The Customer Support on Twitter dataset is a large, modern corpus of tweets and replies to aid innovation in natural language understanding and conversational models, and for study of modern customer support practices and impact.

https://i.imgur.com/nTv3Iuu.png" alt="Example Analysis - Inbound Volume for the Top 20 Brands">

Context

Natural language remains the densest encoding of human experience we have, and innovation in NLP has accelerated to power understanding of that data, but the datasets driving this innovation don't match the real language in use today. The Customer Support on Twitter dataset offers a large corpus of modern English (mostly) conversations between consumers and customer support agents on Twitter, and has three important advantages over other conversational text datasets:

• Focused - Consumers contact customer support to have a specific problem solved, and the manifold of problems to be discussed is relatively small, especially compared to unconstrained conversational datasets like the reddit Corpus.
• Natural - Consumers in this dataset come from a much broader segment than those in the Ubuntu Dialogue Corpus and have much more natural and recent use of typed text than the Cornell Movie Dialogs Corpus.
• Succinct - Twitter's brevity causes more natural responses from support agents (rather than scripted), and to-the-point descriptions of problems and solutions. Also, its convenient in allowing for a relatively low message limit size for recurrent nets.

Inspiration

The size and breadth of this dataset inspires many interesting questions:

• Can we predict company responses? Given the bounded set of subjects handled by each company, the answer seems like yes!
• Do requests get stale? How quickly do the best companies respond, compared to the worst?
• Can we learn high quality dense embeddings or representations of similarity for topical clustering?
• How does tone affect the customer support conversation? Does saying sorry help?
• Can we help companies identify new problems, or ones most affecting their customers?

Acknowledgements

Dataset built with PointScrape.

Content

The dataset is a CSV, where each row is a tweet. The different columns are described below. Every conversation included has at least one request from a consumer and at least one response from a company. Which user IDs are company user IDs can be calculated using the inbound field.

tweet_id

A unique, anonymized ID for the Tweet. Referenced by response_tweet_id and in_response_to_tweet_id.

author_id

A unique, anonymized user ID. @s in the dataset have been replaced with their associated anonymized user ID.

inbound

Whether the tweet is "inbound" to a company doing customer support on Twitter. This feature is useful when re-organizing data for training conversational models.

created_at

Date and time when the tweet was sent.

text

Tweet content. Sensitive information like phone numbers and email addresses are replaced with mask values like _email_.

response_tweet_id

IDs of tweets that are responses to this tweet, comma-separated.

in_response_to_tweet_id

ID of the tweet this tweet is in response to, if any.

Contributing

Know of other brands the dataset should include? Found something that needs to be fixed? Start a discussion, or email me directly at $FIRSTNAME@$LASTNAME.com!

Acknowledgements

A huge thank you to my friends who helped bootstrap the list of companies that do customer support on Twitter! There are many rocks that would have been left un-turned were it not for your suggestions!

Relevant Resources

• NLTK - casual_tokenize for social media text tokenizing, vader sentiment analysis for social media text
• SciKit Learn - BoW Count Vectorizer, Multinomial Naive Bayes Classifier
• Topic Modeling via Phrase detection with gensim
• facebook research - fastText text classifier

Licensing

For commercial applications and use of full dataset, please contact stuart@thoughtvector.io.

https://raw.githubusercontent.com/Masterx-AI/Project_Twitter_Sentiment_Analysis_/main/twitt.jpg" alt="">

Description:

Twitter is an online Social Media Platform where people share their their though as tweets. It is observed that some people misuse it to tweet hateful content. Twitter is trying to tackle this problem and we shall help it by creating a strong NLP based-classifier model to distinguish the negative tweets & block such tweets. Can you build a strong classifier model to predict the same?

Each row contains the text of a tweet and a sentiment label. In the training set you are provided with a word or phrase drawn from the tweet (selected_text) that encapsulates the provided sentiment.

Make sure, when parsing the CSV, to remove the beginning / ending quotes from the text field, to ensure that you don't include them in your training.

You're attempting to predict the word or phrase from the tweet that exemplifies the provided sentiment. The word or phrase should include all characters within that span (i.e. including commas, spaces, etc.)

Columns:

1. textID - unique ID for each piece of text
2. text - the text of the tweet
3. sentiment - the general sentiment of the tweet

Acknowledgement:

The dataset is download from Kaggle Competetions:
https://www.kaggle.com/c/tweet-sentiment-extraction/data?select=train.csv

Objective:

• Understand the Dataset & cleanup (if required).
• Build classification models to predict the twitter sentiments.
• Compare the evaluation metrics of vaious classification algorithms.

