external news/link cards.

Suggested use cases

• exploratory data analysis
• sentiment and stance analysis
• conflict discourse analysis
• media/reaction type classification
• political communication research
• multimodal social media response analysis

Overview

Tracking policy proposal analysis discussions, civic participation advocacy threads, government reform debate engagement, and democratic engagement community signal monitoring activity

Dataset Contents

• Rows: 4,923
• Columns: 338
• Platforms: theguardian, reddit, mastodon, bluesky, 4chan, 8kun, something_awful, locals, minds, lemmy, substack, medium, telegram, bitchute, odysee, flipboard, wikipedia, wikidata, metafilter, hackernews, lobsters, youtube
• Snapshot period: 2011-06 → 2026-09

Files

• policy-debate-and-civic-engagement.csv.gz — 2,637,013 bytes
• policy-debate-and-civic-engagement.json.gz — 3,280,223 bytes
• policy-debate-and-civic-engagement_model-card.json — 9,874 bytes
• policy-debate-and-civic-engagement_platforms.json — 816 bytes

Schema

Column	Type	Description
id	id	Unique identifier of the post.
platform	string	Source platform the post was collected from.
author	string	Author or handle that published the post.
url	url	Canonical URL of the post.
published_at	datetime	UTC timestamp the post was published (ISO-8601).
created_at	datetime	UTC timestamp the post was created (ISO-8601).
first_seen	datetime	UTC timestamp the post was first observed (ISO-8601).
last_updated	datetime	UTC timestamp the post record was last updated (ISO-8601).
title	string	Title of the post where the platform has one.
content	string	Full text content of the post.
media_type	string	Type of media attached to the post.
language_detected	boolean	Language detected for the post content.
likes	integer	Number of likes the post received.
comments	integer	Number of comments on the post.
shares	integer	Number of shares/reposts of the post.
views	integer	Number of views the post received.
saves	integer	Number of times the post was saved or bookmarked.
reactions	string	Reaction breakdown for the post (JSON object).
engagement_rate	decimal	Engagement rate computed from interactions divided by reach.
score	integer	Platform-native score for the post (e.g. Reddit score).
upvote_ratio	decimal	Ratio of upvotes to total votes.
author_followers	integer	Follower count of the post author.
hashtags	string	Hashtags used in the post (JSON array).
mentions	string	Accounts mentioned in the post (JSON array).
tickers	string	Stock tickers mentioned in the post (JSON array).
ticker_count	integer	Number of distinct stock tickers mentioned.
ticker_mentions	string	Ticker mention counts keyed by ticker (JSON object).
subreddit	string	Subreddit the post was collected from.
domain	string	Domain of the linked source URL.
duration_seconds	integer	Duration of the attached media in seconds.
category	string	Platform-native category label.
is_self	boolean	Whether the post is a self text post.
board	string	Dataset column: board.
thread_id	id	Dataset column: thread_id.
over_18	boolean	Whether the post is marked as mature/NSFW by the platform.
stickied	boolean	Dataset column: stickied.
locked	boolean	Dataset column: locked.
is_fork	boolean	Binary flag: is_fork.
archived	boolean	Dataset column: archived.
language_github	string	Dataset column: language_github.
…	…	298 more columns — full schema in the metadata JSON

Methodology & Provenance

Posts were collected from publicly accessible platform surfaces using the query terms and platforms listed above. All enrichment signals (sentiment, emotions, sarcasm, topics, financial signals, quality scores) are produced by deterministic rule-based classifiers — no external model API is called — so identical input always yields identical output. Content is stored as collected; user-generated content remains the property of its original authors.

About Social Intel

This dataset is published by Social Intel — a research platform that collects publicly accessible conversations from 100+ social media surfaces and enriches every post with deterministic signals: sentiment, emotions, sarcasm, topic tags, financial indicators and content-quality scores.

Looking for more?

• Custom dataset requests — need posts about a specific topic, platform or vertical? Tell us your queries and we will collect exactly that data for you: Request a custom dataset
• Pro datasets — full-history exports with 10,000+ records, every enrichment signal, and fresh weekly updates: Browse pro datasets

License

• Social Intel Commercial v1 — License terms

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.

Trump Truth Social Posts Dataset (Second Presidency)

Overview

This dataset contains posts retrieved from President Donald J. Trump’s official Truth Social account during his second presidency period (2025–2026). The collection includes original Truth Social posts focused on political events, economic policy, geopolitics, trade relations, elections, national security, and public communications.

