The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

This is the dataset for the article "A Predictive Method to Improve the Effectiveness of Twitter Communication in a Cultural Heritage Scenario".

Abstract:

Museums are embracing social technologies in the attempt to broaden their audience and to engage people. Although social communication seems an easy task, media managers know how hard it is to reach millions of people with a simple message. Indeed, millions of posts are competing every day to get visibility in terms of likes and shares and very little research focused on museums communication to identify best practices. In this paper, we focus on Twitter and we propose a novel method that exploits interpretable machine learning techniques to: (a) predict whether a tweet will likely be appreciated by Twitter users or not; (b) present simple suggestions that will help enhancing the message and increasing the probability of its success. Using a real-world dataset of around 40,000 tweets written by 23 world famous museums, we show that our proposed method allows identifying tweet features that are more likely to influence the tweet success.

Code to run a selection of experiments is available at https://github.com/rmartoglia/predict-twitter-ch

Dataset structure

The dataset contains the dataset used in the experiments of the above research paper. Only the extracted features for the museum tweet threads (and not the message full text) are provided and needed for the analyses.

We selected 23 well known world spread art museums and grouped them into five groups: G1 (museums with at least three million of followers); G2 (museums with more than one million of followers); G3 (museums with more than 400,000 followers); G4 (museums with more that 200,000 followers); G5 (Italian museums). From these museums, we analyzed ca. 40,000 tweets, with a number varying from 5k ca. to 11k ca. for each museum group, depending on the number of museums in each group.

Content features: these are the features that can be drawn form the content of the tweet itself. We further divide such features in the following two categories:

– Countable: these features have a value ranging into different intervals. We take into consideration: the number of hashtags (i.e., words preceded by #) in the tweet, the number of URLs (i.e., links to external resources), the number of images (e.g., photos and graphical emoticons), the number of mentions (i.e., twitter accounts preceded by @), the length of the tweet;

– On-Off : these features have binary values in {0, 1}. We observe whether the tweet has exclamation marks, question marks, person names, place names, organization names, other names. Moreover, we also take into consideration the tweet topic density: assuming that the involved topics correspond to the hashtags mentioned in the text, we define a tweet as dense of topics if the number of hashtags it contains is greater than a given threshold, set to 5. Finally, we observe the tweet sentiment that might be present (positive or negative) or not (neutral).

Context features: these features are not drawn form the content of the tweet itself and might give a larger picture of the context in which the tweet was sent. Namely, we take into consideration the part of the day in which the tweet was sent (morning, afternoon, evening and night respectively from 5:00am to 11:59am, from 12:00pm to 5:59pm, from 6:00pm to 10:59pm and from 11pm to 4:59am), and a boolean feature indicating whether the tweet is a retweet or not.

User features: these features are proper of the user that sent the tweet, and are the same for all the tweets of this user. Namely we consider the name of the museum and the number of followers of the user.

The dataset consists of various columns containing information related to tweets posted on Twitter. Each row in the dataset represents a single tweet. Here's an explanation of the columns in the dataset from a third-person perspective:

Tweet: This column contains the actual text content of the tweet. It includes the message that the user posted on Twitter. Tweets can vary in length from a few characters to the maximum allowed by Twitter.

Sentiment: This column indicates the sentiment or emotional tone of the tweet. Sentiment can be classified into categories such as positive, negative, or neutral. It reflects the overall opinion or attitude expressed in the tweet.

Username: This column contains the username of the Twitter account that posted the tweet. Each Twitter user has a unique username that identifies their account.

Timestamp: This column contains the timestamp indicating when the tweet was posted. It includes information about the date and time when the tweet was published on Twitter.

Retweets: This column represents the number of times the tweet has been retweeted by other Twitter users. A retweet is when a user shares another user's tweet with their followers.

Likes: This column indicates the number of likes or favorites received by the tweet. Users can express their appreciation for a tweet by liking it.