Dataset

This dataset was created by Rock-Lagoon

Contents

File Description

This dataset contains Twitter support conversations collected from various company accounts. It includes customer inquiries and corresponding support responses. The data is useful for training AI chatbots, analyzing customer service trends, and developing sentiment analysis models.

Column Description

This dataset contains customer support interactions on Twitter. It includes the following columns: tweet_id: A unique identifier for each tweet. author_id: The unique ID of the user who posted the tweet. inbound: A boolean value indicating whether the tweet is from a customer (True) or from the support team (False). created_at: The timestamp of when the tweet was posted (in UTC format). text: The content of the tweet. response_tweet_id: The unique ID of the response tweet, if applicable. in_response_to_tweet_id: The ID of the original tweet to which this tweet is responding.

How This Data Can Be Used? Training a chatbot: Helps in generating automated support responses. Sentiment analysis: Can analyze whether tweets are complaints, queries, or feedback. Conversation tracking: By linking response tweets with original messages.

originalAuthor : MANORAMA Source : https://www.kaggle.com/datasets/manovirat/aspect/data

Note: This dataset is shared for educational and research purposes only.

Overview

This dataset simulates a production-grade customer support system for a multi-product SaaS company. It contains 2,000 support tickets submitted through 7 different channels — email, chat, phone, WhatsApp, Twitter DM, helpdesk portal, and web form — covering a broad range of issue types, customer segments, and global regions.

Each ticket captures the full lifecycle of a customer issue: from the moment it was created, through triage and agent assignment, to resolution. The dataset is ideal for building and benchmarking NLP classifiers, customer sentiment models, resolution time predictors, churn-risk scorers, and support analytics dashboards.

What Makes This Dataset Unique

Real customer messages — natural language ticket messages across 6 languages (English, Hindi, Arabic, Spanish, French, Japanese, German) End-to-end ticket lifecycle — creation time, resolution time, first response time, reopened/escalated flags Multi-dimensional — product, plan tier, region, channel, priority, and agent all captured Rich target variables — CSAT score (1–5), sentiment (5 levels), resolution status, escalation flag, and resolution time in hours Balanced classes — thoughtfully distributed across categories, regions, and channels

Potential Use Cases

🤖 NLP / Text Classification — Classify support tickets by category or priority from message text 😊 Sentiment Analysis — Predict or validate customer sentiment from message tone ⏱️ Regression — Predict resolution time or first response time 📊 Support Analytics — Visualize agent performance, channel effectiveness, or regional patterns 🔁 Escalation Prediction — Build models to flag tickets likely to be escalated ⭐ CSAT Prediction — Predict customer satisfaction scores from ticket features 🌍 Multilingual NLP — Work with support messages in 7 languages

Dataset at a Glance

PropertyValueRows2,000Columns23Time PeriodJan 2024 – Jun 2025Channels7 (Email, Chat, Phone, WhatsApp, Twitter DM, Helpdesk Portal, Web Form)Products5 (TaskBoard, DataDash, CloudSync Pro, InvoiceFlow, SecureVault)Regions7 (North America, Europe, South Asia, Southeast Asia, Middle East, Africa, Latin America)Languages7 (English, Hindi, Arabic, Spanish, French, Japanese, German)Priority Levels4 (Low, Medium, High, Critical)Ticket Statuses5 (Open, In Progress, Resolved, Closed, Escalated)

Tweets scraped will all possible datapoints provided by twitter in each tweet. For data extraction or scraping contact me on telegram - @akaseobhw

All datapoints present for each tweet.

Each entry in the dataset represents a tweet along with various attributes such as the tweet's ID, URL, text content, retweet count, reply count, like count, quote count, view count, creation date, language, and more. Additionally, there are details about the tweet's author, including their username, profile URL, follower count, following count, profile picture, cover picture, description, location, creation date, and more.

