Gain valuable insights with our comprehensive Social Media Dataset, designed to help businesses, marketers, and analysts track trends, monitor engagement, and optimize strategies. This dataset provides structured and reliable social media data from multiple platforms.

Dataset Features

User Profiles: Access public social media profiles, including usernames, bios, follower counts, engagement metrics, and more. Ideal for audience analysis, influencer marketing, and competitive research. Posts & Content: Extract posts, captions, hashtags, media (images/videos), timestamps, and engagement metrics such as likes, shares, and comments. Useful for trend analysis, sentiment tracking, and content strategy optimization. Comments & Interactions: Analyze user interactions, including replies, mentions, and discussions. This data helps brands understand audience sentiment and engagement patterns. Hashtag & Trend Tracking: Monitor trending hashtags, topics, and viral content across platforms to stay ahead of industry trends and consumer interests.

Customizable Subsets for Specific Needs Our Social Media Dataset is fully customizable, allowing you to filter data based on platform, region, keywords, engagement levels, or specific user profiles. Whether you need a broad dataset for market research or a focused subset for brand monitoring, we tailor the dataset to your needs.

Popular Use Cases

Brand Monitoring & Reputation Management: Track brand mentions, customer feedback, and sentiment analysis to manage online reputation effectively. Influencer Marketing & Audience Analysis: Identify key influencers, analyze engagement metrics, and optimize influencer partnerships. Competitive Intelligence: Monitor competitor activity, content performance, and audience engagement to refine marketing strategies. Market Research & Consumer Insights: Analyze social media trends, customer preferences, and emerging topics to inform business decisions. AI & Predictive Analytics: Leverage structured social media data for AI-driven trend forecasting, sentiment analysis, and automated content recommendations.

Whether you're tracking brand sentiment, analyzing audience engagement, or monitoring industry trends, our Social Media Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Context

Worldwide Social Media User in 2021 (Quarterly)

Acknowledgements

Facebook: https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/ Twitter: https://investor.twitterinc.com/home/default.aspx Instagram: https://investor.fb.com/home/default.aspx

1) Data Introduction • The Social Media Usage Dataset(Applications) features patterns and activity indicators that 1,000 users use seven major social media platforms, including Facebook, Instagram, and Twitter.

2) Data Utilization (1) Social Media Usage Dataset(Applications) has characteristics that: • This dataset provides different social media activity data for each user, including daily usage time, number of posts, number of likes received, and number of new followers. (2) Social Media Usage Dataset(Applications) can be used to: • Analysis of User Participation by Platform: You can analyze participation and popular trends by platform by comparing usage time and activity for each social media. • Establish marketing strategy: Based on user activity data, it can be used for targeted marketing, content production, and user retention strategies.

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.

About Dataset This dataset captures the pulse of viral social media trends across Facebook, Instagram and Twitter. It provides insights into the most popular hashtags, content types, and user engagement levels, offering a comprehensive view of how trends unfold across platforms. With regional data and influencer-driven content, this dataset is perfect for:

Trend analysis 🔍 Sentiment modeling 💭 Understanding influencer marketing 📈 Dive in to explore what makes content go viral, the behaviors that drive engagement, and how trends evolve on a global scale! 🌍

Social media datasets provide real-time insight into public opinion, trending topics, user behavior, sentiment, and global events as reflected on platforms like Twitter (X), Facebook, and Instagram. These datasets are crucial for marketing analysts, newsrooms, political strategists, crisis response teams, and brand managers to monitor discourse and take data-driven action. Extracted from live user-generated content, […]

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.

This dataset provides comprehensive social media profile links discovered through real-time web search. It includes profiles from major social networks like Facebook, TikTok, Instagram, Twitter, LinkedIn, Youtube, Pinterest, Github and more. The data is gathered through intelligent search algorithms and pattern matching. Users can leverage this dataset for social media research, influencer discovery, social presence analysis, and social media marketing. The API enables efficient discovery of social profiles across multiple platforms. The dataset is delivered in a JSON format via REST API.