The dataset was compiled for research and analytical purposes, including:

• natural language processing (NLP)
• political communication analysis
• sentiment analysis
• financial market research
• geopolitical risk studies
• event-driven forecasting

Dataset Contents

The dataset includes:

• post text/content
• posting timestamps/dates
• cleaned textual data
• metadata fields (depending on extraction source)

Posts cover a wide range of topics including:

• tariffs and trade negotiations
• Federal Reserve commentary
• sanctions and foreign policy
• military conflicts and ceasefires
• immigration and border policy
• energy and oil policy
• elections and campaign messaging
• international diplomacy

Source

All posts were retrieved from Truth Social, the social media platform used by Donald J. Trump for official public communication.

Potential Applications

This dataset can be used for:

• NLP model training
• political sentiment analysis
• financial market event studies
• geopolitical risk analysis
• text classification
• time-series analysis
• policy communication research
• transformer and LLM fine-tuning

Disclaimer

This dataset is provided for research and educational purposes only. The dataset reflects publicly available social media posts and does not imply factual verification of statements contained within the posts.

Donald Trump is one of the most outspoken and influential global leaders in our time. Banned from Twitter in January 2021, he launched Truth Social in February 2022 as his primary social media platform, using it to comment on politics, geopolitics, media, legal battles, and personal matters with a distinctive and often polarizing voice.

As there are no other Kaggle datasets on Trump's Truth Social posts, I sourced them from Matt Stiles' github repo, licensed under CC0-1.0 (public domain), which is updated every five minutes and covers posts from February 2022 through April 2026. Symbolic NLP features were computed using VADER, spaCy, Detoxify, NRCLex, and textstat. LLM classification was performed using Gemini 2.5 Flash. See Provenance for more details

Data Dictionary

Category	Column	Description
🧾 Basic Metadata	id	Unique Truth Social post ID
	created_at	UTC timestamp of the post
	url	Direct link to the original post
	text	Cleaned post text (HTML tags removed). Null means that the post did not contain any texts
	has_text	Boolean; False for image/video-only posts
📈 Engagement	like_count	Total number of likes
	retruth_count	Total number of reposts (re-truths)
	reply_count	Total number of replies
	engagement_ratio	Likes ÷ (likes + retruths + replies)
	is_retruth	Boolean; whether the post is a repost. Detected via HTML class marker in raw content, may have occasional false negatives
⏱️ Temporal	hour_of_day	Hour the post was made (UTC)
	day_of_week	Day name (e.g., Monday)
	is_weekend	Boolean; whether post was made on a weekend
	days_since_last_post	Gap since previous post (in days)
🧠 Linguistic	word_count	Total number of words
	readability_score	Flesch-Kincaid grade level
	post_length_category	Short < 100 words, Medium 100–300 words, Long > 300 words
🔤 Writing Style	cap_ratio	Proportion of uppercase characters
	all_caps_word_count	Number of fully uppercase words
	exclamation_count	Count of "!"
	question_count	Count of "?"
	ellipsis_count	Count of "..."
🔗 Content	link_count	Number of URLs in the post
	hashtag_count	Number of hashtags used
	hashtag_list	List of hashtags used
🧍 Named Entities	mentions_person	Detected people (spaCy)
	companies_mentioned	Detected organisations
	countries_mentioned	Detected countries / locations
	politicians_mentioned	Known political figures
	media_outlets_mentioned	Media organisations mentioned
	mentions_media	Boolean; whether media is mentioned
😊 Sentiment	sentiment_score	Compound sentiment score (-1 to 1)
	sentiment_label	Positive / Neutral / Negative
	emotion	Dominant emotion (NRCLex). Possible values: joy, anger, fear, disgust, surprise, sadness, trust, anticipation
	sentiment_toward_target	Gemini-classified Sentiment toward target (pos/neg/neutral/mixed)
☣️ Toxicity	toxicity_score	Toxicity probability (0 to 1)
🏷️ Topics	topic	Classified topic (Economy, Military, Legal, Media, Immigration, Foreign Policy, Personal, Elections, Religion, Other)
	rhetorical_device	Insult, Threat, Boast, Accusation, Call to Action, Policy Statement, Praise, Denial, Other
	target_entity	Main entity being discussed (null if no specific entity is addressed)
	is_policy_related	Boolean; whether post discusses policy