Here's a brief description of the key fields present in each tweet entry:

• type: Indicates the type of data, in this case, it's a tweet.
• id: Unique identifier for the tweet.
• url: URL of the tweet.
• twitterUrl: Twitter URL of the tweet.
• text: Text content of the tweet.
• retweetCount: Number of retweets.
• replyCount: Number of replies.
• likeCount: Number of likes (favorites).
• quoteCount: Number of times the tweet has been quoted.
• viewCount: Number of views.
• createdAt: Date and time when the tweet was created.
• lang: Language of the tweet.
• quoteId: ID of the quoted tweet, if this tweet is a quote.
• bookmarkCount: Number of times the tweet has been bookmarked.
• isReply: Indicates whether the tweet is a reply to another tweet.
• author: Information about the author of the tweet.
  • userName: Username of the author.
  • url: URL of the author's profile.
  • followers: Number of followers of the author.
  • following: Number of accounts the author is following.
  • profilePicture: URL of the author's profile picture.
  • coverPicture: URL of the author's cover picture.
  • description: Description or bio of the author.
  • location: Location of the author.
  • createdAt: Date and time when the author's account was created.
• entities: Entities present in the tweet, such as hashtags, symbols, URLs, and user mentions.
• isRetweet: Indicates whether the tweet is a retweet.
• isQuote: Indicates whether the tweet is a quote.
• quote: Information about the quoted tweet, if this tweet is a quote.
• media: Information about any media (such as images or videos) attached to the tweet.

This dataset can be analyzed to gain insights into trends, sentiments, and user behavior on Twitter. You can use Python libraries like pandas to load this dataset and perform various analyses and visualizations.

Content

This dataset contains public tweets posted by users addressing major US airlines on Twitter. Each record includes the tweet text along with sentiment labels such as positive, negative, or neutral. It may also include additional fields like tweet ID, airline name, timestamp, and user-related metadata that help in analyzing the nature of customer feedback.

Context

The dataset was created to study customer opinions and experiences shared on social media regarding airline services. It is widely used for sentiment analysis, natural language processing (NLP), and machine learning tasks to understand how customers express satisfaction or dissatisfaction and to build models that can automatically classify sentiment from text.

Overview This dataset contains a curated collection of tweets extracted from X (Twitter) between April 2020 and January 2024, focusing on sentiments related to Hinduphobia during the COVID-19 pandemic. The tweets originate from India and are presented in Hinglish (a mix of Hindi and English) and English. It includes 1,000 manually labeled negative tweets (Hinduphobic) and 1,000 positive/neutral tweets (Pro-Hindu), providing a balanced perspective for sentiment analysis.

Data Collection Methodology
Using the X (Twitter) API, we collected tweets that explicitly mention the pandemic with terms such as COVID, COVID-19, or coronavirus. To enhance the dataset, we employed a variety of targeted keywords and hashtags to scrape relevant content. The dataset is designed to capture sentiments related to the Hindu community during this timeframe, encompassing both negative and positive/neutral sentiments. Each tweet has been manually labeled to ensure precise sentiment classification.

Dataset Usage Terms
By using this dataset, you agree to: (i) Use the content of this dataset and any data generated from it for non-commercial research purposes only. (ii) Remain in compliance with Twitter's policies. (iii) Cite this dataset appropriately.

Citation
If you use this dataset, please cite our paper: Paper Link

Contact
For any queries regarding the dataset, please contact: [ashutoshs22102@iiitnr.edu.in].

Hate Speech and Offensive Language Detection

Hate Speech and Offensive Language Detection on Twitter

By hate_speech_offensive (From Huggingface) [source]

About this dataset

This dataset, named hate_speech_offensive, is a meticulously curated collection of annotated tweets with the specific purpose of detecting hate speech and offensive language. The dataset primarily consists of English tweets and is designed to train machine learning models or algorithms in the task of hate speech detection. It should be noted that the dataset has not been divided into multiple subsets, and only the train split is currently available for use.

The dataset includes several columns that provide valuable information for understanding each tweet's classification. The column count represents the total number of annotations provided for each tweet, whereas hate_speech_count signifies how many annotations classified a particular tweet as hate speech. On the other hand, offensive_language_count indicates the number of annotations categorizing a tweet as containing offensive language. Additionally, neither_count denotes how many annotations identified a tweet as neither hate speech nor offensive language.

For researchers and developers aiming to create effective models or algorithms capable of detecting hate speech and offensive language on Twitter, this comprehensive dataset offers a rich resource for training and evaluation purposes

How to use the dataset

• Introduction:

• Dataset Overview:

  • The dataset is presented in a CSV file format named 'train.csv'.
  • It consists of annotated tweets with information about their classification as hate speech, offensive language, or neither.
  • Each row represents a tweet along with the corresponding annotations provided by multiple annotators.
  • The main columns that will be essential for your analysis are: count (total number of annotations), hate_speech_count (number of annotations classifying a tweet as hate speech), offensive_language_count (number of annotations classifying a tweet as offensive language), neither_count (number of annotations classifying a tweet as neither hate speech nor offensive language).