Researcher(s): Alexandros Mokas, Eleni Kamateri

Supervisor: Ioannis Tsampoulatidis

This repository contains 3 social media datasets:

2 Post-processing datasets: These datasets contain post-processing data extracted from the analysis of social media posts collected for two different use cases during the first two years of the Deepcube project. More specifically, these include:

The UC2 dataset containing the post-processing analysis of the Twitter data collected for the DeepCube use case (UC2) dealing with the climate induced migration in Africa. This dataset contains in total 5,695,253 social media posts collected from the Twitter platform, based on the initial version of search criteria relevant to UC2 defined by Universitat De Valencia, focused on the regions of Ethiopia and Somalia and started from 26 June, 2021 till March, 2023.

The UC5 dataset containing the post-processing analysis of the Twitter and Instagram data collected for the DeepCube use case (UC5) related to the sustainable and environmentally-friendly tourism. This dataset contains in total 58,143 social media posts collected from the Twitter and Instagram platform (12,881 collected from Twitter and 45,262 collected from Instagram), based on the initial version of search criteria relevant to UC5 defined by MURMURATION SAS, focused on the regions of Brasil and started from 26 June, 2021 till March, 2023.

1 Annotated dataset: An additional anottated dataset was created that contains post-processing data along with annotations of Twitter posts collected for UC2 for the years 2010-2022. More specifically, it includes:

The UC2 dataset contain the post-processing of the Twitter data collected for the DeepCube use case (UC2) dealing with the climate induced migration in Africa. This dataset contains in total 1721 annotated (412 relevant and 1309 irrelevant) by social media posts collected from the Twitter platform, focused on the region of Somalia and started from 1 January, 2010 till 31 December, 2022.

For every social media post retrieved from Twitter and Instagram, a preprocessing step was performed. This involved a three-step analysis of each post using the appropriate web service. First, the location of the post was automatically extracted from the text using a location extraction service. Second, the images included in the post were analyzed using a concept extraction service, which identified and provided the top ten concepts that best described the image. These concepts included items such as "person," "building," "drought," "sun," and so on. Finally, the sentiment expressed in the post's text was determined by using a sentiment analysis service. The sentiment was classified as either positive, negative, or neutral.

After the social media posts were preprocessed, they were visualized using the Social Media Web Application. This intuitive, user-friendly online application was designed for both expert and non-expert users and offers a web-based user interface for filtering and visualizing the collected social media data. The application provides various filtering options, an interactive map, a timeline, and a collection of graphs to help users analyze the data. Moreover, this application provides users with the option to download aggregated data for specific periods by applying filters and clicking the "Download Posts" button. This feature allows users to easily extract and analyze social media data outside of the web application, providing greater flexibility and control over data analysis.

The dataset is provided by INFALIA. INFALIA, being a spin-off of the CERTH institute and a partner of a research EU project, releases this dataset containing Tweets IDs and post pre-processing data for the sole purpose of enabling the validation of the research conducted within the DeepCube. Moreover, Twitter Content provided in this dataset to third parties remains subject to the Twitter Policy, and those third parties must agree to the Twitter Terms of Service, Privacy Policy, Developer Agreement, and Developer Policy (https://developer.twitter.com/en/developer-terms) before receiving this download.

Please cite the following paper when using this dataset:

N. Thakur, “Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis”, Proceedings of the 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024), Chengdu, China, October 18-20, 2024 (Paper accepted for publication, Preprint available at: https://arxiv.org/abs/2410.03293)

Abstract

The outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect and communicate with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19-related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse on Instagram in this context. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. The work presented in this paper aims to address this research gap and presents a novel multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset contains Instagram posts in 161 different languages. After the development of this dataset, multilingual sentiment analysis was performed using VADER and twitter-xlm-roberta-base-sentiment. This process involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset.

For each of these posts, the Post ID, Post Description, Date of publication, language code, full version of the language, and sentiment label are presented as separate attributes in the dataset.