Hashtags: This column contains any hashtags included in the tweet. Hashtags are keywords or phrases preceded by the "#" symbol, used to categorize or label tweets and make them more discoverable.

Mentions: This column includes any Twitter usernames mentioned in the tweet. Mentions are when a user tags another user in their tweet by including their username preceded by the "@" symbol.

Location: This column provides information about the location associated with the tweet. It may include details such as the city, state, country, or geographical coordinates from which the tweet was posted, if available.

Source: This column specifies the source or platform used to post the tweet. It indicates whether the tweet was posted from the Twitter website, a mobile app, or a third-party application.

This dataset features the training models, emotion classifications and emotion patterns before and after events, related to the paper:

F. Kunneman, M. van Mulken and A. Van den Bosch, Anticipointment detection in event tweets (under review)

Abstract of the study:
We developed a system to detect positive expectation, disappointment, and satisfaction in tweets that refer to events automatically discovered in the Twitter stream. The emotional content shared on Twitter when referring to public events can provide insights into the presumed and experienced quality of the event. We expected to find a connection between positive expectation and disappointment, a succession that is sometimes referred to as anticipointment. The application of computational approaches makes it possible to detect the presence and strength of this hypothetical relation for a large number of events. We extracted events from a longitudinal data set of Dutch Twitter posts, and modeled classifiers to recognize emotion in the tweets related to those events by means of hashtag-labeled training data. After classifying all tweets before and after the events in our data set, we summarized the collective emotions by calculating the percentage of tweets classified with an emotion as well as ranking tweets based on the classifier confidence score for an emotion and selecting the 90th percentile. Only a weak correlation of around 0.2 was found between positive expectation and disappointment, while a higher correlation of 0.6 was found between positive
expectation and satisfaction. The most anticipointing events were events with a clear loss, such as a canceled event or when the favored sports team had lost. We conclude that senders of Twitter posts might be more inclined to share satisfaction than disappointment after a much anticipated event.

Subject period: January 1st 2011 until October 31st 2015

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

The number of Twitter users in Brazil was forecast to continuously increase between 2024 and 2028 by in total 3.4 million users (+15.79 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 24.96 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

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...

Instagram’s most popular post

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              After several cryptic posts published by the account, World Record Egg revealed itself to be a part of a mental health campaign aimed at the pressures of social media use.

              Instagram’s most popular accounts

              As of April 2024, the official Instagram account @instagram had the most followers of any account on the platform, with 672 million followers. Portuguese footballer Cristiano Ronaldo (@cristiano) was the most followed individual with 628 million followers, while Selena Gomez (@selenagomez) was the most followed woman on the platform with 429 million. Additionally, Inter Miami CF striker Lionel Messi (@leomessi) had a total of 502 million. Celebrities such as The Rock, Kylie Jenner, and Ariana Grande all had over 380 million followers each.

              Instagram influencers

              In the United States, the leading content category of Instagram influencers was lifestyle, with 15.25 percent of influencers creating lifestyle content in 2021. Music ranked in second place with 10.96 percent, followed by family with 8.24 percent. Having a large audience can be very lucrative: Instagram influencers in the United States, Canada and the United Kingdom with over 90,000 followers made around 1,221 US dollars per post.

              Instagram around the globe

              Instagram’s worldwide popularity continues to grow, and India is the leading country in terms of number of users, with over 362.9 million users as of January 2024. The United States had 169.65 million Instagram users and Brazil had 134.6 million users. The social media platform was also very popular in Indonesia and Turkey, with 100.9 and 57.1, respectively. As of January 2024, Instagram was the fourth most popular social network in the world, behind Facebook, YouTube and WhatsApp.

Datasets for the publication "Auditing Elon Musk's Impact on Hate Speech and Bots" [1].