• Data Collection Methodology: The data collection methodology used to create this dataset involved obtaining tweets from Twitter's public API using specific search terms related to hate speech and offensive language. These tweets were then manually labeled by multiple annotators who reviewed them for classification purposes.

• Data Quality: Although efforts have been made to ensure the accuracy of the data, it is important to acknowledge that annotations are subjective opinions provided by individual annotators. As such, there may be variations in classifications between annotators.

• Preprocessing Techniques: Prior to training machine learning models or algorithms on this dataset, it is recommended to apply standard preprocessing techniques such as removing URLs, usernames/handles, special characters/punctuation marks, stop words removal, tokenization, stemming/lemmatization etc., depending on your analysis requirements.

• Exploratory Data Analysis (EDA): Conducting EDA on the dataset will help you gain insights and understand the underlying patterns in hate speech and offensive language. Some potential analysis ideas include:

  • Distribution of tweet counts per classification category (hate speech, offensive language, neither).
  • Most common words/phrases associated with each class.
  • Co-occurrence analysis to identify correlations between hate speech and offensive language.

• Building Machine Learning Models: To train models for automatic detection of hate speech and offensive language, you can follow these steps: a) Split the dataset into training and testing sets for model evaluation purposes. b) Choose appropriate features/

Research Ideas

• Sentiment Analysis: This dataset can be used to train models for sentiment analysis on Twitter data. By classifying tweets as hate speech, offensive language, or neither, the dataset can help in understanding the sentiment behind different tweets and identifying patterns of negative or offensive language.
• Hate Speech Detection: The dataset can be used to develop models that automatically detect hate speech on Twitter. By training machine learning algorithms on this annotated dataset, it becomes possible to create systems that can identify and flag hate speech in real-time, making social media platforms safer and more inclusive.
• Content Moderation: Social media platforms can use this dataset to improve their content m...

Similar to Covid-19, misinformation abounds online about the monkeypox virus, especially on social media. This resource offers two datasets of monkeypox related tweets, each of which has been annotated using two labelling systems: the first a binary ('misinformation' versus 'other') approach; the second a ternary ('misinformation' versus 'good information' versus 'other') approach. (Please note: due to the occasional blurriness of these distinctions, users are recommended to excise the 'other' class if training using the 'misinformation' versus 'good information' dichotomy.)

The first, larger dataset available here (monkeypox.csv) consists of tweets published during a few days early in July 2022. The second, smaller dataset consists of tweets published on 30 August 2022. A suggested use for the datasets is to train and evaluate a classifier on the larger dataset, and then establish how well the classifier would have performed over a month later.

Classification decisions were made via recourse to authoritative sources of public health information such as the World Health Organization, the US Center for Disease Control, and the UK National Health Service -- as well as debunking services such as Politifact and Snopes.

This dataset contains tweets posted for various services and products along with the emotion contained in the tweet. It contains three columns, the tweet text, the product/service, and the emotion contained in the tweet. It can be used to train various ML models for analyzing the sentiments in the tweets.

If you find the data useful do give an upvote and let me know in the discussions about any improvements.

Cheers!!

Context

This is was a Dataset Created as a part of the university Project On Sentimental Analysis On Multi-Source Social Media Platforms using PySpark.

There two datasets Respectively one Consists of Tweets from Twitter with Sentimental Label and the other from Reddit which Consists of Comments with its Sentimental Label.

1. Twitter Dataset

2.**Reddit Dataset**

All these Tweets and Comments were extracted using there Respective Apis Tweepy and PRAW. These tweets and Comments Were Made on Narendra Modi and Other Leaders as well as Peoples Opinion Towards the Next Prime Minister of The Nation ( In Context with General Elections Held In India - 2019). All the Tweets and Comments From twitter and Reddit are Cleaned using Pythons re and also NLP with a Sentimental Label to each ranging from -1 to 1.

1. 0 Indicating it is a Neutral Tweet/Comment 2.1 Indicating a Postive Sentiment 3.-1 Indicating a Negative Tweet/Comment

Content

Twitter.csv Dataset has around 163K Tweets along with Sentiment Labels. Reddit.csv Dataset has around 37K Comments along with its Sentimental Label So Generally Each Dataset has two columns, the first column has the cleaned tweets and Comments and the Second one indicates its Sentimental Label

Acknowledgements

This Dataset was Created with the help of my fellow teammates who passionately worked hard to gather more data with the help of the Tweepy and Reddit Apis. My Project Coordinator encouraged us to collect as much data as possible and he was the main motivation behind Implementing Sentimental Analysis on Multi-Source Social Media Platforms rather than a Single Platform Such as Twitter.