Overview

Monitoring FOIA request discussion threads, government accountability advocacy campaign tracking, public records access debate engagement, and civic transparency community signal monitoring

Dataset Contents

• Rows: 4,944
• Columns: 341
• Platforms: theguardian, reddit, mastodon, bluesky, 4chan, 8kun, something_awful, locals, minds, lemmy, substack, medium, telegram, bitchute, odysee, flipboard, wikipedia, wikidata, metafilter, hackernews, lobsters, youtube
• Snapshot period: 2008-09-11 → 2026-09-11

Files

• government-transparency-and-accountability.csv.gz — 3,243,912 bytes
• government-transparency-and-accountability.json.gz — 3,968,917 bytes
• government-transparency-and-accountability_model-card.json — 9,998 bytes
• government-transparency-and-accountability_platforms.json — 888 bytes

Schema

Column	Type	Description
id	id	Unique identifier of the post.
platform	string	Source platform the post was collected from.
author	string	Author or handle that published the post.
url	url	Canonical URL of the post.
published_at	datetime	UTC timestamp the post was published (ISO-8601).
created_at	datetime	UTC timestamp the post was created (ISO-8601).
first_seen	datetime	UTC timestamp the post was first observed (ISO-8601).
last_updated	datetime	UTC timestamp the post record was last updated (ISO-8601).
title	string	Title of the post where the platform has one.
content	string	Full text content of the post.
original_content	string	Original text before any translation.
translated_from	string	Source language a translated post was produced from.
media_type	string	Type of media attached to the post.
language_detected	boolean	Language detected for the post content.
likes	integer	Number of likes the post received.
comments	integer	Number of comments on the post.
shares	integer	Number of shares/reposts of the post.
views	integer	Number of views the post received.
saves	integer	Number of times the post was saved or bookmarked.
reactions	string	Reaction breakdown for the post (JSON object).
engagement_rate	decimal	Engagement rate computed from interactions divided by reach.
score	integer	Platform-native score for the post (e.g. Reddit score).
upvote_ratio	decimal	Ratio of upvotes to total votes.
author_followers	integer	Follower count of the post author.
hashtags	string	Hashtags used in the post (JSON array).
mentions	string	Accounts mentioned in the post (JSON array).
tickers	string	Stock tickers mentioned in the post (JSON array).
ticker_count	integer	Number of distinct stock tickers mentioned.
ticker_mentions	string	Ticker mention counts keyed by ticker (JSON object).
subreddit	string	Subreddit the post was collected from.
domain	string	Domain of the linked source URL.
duration_seconds	integer	Duration of the attached media in seconds.
category	string	Platform-native category label.
is_self	boolean	Whether the post is a self text post.
board	string	Dataset column: board.
thread_id	id	Dataset column: thread_id.
over_18	boolean	Whether the post is marked as mature/NSFW by the platform.
stickied	boolean	Dataset column: stickied.
locked	boolean	Dataset column: locked.
is_fork	boolean	Binary flag: is_fork.
…	…	301 more columns — full schema in the metadata JSON

Methodology & Provenance

Posts were collected from publicly accessible platform surfaces using the query terms and platforms listed above. All enrichment signals (sentiment, emotions, sarcasm, topics, financial signals, quality scores) are produced by deterministic rule-based classifiers — no external model API is called — so identical input always yields identical output. Content is stored as collected; user-generated content remains the property of its original authors.

About Social Intel

This dataset is published by Social Intel — a research platform that collects publicly accessible conversations from 100+ social media surfaces and enriches every post with deterministic signals: sentiment, emotions, sarcasm, topic tags, financial indicators and content-quality scores.

Looking for more?

• Custom dataset requests — need posts about a specific topic, platform or vertical? Tell us your queries and we will collect exactly that data for you: Request a custom dataset
• Pro datasets — full-history exports with 10,000+ records, every enrichment signal, and fresh weekly updates: [Browse pro datasets](https://socia...