The Instagram posts in this dataset are present in 161 different languages out of which the top 10 languages in terms of frequency are English (343041 posts), Spanish (30220 posts), Hindi (15832 posts), Portuguese (15779 posts), Indonesian (11491 posts), Tamil (9592 posts), Arabic (9416 posts), German (7822 posts), Italian (5162 posts), Turkish (4632 posts)

There are 535,021 distinct hashtags in this dataset with the top 10 hashtags in terms of frequency being #covid19 (169865 posts), #covid (132485 posts), #coronavirus (117518 posts), #covid_19 (104069 posts), #covidtesting (95095 posts), #coronavirusupdates (75439 posts), #corona (39416 posts), #healthcare (38975 posts), #staysafe (36740 posts), #coronavirusoutbreak (34567 posts)

The following is a description of the attributes present in this dataset

• Post ID: Unique ID of each Instagram post
• Post Description: Complete description of each post in the language in which it was originally published
• Date: Date of publication in MM/DD/YYYY format
• Language code: Language code (for example: “en”) that represents the language of the post as detected using the Google Translate API
• Full Language: Full form of the language (for example: “English”) that represents the language of the post as detected using the Google Translate API
• Sentiment: Results of sentiment analysis (using the preprocessed version of each post) where each post was classified as positive, negative, or neutral

Open Research Questions

This dataset is expected to be helpful for the investigation of the following research questions and even beyond:

1. How does sentiment toward COVID-19 vary across different languages?
2. How has public sentiment toward COVID-19 evolved from 2020 to the present?
3. How do cultural differences affect social media discourse about COVID-19 across various languages?
4. How has COVID-19 impacted mental health, as reflected in social media posts across different languages?
5. How effective were public health campaigns in shifting public sentiment in different languages?
6. What patterns of vaccine hesitancy or support are present in different languages?
7. How did geopolitical events influence public sentiment about COVID-19 in multilingual social media discourse?
8. What role does social media discourse play in shaping public behavior toward COVID-19 in different linguistic communities?
9. How does the sentiment of minority or underrepresented languages compare to that of major world languages regarding COVID-19?
10. What insights can be gained by comparing the sentiment of COVID-19 posts in widely spoken languages (e.g., English, Spanish) to those in less common languages?

All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

Context: This dataset offers insights into the usage patterns of social media apps for 1,000 users across seven popular platforms: Facebook, Instagram, Twitter, Snapchat, TikTok, LinkedIn, and Pinterest. It tracks various metrics such as daily time spent on the app, number of posts made, likes received, and new followers gained.

Dataset Features:

User_ID: Unique identifier for each user. App: The social media platform being used. Daily_Minutes_Spent: Total time a user spends on the app each day, ranging from 5 to 500 minutes. Posts_Per_Day: Number of posts a user creates per day, ranging from 0 to 20. Likes_Per_Day: Total number of likes a user receives on their posts each day, ranging from 0 to 200. Follows_Per_Day: The number of new followers a user gains daily, ranging from 0 to 50. Context & Use Cases: This dataset could be particularly useful for social media analysts, digital marketers, or researchers interested in understanding user engagement trends across different platforms. It provides insights into how much time users spend, how actively they post, and the level of engagement they receive (in terms of likes and followers).

Conclusion & Outcome: Analyzing this dataset could yield several outcomes:

Engagement Patterns: Identifying which platforms have higher engagement in terms of time spent or likes received. Active Users: Determining which users are the most active across various platforms based on the number of posts and followers gained. User Retention: Studying the correlation between time spent and follower growth, providing insight into user retention strategies for different platforms. Overall, the dataset allows for exploration of social media usage trends and helps drive decision-making for marketing strategies, content creation, and platform engagement.

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

Twitter User Dataset

This dataset was obtained by crawling Twitter's REST API using the Python library Tweepy 3. The dataset comprises tweets from the 20 most popular Twitter users based on the number of followers, with retweets excluded. These accounts include public figures such as Katy Perry and Barack Obama, platforms like YouTube and Instagram, and television channels such as CNN Breaking News and The Ellen Show. The dataset presents a diverse collection of tweets, ranging from… See the full description on the dataset page: https://huggingface.co/datasets/haydenbanz/Tweets_Dataset.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Social Media Synthetic Dataset

  Description

Topic: Social Media Posts and Interactions Domains: Social Media Platforms (Twitter, Facebook, Instagram) Focus: Synthetic collection of social media content and interactions Number of Entries: 500 Dataset Type: Raw Dataset Model Used: anthropic/claude-3-5-sonnet-20241022 Language: English Generated by: SynthGenAI Package

  Additional Information

The dataset contains synthesized social media posts mimicking real… See the full description on the dataset page: https://huggingface.co/datasets/Shekswess/social-media-instruction.