This dataset contains 50,000 tweets related to job vacancies and hiring, extracted using the keywords 'Job Vacancy,' 'We are Hiring,' and 'We're Hiring'. The tweets were collected between January 1, 2019, and April 10, 2023, with the help of snscrape library of Python and are provided in a CSV format.

The purpose behind this dataset

• To explore text pre-processing and test NLP skills
• Draw interesting insights on Job Market from Job Postings.
• Analyse company/role requirements if possible

The dataset includes the following information for each tweet: ID: The unique identifier for the tweet. Timestamp: The date and time when the tweet was posted. User: The Twitter handle of the user who posted the tweet. Text: The content of the tweet. Hashtag: The hashtags included in the tweet, if any. Retweets: The number of times the tweet has been retweeted as of the time it was scraped. Likes: The number of likes the tweet has received as of the time it was scraped. Replies: The number of replies to the tweet as of the time it was scraped. Source: The source application or device used to post the tweet. Location: The location listed on the user's Twitter profile, if any. Verified_Account: A Boolean value indicating whether the user's Twitter account has been verified. Followers: The number of followers the user has as of the time the tweet was scraped. Following: The number of accounts the user is following as of the time the tweet was scraped

Context

This dataset contains 4.403 Indonesian tweets which are labeled into five emotion classes: love, anger, sadness, joy and fear.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

The "Famous Keyword Twitter Replies Dataset" is a comprehensive collection of Twitter data that focuses on popular keywords and their associated replies. This dataset contains five essential columns that provide valuable insights into the Twitter conversation dynamics:

1. Keyword: This column represents the specific keyword or topic of interest that generated the original tweet. It helps identify the context or subject matter around which the conversation revolves.

2. Main_tweet: The main_tweet column contains the original tweet related to the keyword. It serves as the starting point or focal point of the conversation and often provides essential information or opinions on the given topic.

3. Main_likes: This column provides the number of likes received by the main_tweet. Likes serve as a measure of engagement and indicate the level of popularity or resonance of the original tweet within the Twitter community.

4. Reply: The reply column consists of the replies or responses to the main_tweet. These replies may include comments, opinions, additional information, or discussions related to the keyword or the original tweet itself. The replies help capture the diverse perspectives and conversations that emerge in response to the main_tweet.

5. Reply_likes: This column records the number of likes received by each reply. Similar to the main_likes column, the reply_likes column measures the level of engagement and popularity of individual replies. It enables the identification of particularly noteworthy or well-received replies within the dataset.

By analyzing this "Famous Keyword Twitter Replies Dataset," researchers, analysts, and data scientists can gain valuable insights into how popular keywords spark discussions on Twitter and how these discussions evolve through replies.

The dataset's information on likes allows for the evaluation of tweet and reply popularity, helping to identify influential or impactful content.

This dataset serves as a valuable resource for various applications, including sentiment analysis, trend identification, opinion mining, and understanding social media dynamics.

Number of tweets for each pairs of tweet and reply

Total has 17255 pairs of tweet/reply

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F9998584%2Fc33bf662ec0b710877ba40287bc6025e%2Fcount.png?generation=1686152411950305&alt=media" alt="">

This dataset comprises 15,000 Arabic-language tweets manually annotated for depression-related content. Each tweet is labeled as either "Depression" or "Non-depression," with additional binary flags indicating the presence of negation and intensifiers—two key linguistic features often associated with emotional expression. Tweets represent a range of dialects and Modern Standard Arabic, and were sourced publicly from Twitter (X).

The dataset supports research in natural language processing (NLP), mental health detection, sentiment analysis, and cross-lingual depression detection models. Annotation was conducted by native Arabic speakers with training in both linguistics and psychology.

Key Columns:

tweet: Original tweet text (Arabic)

label: Binary indicator (1 = Depression, 0 = Non-depression)

negation_flag: Presence of negation (1 or 0)

intensifier_flag: Presence of emotional intensifiers (1 or 0)

Class: Text label version of label

Licensing & Ethics: Data includes publicly available tweets and excludes any personally identifiable information. To ensure ethical use, it must be used for research purposes only, respecting privacy norms.