Overview

Tracking human rights violation reporting, social justice movement coordination discussions, civil liberties defense advocacy, and equality community engagement signal aggregation across platforms

Dataset Contents

• Rows: 7,287
• Columns: 341
• Platforms: theguardian, reddit, mastodon, bluesky, 4chan, 8kun, something_awful, locals, minds, lemmy, substack, medium, telegram, bitchute, odysee, flipboard, wikipedia, wikidata, metafilter, hackernews, lobsters, youtube
• Snapshot period: 2008-04-11 → 2026-09-11

Files

• human-rights-and-social-justice-discourse.csv.gz — 5,772,735 bytes
• human-rights-and-social-justice-discourse.json.gz — 6,961,900 bytes
• human-rights-and-social-justice-discourse.parquet — 9,243,993 bytes
• human-rights-and-social-justice-discourse_model-card.json — 9,989 bytes
• human-rights-and-social-justice-discourse_platforms.json — 884 bytes

Schema

Column	Type	Description
id	id	Unique identifier of the post.
platform	string	Source platform the post was collected from.
author	string	Author or handle that published the post.
url	url	Canonical URL of the post.
published_at	datetime	UTC timestamp the post was published (ISO-8601).
created_at	datetime	UTC timestamp the post was created (ISO-8601).
first_seen	datetime	UTC timestamp the post was first observed (ISO-8601).
last_updated	datetime	UTC timestamp the post record was last updated (ISO-8601).
title	string	Title of the post where the platform has one.
content	string	Full text content of the post.
original_content	string	Original text before any translation.
translated_from	string	Source language a translated post was produced from.
media_type	string	Type of media attached to the post.
language_detected	boolean	Language detected for the post content.
likes	integer	Number of likes the post received.
comments	integer	Number of comments on the post.
shares	integer	Number of shares/reposts of the post.
views	integer	Number of views the post received.
saves	integer	Number of times the post was saved or bookmarked.
reactions	string	Reaction breakdown for the post (JSON object).
engagement_rate	decimal	Engagement rate computed from interactions divided by reach.
score	integer	Platform-native score for the post (e.g. Reddit score).
upvote_ratio	decimal	Ratio of upvotes to total votes.
author_followers	integer	Follower count of the post author.
hashtags	string	Hashtags used in the post (JSON array).
mentions	string	Accounts mentioned in the post (JSON array).
tickers	string	Stock tickers mentioned in the post (JSON array).
ticker_count	integer	Number of distinct stock tickers mentioned.
ticker_mentions	string	Ticker mention counts keyed by ticker (JSON object).
subreddit	string	Subreddit the post was collected from.
domain	string	Domain of the linked source URL.
duration_seconds	integer	Duration of the attached media in seconds.
category	string	Platform-native category label.
is_self	boolean	Whether the post is a self text post.
board	string	Dataset column: board.
thread_id	id	Dataset column: thread_id.
over_18	boolean	Whether the post is marked as mature/NSFW by the platform.
stickied	boolean	Dataset column: stickied.
locked	boolean	Dataset column: locked.
is_fork	boolean	Binary flag: is_fork.
…	…	301 more columns — full schema in the metadata JSON

Methodology & Provenance

Posts were collected from publicly accessible platform surfaces using the query terms and platforms listed above. All enrichment signals (sentiment, emotions, sarcasm, topics, financial signals, quality scores) are produced by deterministic rule-based classifiers — no external model API is called — so identical input always yields identical output. Content is stored as collected; user-generated content remains the property of its original authors.

About Social Intel

This dataset is published by Social Intel — a research platform that collects publicly accessible conversations from 100+ social media surfaces and enriches every post with deterministic signals: sentiment, emotions, sarcasm, topic tags, financial indicators and content-quality scores.

Looking for more?

• Custom dataset requests — need posts about a specific topic, platform or vertical? Tell us your queries and we will collect exactly that data for you: Request a custom dataset
• Pro datasets — full-history exports with 10,000+ records, every enric...

Dataset Description

This dataset consists of 50,034 Twitter posts curated for the task of political vs non-political tweet classification. Each record contains two columns: the raw tweet text and a binary label indicating whether the tweet is political in nature.

Basic Information

• Total samples: 50,034
• Shape: (50034, 2)
• Columns:
  • tweet – textual content of the tweet
  • type – class label (1 = political, 0 = non-political)

Label Distribution

The dataset exhibits a moderate class imbalance, reflecting real-world Twitter discourse:

• Political tweets (type = 1): 27,459
• Non-political tweets (type = 0): 22,710

Data Quality Checks

The following validation steps were performed:

• Missing values: None
• Duplicate tweets: None
• Empty tweets: None
• Very short tweets (< 5 characters): None

These checks confirm that the dataset is clean and suitable for supervised learning.