File information:

• baseline_tweet_ids_2022.csv, hate_tweet_ids_2022.csv: List of IDs and their corresponding dates from the "baseline" and "hate" samples of tweets used in the publication, respectively. The former is used to create the number of baseline tweets each day (‘baseline_freq.csv’) while the latter is used to create the number of hate tweets each day (‘hate_freq.csv'). We share the date a tweet was made as well as its tweet ID from which you can find the original tweet’s URL with the help of this web page. As you explore these data, you may notice in a minority of cases hate tweets that are not hateful or, alternatively, baseline tweets that are hateful. This is a product of our filtering method used to collect and analyze tweets at scale. We always look forward to hearing your suggestions to improve the tweet filtering process.
• baseline_freq.csv, hate_freq.csv: Number of collected tweets per day for the baseline and hate samples, respectively. The file 'freq_data.py' is used to calculate these frequencies from the raw data. Feel free to consult this if you have questions about how the frequencies are calculated (or if you want to change how the data are aggregated). Use these data to recreate Figure 2 from Hickey et al [1]. See the Methods section of the publication for more details.
• user_hate_levels_per_day.csv: CSV file with dates (YYYY-MM-DD format) and the mean proportion of slurs used by hateful users each day from October 1st to November 30th, 2022. Use these data to recreate Figure 1 from Hickey et al [1]. See the Methods section for more details.
• hate_keywords.txt: Words used to query the Twitter Academic API for hate tweets.
• unfiltered_tweets_containing_hate_words.csv: All tweets with hate words collected with values for Perspective API attributes.

Reference:

1. Hickey, D., Schmitz, M., Fessler, D.M.T, Smaldino, P., Muric, G., & Burghardt, K. Auditing Elon Musk's Impact on Hate Speech and Bots. In Proceedings of the 17th International AAAI Conference on Web and Social Media, (2023).

V2 note: This is an updated version of this dataset that was previously uploaded on 1/23/2024. In the previous version, tweet IDs were stored as floating point values which truncated them. In this version, that issue has been fixed and all IDs are complete.

The number of Reddit users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.3 million users (+5.21 percent). After the ninth consecutive increasing year, the Reddit user base is estimated to reach 208.12 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Reddit users in countries like Mexico and Canada.

Context

The 2020 US election is happening on the 3rd November 2020 and the resulting impact to the world will no doubt be large, irrespective of which candidate is elected! After reading the two papers, here and here, I was inspired to attempt a similar sentiment analysis myself!

Content

Tweets collected, using the Twitter API statuses_lookup and snsscrape for keywords, with the original intention to try to update this dataset daily so that the timeframe will eventually cover 15.10.2020 and 04.11.2020. Added 06.11.2020 With the events of the election still ongoing as of the date that this comment was added, I've decided to keep updating the dataset with tweets until at least the end of the 6th Nov. Added 08.11.2020, just one more version pending to include tweets until at the end of the 8th Nov.

Columns are as follows: - created_at: Date and time of tweet creation - tweet_id: Unique ID of the tweet - tweet: Full tweet text - likes: Number of likes - retweet_count: Number of retweets - source: Utility used to post tweet - user_id: User ID of tweet creator - user_name: Username of tweet creator - user_screen_name: Screen name of tweet creator - user_description: Description of self by tweet creator - user_join_date: Join date of tweet creator - user_followers_count: Followers count on tweet creator - user_location: Location given on tweet creator's profile - lat: Latitude parsed from user_location - long: Longitude parsed from user_location - city: City parsed from user_location - country: Country parsed from user_location - state: State parsed from user_location - state_code: State code parsed from user_location - collected_at: Date and time tweet data was mined from twitter*

Acknowledgements

• @taniaj and her great Australian Election 2019 Tweets dataset, I used as a template for this dataset.

• Thanks to Twitter for providing the free API and snsscrape to allow collection of the tweet_ids.

Cover photo by Jorge Alcala on Unsplash Unsplash Images are distributed under a unique Unsplash License.