Potential Applications:

NLP model training for Arabic mental health detection

Comparative studies across languages and cultures

Social media monitoring for psychological insights

Enhancing clinical decision support tools using real-world text data

this dataset is a combination of over 6 different datasets found on Kaggle. the labels are 0 and 1 which means negative and positive tweets. in the cleared dataset I delete mentions. you can do any preprocessing you want on the dataset. I will appreciate any notebooks submitted on this dataset to help others with sentiment analysis tasks. I will submit mine as well.

Dataset Description

Title: [Sentiment Analysis Dataset]

Description: This dataset contains [social media comments, customer reviews, etc.], specifically collected from [Reddit, Twitter, etc.]. The primary goal of this dataset is to [describe the purpose, e.g., analyze sentiment, predict outcomes, etc.].

** Features** - Number of Rows: [2000] - Number of Columns: [3] - Columns Discriptors: - Id: A unique identifier for each entry. - Body: The text content or main body of the entry. - Sentiment Type: The sentiment classification of the text (e.g., positive, negative, neutral).

Twitter Friends and hashtags

Context

This datasets is an extract of a wider database aimed at collecting Twitter user's friends (other accound one follows). The global goal is to study user's interest thru who they follow and connection to the hashtag they've used.

Content

It's a list of Twitter user's informations. In the JSON format one twitter user is stored in one object of this more that 40.000 objects list. Each object holds :

• avatar : URL to the profile picture

• followerCount : the number of followers of this user

• friendsCount : the number of people following this user.

• friendName : stores the @name (without the '@') of the user (beware this name can be changed by the user)

• id : user ID, this number can not change (you can retrieve screen name with this service : https://tweeterid.com/)

• friends : the list of IDs the user follows (data stored is IDs of users followed by this user)

• lang : the language declared by the user (in this dataset there is only "en" (english))

• lastSeen : the time stamp of the date when this user have post his last tweet.

• tags : the hashtags (whith or without #) used by the user. It's the "trending topic" the user tweeted about.

• tweetID : Id of the last tweet posted by this user.

You also have the CSV format which uses the same naming convention.

These users are selected because they tweeted on Twitter trending topics, I've selected users that have at least 100 followers and following at least 100 other account (in order to filter out spam and non-informative/empty accounts).

Acknowledgements

This data set is build by Hubert Wassner (me) using the Twitter public API. More data can be obtained on request (hubert.wassner AT gmail.com), at this time I've collected over 5 milions in different languages. Some more information can be found here (in french only) : http://wassner.blogspot.fr/2016/06/recuperer-des-profils-twitter-par.html

Past Research

No public research have been done (until now) on this dataset. I made a private application which is described here : http://wassner.blogspot.fr/2016/09/twitter-profiling.html (in French) which uses the full dataset (Millions of full profiles).

Inspiration

On can analyse a lot of stuff with this datasets :

• stats about followers & followings
• manyfold learning or unsupervised learning from friend list
• hashtag prediction from friend list

Contact

Feel free to ask any question (or help request) via Twitter : @hwassner

Enjoy! ;)

YouseeShawn Tweets

Retweets, Likes, and Replies

By Twitter [source]

About this dataset

This dataset is a comprehensive exploration of the YouseeShawn Twitter account, providing a valuable glimpse into their interactions with the rest of the world on social media. Here you will find detailed information on each Tweet they posted, including its content, how many Likes and Retweets it received, any photographs or external links utilized within the post, any Tweets they quoted or replied to as well as how many replies and quotes were received by them. This invaluable source of insight can be used to learn more about user engagement and preferences across various topics and help optimize future social media campaigns

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides valuable insight into the interactions of YouseeShawn on Twitter. This is useful for evaluating their performance, engaging with other users, and understanding their follower base. To make the most of the data, it should be analyzed alongside other metrics such as impressions, engagement rate, and reach. Here are some tips on how to use this dataset:

Research Ideas

• Analyzing the efficacy of different types of Tweets: This dataset can be used to analyze which type of Tweets from YouseeShawn generate the most Likes, replies, and retweets over time.
• Investigating conversation trends: Using this data, analysts can monitor how often users are engaging with YouseeShawn's content and what topics of conversation may be more effective than others in terms of engagement.
• Optimizing content for external links: This dataset can provide valuable insight into how many external links YouseeShawn includes within their tweets, as well as how users respond to them - helping marketers identify the most effective strategies for including external links in tweets with high rates of engagement

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

Acknowledgements

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