Instagram’s most popular post

              As of April 2024, the most popular post on Instagram was Lionel Messi and his teammates after winning the 2022 FIFA World Cup with Argentina, posted by the account @leomessi. Messi's post, which racked up over 61 million likes within a day, knocked off the reigning post, which was 'Photo of an Egg'. Originally posted in January 2021, 'Photo of an Egg' surpassed the world’s most popular Instagram post at that time, which was a photo by Kylie Jenner’s daughter totaling 18 million likes.
              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.

This dataset tracks influencer marketing campaigns across major social media platforms, providing a robust foundation for analyzing campaign effectiveness, engagement, reach, and sales outcomes. Each record represents a unique campaign and includes details such as the campaign’s platform (Instagram, YouTube, TikTok, Twitter), influencer category (e.g., Fashion, Tech, Fitness), campaign type (Product Launch, Brand Awareness, Giveaway, etc.), start and end dates, total user engagements, estimated reach, product sales, and campaign duration. The dataset structure supports diverse analyses, including ROI calculation, campaign benchmarking, and influencer performance comparison.

Columns: - campaign_id: Unique identifier for each campaign
- platform: Social media platform where the campaign ran
- influencer_category: Niche or industry focus of the influencer
- campaign_type: Objective or style of the campaign
- start_date, end_date: Campaign time frame
- engagements: Total user interactions (likes, comments, shares, etc.)
- estimated_reach: Estimated number of unique users exposed to the campaign
- product_sales: Number of products sold as a result of the campaign
- campaign_duration_days: Duration of the campaign in days

Getting Started with the Data

1. Load and Inspect the Dataset

import pandas as pd

df = pd.read_csv('influencer_marketing_roi_dataset.csv', parse_dates=['start_date', 'end_date'])
print(df.head())
print(df.info())

2. Basic Exploration

# Overview of campaign types and platforms
print(df['campaign_type'].value_counts())
print(df['platform'].value_counts())

# Summary statistics
print(df[['engagements', 'estimated_reach', 'product_sales']].describe())

3. Engagement and Sales Analysis

# Average engagements and sales by platform
platform_stats = df.groupby('platform')[['engagements', 'product_sales']].mean()
print(platform_stats)

# Top influencer categories by product sales
top_categories = df.groupby('influencer_category')['product_sales'].sum().sort_values(ascending=False)
print(top_categories)

4. ROI Calculation Example

# Assume a fixed campaign cost for demonstration
df['campaign_cost'] = 500 + df['estimated_reach'] * 0.01 # Example formula

# Calculate ROI: (Revenue - Cost) / Cost
# Assume each product sold yields $40 revenue
df['revenue'] = df['product_sales'] * 40
df['roi'] = (df['revenue'] - df['campaign_cost']) / df['campaign_cost']

# View campaigns with highest ROI
top_roi = df.sort_values('roi', ascending=False).head(10)
print(top_roi[['campaign_id', 'platform', 'roi']])

5. Visualizing Campaign Performance

import matplotlib.pyplot as plt
import seaborn as sns

# Engagements vs. Product Sales scatter plot
plt.figure(figsize=(8,6))
sns.scatterplot(data=df, x='engagements', y='product_sales', hue='platform', alpha=0.6)
plt.title('Engagements vs. Product Sales by Platform')
plt.xlabel('Engagements')
plt.ylabel('Product Sales')
plt.legend()
plt.show()

# Average ROI by Influencer Category
category_roi = df.groupby('influencer_category')['roi'].mean().sort_values()
category_roi.plot(kind='barh', color='teal')
plt.title('Average ROI by Influencer Category')
plt.xlabel('Average ROI')
plt.show()

6. Time-Based Analysis

# Campaigns over time
df['month'] = df['start_date'].dt.to_period('M')
monthly_sales = df.groupby('month')['product_sales'].sum()
monthly_sales.plot(figsize=(10,4), marker='o', title='Monthly Product Sales from Influencer Campaigns')
plt.ylabel('Product Sales')
plt.show()

Use Cases

• ROI Analysis: Quantify the return on investment for influencer campaigns across platforms and categories.
• Campaign Benchmarking: Compare campaign performance by type, influencer niche, or platform.
• Trend Analysis: Track engagement, reach, and sales trends over time.
• Influencer Selection: Identify high-performing influencer categories and campaign types for future partnerships.