Text Sanity and Content

The dataset includes a wide variety of tweet styles, such as political statements, opinions, hashtags, mentions, and informal language. Both English and Hindi (including code-mixed usage) are present, closely resembling real-world Twitter data.

Political tweets typically reference: - Political leaders
- Government actions and policies
- Elections and national issues

Non-political tweets typically include: - Casual conversations
- General information or personal content
- Hashtags without political context

Dataset Readiness

After comprehensive cleaning and validation, the dataset is:

• ✅ Free from null values and duplicates
• ✅ Representative of noisy, real-world social media text
• ✅ Suitable for training and evaluating political text classification m

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

Overview

Monitoring union organizing campaign discussions, worker rights advocacy thread engagement, labor strike solidarity sharing, and workplace protection community signal tracking activity

Dataset Contents

• Rows: 5,671
• Columns: 341
• Platforms: theguardian, reddit, mastodon, bluesky, 4chan, 8kun, something_awful, locals, minds, lemmy, substack, medium, telegram, bitchute, odysee, flipboard, wikipedia, wikidata, metafilter, hackernews, lobsters, youtube
• Snapshot period: 2008-08-13 → 2026-09-13

Files

• labour-rights-and-union-activity.csv.gz — 5,211,884 bytes
• labour-rights-and-union-activity.json.gz — 6,258,499 bytes
• labour-rights-and-union-activity.parquet — 8,377,859 bytes
• labour-rights-and-union-activity_model-card.json — 10,003 bytes
• labour-rights-and-union-activity_platforms.json — 823 bytes

Schema

Column	Type	Description
id	id	Unique identifier of the post.
platform	string	Source platform the post was collected from.
author	string	Author or handle that published the post.
url	url	Canonical URL of the post.
published_at	datetime	UTC timestamp the post was published (ISO-8601).
created_at	datetime	UTC timestamp the post was created (ISO-8601).
first_seen	datetime	UTC timestamp the post was first observed (ISO-8601).
last_updated	datetime	UTC timestamp the post record was last updated (ISO-8601).
title	string	Title of the post where the platform has one.
content	string	Full text content of the post.
original_content	string	Original text before any translation.
translated_from	string	Source language a translated post was produced from.
media_type	string	Type of media attached to the post.
language_detected	boolean	Language detected for the post content.
likes	integer	Number of likes the post received.
comments	integer	Number of comments on the post.
shares	integer	Number of shares/reposts of the post.
views	integer	Number of views the post received.
saves	integer	Number of times the post was saved or bookmarked.
reactions	string	Reaction breakdown for the post (JSON object).
engagement_rate	decimal	Engagement rate computed from interactions divided by reach.
score	integer	Platform-native score for the post (e.g. Reddit score).
upvote_ratio	decimal	Ratio of upvotes to total votes.
author_followers	integer	Follower count of the post author.
hashtags	string	Hashtags used in the post (JSON array).
mentions	string	Accounts mentioned in the post (JSON array).
tickers	string	Stock tickers mentioned in the post (JSON array).
ticker_count	integer	Number of distinct stock tickers mentioned.
ticker_mentions	string	Ticker mention counts keyed by ticker (JSON object).
subreddit	string	Subreddit the post was collected from.
domain	string	Domain of the linked source URL.
duration_seconds	integer	Duration of the attached media in seconds.
category	string	Platform-native category label.
is_self	boolean	Whether the post is a self text post.
board	string	Dataset column: board.
thread_id	id	Dataset column: thread_id.
over_18	boolean	Whether the post is marked as mature/NSFW by the platform.
stickied	boolean	Dataset column: stickied.
locked	boolean	Dataset column: locked.
is_fork	boolean	Binary flag: is_fork.
…	…	301 more columns — full schema in the metadata JSON

Methodology & Provenance

Posts were collected from publicly accessible platform surfaces using the query terms and platforms listed above. All enrichment signals (sentiment, emotions, sarcasm, topics, financial signals, quality scores) are produced by deterministic rule-based classifiers — no external model API is called — so identical input always yields identical output. Content is stored as collected; user-generated content remains the property of its original authors.