Inspiration

My primary interest for creating this dataset is to ascertain if there is a correlation between the sentiment of users on Twitter and the eventual election results. Other ideas that might be interesting to investigate include:

• Can we detect if there are or were any attempts at manipulating the election.
• Can we predict the candidate from tweet text only.
• Can we predict the election outcome of each state.

I also included still valid and interesting ideas from the Australian Election 2019 Tweets dataset below:

• Take into account retweets and favourites to weight overall sentiment analysis.
• Which parts of the world are interested (ie: tweet about) in the US elections, apart from the US?
• How do the users who tweet about this sort of thing tend to describe themselves?
• Is there a correlation between when the user joined Twitter and their political views (this assumes the sentiment analysis is already working well)?
• Predict gender from username/screen name and segment tweet count and sentiment by gender

Version

• Version 3 - 355,000 tweets collected, using the Twitter API statuses_lookup and snsscrape for keywords between 15.10.2020 and 22.10.2020.

• Version 5 - New tweets collected for the date of 23.10.2020, with a new total number of tweets at around 387,000 tweets.

• Version 6 - New tweets collected for the date of 24.10.2020, with a new total number of tweets at around 418,000 tweets. Additionally the "coordinates" column was removed with "lat" and "long" columns added for geolocation data (where possible).

• Version 7 - New tweets collected for the date of 25.10.2020, with a new total number of tweets at around 456,000 tweets. Added column "collected_at" to indicate when the data was mined from twitter. *Note this data is only accurate from 21.10.2020 onwards, data in the subject column before this date is an estimation.

• Version 8 - New tweets collected for the date of 26.10.2020, with a new total number of tweets at around 49...

About this Dataset

This dataset includes: 1) A worksheet with structured tabular data of 3000 posts from the five removed Facebook pages linked to the 2016 US election investigation; 2) Previewable PDFs of the raw text from the posts for five of these six Facebook pages; and 3) A full set of connected data visualizations on Tableau public https://public.tableau.com/views/FB4/TotalReachbyPage?:embed=y&:display_count=yes

Objectives of Project

Outside of a limited number of statements regarding election "ads," and hearsay news about a handful of 470 closed Facebook pages, the researchers and the public have been left in the dark about the election hacking scandal as it relates to the internet and society.

Whereas Twitter is over-represented in research, few Americans of outside of politicians, celebrities, sports, and media figures use Twitter to get their daily news, or use it to regularly interact with friends and family. Facebook, on the other hand, is far and away the dominant platform in America for obtaining news as well as for social and civic engagement.

The data presented here is a catalog of the non-promoted organic reach of the posts on each of the alleged foreign influence ops pages, showing the "total shared to" and sum of interactions (FB "reactions" + "likes" + shares, and comments) for each of the individual posts. Data was obtained directly from Crowdtangle, a Facebook-owned social analytics service.

Along with the complete text archive for each of posts, this data sheds light on the larger potential impact of the use of Facebook's platform beyond of a single advertising buy. Specifically, the work presented here suggests that there was a much more subtle, if not outright subversive campaign on these five closed pages to:

a) Siphon Facebook users' data related to their personal views and moral standings about sensitive topics by observing their responses to suggestive statements followed by discussion questions and conversation prompts;

b) Use faux-support, trust-building, and actor deception to test users' attitudes, core values, religious beliefs, and push the boundaries of social norms (e.g., racism justification through immigration); and

c) Encourage users' to be tracked through emotional sharing vectors - "likes," "reactions," and url shares - to monitor issue "wedges," further segment audiences, and to identify "hot-button" issues and keywords around current events.

Share and Use

Please feel free to use this data for your own research and subsequent analysis. All I ask is that you give attribution. The collection, cleaning, and organization of this data -- and the design of the corresponding visualizations were extremely time intensive.

Related Resources

• Election Data Investigation on Medium: https://medium.com/@d1gi/election2016-fakenews-compilation-455870d04bb

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