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

Die Studie über Facebook-Nutzer wurde von infratest dimap im Auftrag der Konrad-Adenauer-Stiftung durchgeführt. Im Erhebungszeitraum 26. November bis 4. Dezember 2018 wurden 2.041 Facebook-Nutzer in Onlineinterviews (CAWI) zu folgenden Themen befragt: Internetnutzung, Facebook-Gruppen, Facebooknutzung, politische Inhalte auf Facebook, Reaktion auf Inhalte, Bildexperiment und Sonntagsfrage. Die Auswahl der Befragten erfolgte durch eine Quotenstichprobe aus einem Online-Access-Panel. Nutzung verschiedener Internetangebote (Tinder, Facebook, Twitter, snapchat, Instagram, YouTube, Online-Zeitungen, nichts davon); Nutzung offener oder geschlossener Facebook-Gruppen; Facebook-Inhalte zu politischen Themen, zu berufsbezogenen Themen, zu Hobbies, zur Unterhaltung bzw. zu anderen Themen; Art der Facebook-Nutzung (lese/ like/ teile Inhalte, schreibe Kommentare, verbreite eigene Inhalte); politische Facebook-Nutzung (lese/ like/ teile politische Inhalte, schreibe Kommentare zu politischen Themen, verbreite eigenen Inhalt zu politischen Themen); Reaktion auf Facebook- Inhalte bzw. Kommentare (fühle mich informiert, unterhalten, verärgert, provoziert); Zustimmung zu verschiedenen Aussagen zu Facebook (auf Facebook regen mich andere auf, zeige ich anderen ihre Grenzen, kann ich anonym meine Meinung sagen, finde ich viele verschiedene Meinungen, finde ich Meinungen, die sonst unterdrückt werden, traue ich mich Dinge zu sagen/ teilen, die ich sonst nicht sagen würde); Parteipräferenz (Sonntagsfrage); Kommentar (offen) zu einem provozierenden Bild (Split A: Flüchtlinge, Split B: Pegida). Demographie: Geschlecht; Alter (Geburtsjahr); Bildung; Erwerbstätigkeit; berufliche Stellung; Haushaltsnettoeinkommen (gruppiert); Bundesland. Zusätzlich verkodet wurde: lfd. Nummer; Gewichtungsfaktor. The study on Facebook users was conducted by infratest dimap on behalf of the Konrad Adenauer Foundation. During the survey period from November 26 to December 4, 2018, 2,041 Facebook users were surveyed in online interviews (CAWI) on the following topics: internet use, Facebook groups, Facebook use, political content on Facebook, reaction to content, image experiment and Sunday question. Respondents were selected by quota sampling from an online access panel. Use of various internet services (Tinder, Facebook, Twitter, snapchat, Instagram, YouTube, online newspapers, none of the above); use of open or closed Facebook groups; Facebook content on political topics, on job-related topics, on hobbies, on entertainment or on other topics; type of Facebook use (read/ like/ share content, write comments, disseminate own content); political Facebook use (read/ like/ share political content, write comments on political topics, disseminate own content on political topics); reaction to Facebook content or comments (do I feel informed, entertained, annoyed, provoked, etc.). comments (feel informed, entertained, annoyed, provoked); agreement with various statements on Facebook (on Facebook others upset me, I show others their limits, I can speak my mind anonymously, I find many different opinions, I find opinions that are otherwise suppressed, I dare to say/ share things I would not otherwise say); party preference (Sunday question); comment (open) on a provocative image (split A: refugees, split B: Pegida). Demography: sex; age (year of birth); education; employment; occupational status; net household income (grouped); federal state. Additionally coded: serial number; weighting factor.