About Social Intel

This dataset is published by Social Intel — a research platform that collects publicly accessible conversations from 100+ social media surfaces and enriches every post with deterministic signals: sentiment, emotions, sarcasm, topic tags, financial indicators and content-quality scores.

Looking for more?

• Custom dataset requests — need posts about a specific topic, platform or vertical? Tell us your queries and we will collect exactly that data for you: Request a custom dataset
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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 | |:-------------------|:-----------------------------------------------------------------------------------------------------------------------| ...

Searchable public archive of @realDonaldTrump Truth Social posts with timestamps, post types, source URLs, attachment provenance, Parquet tables, a cleaned NLP subset, metadata-stripped image derivatives, and complete video occurrence metadata with transcripts/descriptions. Independent research dataset; not official or endorsed.

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.

I created this dataset as part of my Senior Thesis at Princeton University. Part of my thesis was investigating whether social media algorithms cause an increase in the spread of fake news. There is not yet much research on this topic as most social media platforms do not share exactly how their algorithm functions and when they make changes. However, Reddit is relatively more transparent about their algorithm, and on April 12, 2022, they revealed that they introduced suggested content onto user's news feeds; thus, this date is when Reddit implemented its 'algorithm'.

From a public dataset of all Reddit posts from the top 40,000 subreddits from 2005 to 2023, I specifically extracted data from r/politics from one month prior to the date Reddit implemented its algorithm, and one month after. Each of these datasets were filtered to keep just the 100 most upvoted posts. As each post consisted of a link to a news article (this is the style of all posts in r/politics), the headline and text of each article was manually collected and stored in the datasets. (A few of the sites had paywalls preventing manual data collection, leaving 96 data points from before the algorithm implementation, and 88 data points from after the algorithm implementation.)

Hope that you find these datasets useful!

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.

-**Motivation:** On various Social Media platforms, people tend to use the informal way to communicate, or write posts and comments: their local dialects. In Africa, more than 1500 dialects and languages exist. While being so diverse and rich, Arabic language and particularly Arabic dialects, are still under represented and not yet fully exploited. Arabizi is a term describing a system of writing Arabic using English characters. This term comes from two words “arabi” (Arabic) and “Engliszi” (English). Arabizi is the representation of Arabic sounds using Latin letters and numbers to replace the non existing equivalent ones. Particularly in Tunisia, this way of writing was introduced as ”Tunizi”. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11399696%2F55a8562b118fc4d877b6dc3c60c34782%2FTUNIZI%20example%20-%20Copie.jpg?generation=1703292391903003&alt=media" alt=""> Tunizi example comments and their Modern Standard Arabic (MSA) and English translations.

-**About this Dataset :** This is a large common-crawl-based Tunisian Arabizi dialectal dataset dedicated for Sentiment Analysis. The dataset consists of a total of 100k comments (about movies, politic, sport, etc.) annotated manually by Tunisian native speakers as Positive, Negative, and Neutral.

-**Value of this Data :** The authors introduced this large Tunizi dataset built for the sentiment analysis task, in order to help Tunisian and other researchers interested in the Natural Language Processing (NLP) field. This dataset can be also used for other NLP subtasks such as dialect identification, named entities recognition, etc...

	Specifications table
Subject	Natural Language Processing - NLP
Type of data	Text
Region	North Africa - Tunisia
Data format	Annotated, Analysed, Filtered Data
Data Article	Introducing A large Tunisian Arabizi Dialectal Dataset for Sentiment Analysis
Data source location	https://data.mendeley.com/datasets/9sxpkmm8xn/1

-**How the data were acquired:**
According to the article authors, because of the lack of available Tunisian dialect data (books, wikipedia, etc.), they used a Common Crawl-based dataset extracted from social media. It is collected from comments on various social networks. The chosen posts included sports, politics, comedy, TV shows, TV series, arts and Tunisian music videos such that the dataset is representative and contains different types of ages, background, writing, etc. This data does not include any confidential information since it is collected from comments on public Social Media posts. However, negative comments may include offensive or insulting content. This dataset relates directly to people from different regions, different ages and different genders. A filter was applied to ensure that only Latin based comments are included. The extracted data was preprocessed by removing links, emoji symbols, and ponctuations|

Header & Thumbnail Image : Credits @VectorStock

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).

Dataset

This dataset was created by Anjay23

Released under CC0: Public Domain

Contents

It contains the following files: