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.

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.

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.

The number of social media users in the United States was forecast to continuously increase between 2024 and 2029 by in total 26 million users (+8.55 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 330.07 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.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).

This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/

The goal of this dataset is to predict sentiment score for news title. This dataset contains 83164 time series obtained from the News Popularity in Multiple Social Media Platforms dataset from the UCI repository. This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation. The time series has 3 dimensions.

Please refer to https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms for more details

Citation request
Nuno Moniz and Luis Torgo (2018), Multi-Source Social Feedback of Online News Feeds, CoRR

The Reddit Subreddit Dataset by Dataplex offers a comprehensive and detailed view of Reddit’s vast ecosystem, now enhanced with appended AI-generated columns that provide additional insights and categorization. This dataset includes data from over 2.1 million subreddits, making it an invaluable resource for a wide range of analytical applications, from social media analysis to market research.

Dataset Overview:

This dataset includes detailed information on subreddit activities, user interactions, post frequency, comment data, and more. The inclusion of AI-generated columns adds an extra layer of analysis, offering sentiment analysis, topic categorization, and predictive insights that help users better understand the dynamics of each subreddit.

2.1 Million Subreddits with Enhanced AI Insights: The dataset covers over 2.1 million subreddits and now includes AI-enhanced columns that provide: - Sentiment Analysis: AI-driven sentiment scores for posts and comments, allowing users to gauge community mood and reactions. - Topic Categorization: Automated categorization of subreddit content into relevant topics, making it easier to filter and analyze specific types of discussions. - Predictive Insights: AI models that predict trends, content virality, and user engagement, helping users anticipate future developments within subreddits.

Sourced Directly from Reddit:

All data in this dataset is sourced directly from Reddit, ensuring accuracy and authenticity. The dataset is updated regularly, reflecting the latest trends and user interactions on the platform. This ensures that users have access to the most current and relevant data for their analyses.

Key Features:

• Subreddit Metrics: Detailed data on subreddit activity, including the number of posts, comments, votes, and user participation.
• User Engagement: Insights into how users interact with content, including comment threads, upvotes/downvotes, and participation rates.
• Trending Topics: Track emerging trends and viral content across the platform, helping you stay ahead of the curve in understanding social media dynamics.
• AI-Enhanced Analysis: Utilize AI-generated columns for sentiment analysis, topic categorization, and predictive insights, providing a deeper understanding of the data.

Use Cases:

• Social Media Analysis: Researchers and analysts can use this dataset to study online behavior, track the spread of information, and understand how content resonates with different audiences.
• Market Research: Marketers can leverage the dataset to identify target audiences, understand consumer preferences, and tailor campaigns to specific communities.
• Content Strategy: Content creators and strategists can use insights from the dataset to craft content that aligns with trending topics and user interests, maximizing engagement.
• Academic Research: Academics can explore the dynamics of online communities, studying everything from the spread of misinformation to the formation of online subcultures.

Data Quality and Reliability:

The Reddit Subreddit Dataset emphasizes data quality and reliability. Each record is carefully compiled from Reddit’s vast database, ensuring that the information is both accurate and up-to-date. The AI-generated columns further enhance the dataset's value, providing automated insights that help users quickly identify key trends and sentiments.

Integration and Usability:

The dataset is provided in a format that is compatible with most data analysis tools and platforms, making it easy to integrate into existing workflows. Users can quickly import, analyze, and utilize the data for various applications, from market research to academic studies.

User-Friendly Structure and Metadata:

The data is organized for easy navigation and analysis, with metadata files included to help users identify relevant subreddits and data points. The AI-enhanced columns are clearly labeled and structured, allowing users to efficiently incorporate these insights into their analyses.

Ideal For:

• Data Analysts: Conduct in-depth analyses of subreddit trends, user engagement, and content virality. The dataset’s extensive coverage and AI-enhanced insights make it an invaluable tool for data-driven research.
• Marketers: Use the dataset to better understand your target audience, tailor campaigns to specific interests, and track the effectiveness of marketing efforts across Reddit.
• Researchers: Explore consumer behavior data of online communities, analyze the spread of ideas and information, and study the impact of digital media on public discourse, all while leveraging AI-generated insights.

This dataset is an essential resource for anyone looking to understand the intricacies of Reddit's vast ecosystem, offering the data and AI-enhanced insights needed to drive informed decisions and strategies across various fields. Whether you’re tracking emerging trends, analyzing user behavior, or conducting acade...

Context This dataset is a collection of datasets from different sources related to the automatic detection of cyber-bullying. The data is from different social media platforms like Kaggle, Twitter, Wikipedia Talk pages and YouTube. The data contain text and labeled as bullying or not. The data contains different types of cyber-bullying like hate speech, aggression, insults and toxicity.

Content The data is from different social media platforms like Kaggle, Twitter, Wikipedia Talk pages and YouTube. The data contain text and labeled as bullying or not. The data contains different types of cyber-bullying like hate speech, aggression, insults and toxicity.

Acknowledgements Elsafoury, Fatma (2020), “Cyberbullying datasets”, Mendeley Data, V1, doi: 10.17632/jf4pzyvnpj.1

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

• ID: A unique identifier for each tweet.
• text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.
• polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).
• favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.
• retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.
• user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.
• user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.
• user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.
• user_followers_count: The current number of followers the account has. It is a non-negative integer.
• user_friends_count: The number of users that the account is following. It is a non-negative integer.
• user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.
• user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.
• user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.
• user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

• Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.
• Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.
• Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.

Tiktok network graph with 5,638 nodes and 318,986 unique links, representing up to 790,599 weighted links between labels, using Gephi network analysis software.

Source of:

Peña-Fernández, Simón, Larrondo-Ureta, Ainara, & Morales-i-Gras, Jordi. (2022). Current affairs on TikTok. Virality and entertainment for digital natives. Profesional De La Información, 31(1), 1–12. https://doi.org/10.5281/zenodo.5962655

Abstract:

Since its appearance in 2018, TikTok has become one of the most popular social media platforms among digital natives because of its algorithm-based engagement strategies, a policy of public accounts, and a simple, colorful, and intuitive content interface. As happened in the past with other platforms such as Facebook, Twitter, and Instagram, various media are currently seeking ways to adapt to TikTok and its particular characteristics to attract a younger audience less accustomed to the consumption of journalistic material. Against this background, the aim of this study is to identify the presence of the media and journalists on TikTok, measure the virality and engagement of the content they generate, describe the communities created around them, and identify the presence of journalistic use of these accounts. For this, 23,174 videos from 143 accounts belonging to media from 25 countries were analyzed. The results indicate that, in general, the presence and impact of the media in this social network are low and that most of their content is oriented towards the creation of user communities based on viral content and entertainment. However, albeit with a lesser presence, one can also identify accounts and messages that adapt their content to the specific characteristics of TikTok. Their virality and engagement figures illustrate that there is indeed a niche for current affairs on this social network.

TrueFace is a first dataset of social media processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram.

Images have historically been a universal and cross-cultural communication medium, capable of reaching people of any social background, status or education. Unsurprisingly though, their social impact has often been exploited for malicious purposes, like spreading misinformation and manipulating public opinion. With today's technologies, the possibility to generate highly realistic fakes is within everyone's reach. A major threat derives in particular from the use of synthetically generated faces, which are able to deceive even the most experienced observer. To contrast this fake news phenomenon, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem "in the wild", more realistic image databases, like TrueFace, are needed to train specialised detectors.

How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Please cite the following paper when using this dataset:

N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109

Abstract

The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description

The dataset comprises a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.

The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development.

Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)

Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)

Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)

Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)

Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)

Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)

Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)

Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)

Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.

Table 1. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development

Terminology

List of synonyms and terms

COVID-19

Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus

online learning

online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures

Please cite the following paper when using this dataset:N. Thakur, "Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007AbstractThe exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today’s living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 Tweets about exoskeletons that were posted in a 5-year period from 21 May 2017 to 21 May 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

The current dataset contains 237M Tweet IDs for Twitter posts that mentioned "COVID" as a keyword or as part of a hashtag (e.g., COVID-19, COVID19) between March and July of 2020. Sampling Method: hourly requests sent to Twitter Search API using Social Feed Manager, an open source software that harvests social media data and related content from Twitter and other platforms. NOTE: 1) In accordance with Twitter API Terms, only Tweet IDs are provided as part of this dataset. 2) To recollect tweets based on the list of Tweet IDs contained in these datasets, you will need to use tweet 'rehydration' programs like Hydrator (https://github.com/DocNow/hydrator) or Python library Twarc (https://github.com/DocNow/twarc). 3) This dataset, like most datasets collected via the Twitter Search API, is a sample of the available tweets on this topic and is not meant to be comprehensive. Some COVID-related tweets might not be included in the dataset either because the tweets were collected using a standardized but intermittent (hourly) sampling protocol or because tweets used hashtags/keywords other than COVID (e.g., Coronavirus or #nCoV). 4) To broaden this sample, consider comparing/merging this dataset with other COVID-19 related public datasets such as: https://github.com/thepanacealab/covid19_twitter https://ieee-dataport.org/open-access/corona-virus-covid-19-tweets-dataset https://github.com/echen102/COVID-19-TweetIDs

This dataset offers a comprehensive collection of Telegram users' geolocation data, including IP addresses, with full user consent, covering 50,000 records. This data is specifically tailored for use in AI, ML, DL, and LLM models, as well as applications requiring Geographic Data and Social Media Data. The dataset provides critical geospatial information, making it a valuable resource for developing location-based services, targeted marketing strategies, and more.

What Makes This Data Unique? This dataset is unique due to its focus on geolocation data tied to Telegram users, a platform with a global user base. It includes IP to Geolocation Data, offering precise geospatial insights that are essential for accurate geographic analysis. The inclusion of user consent ensures that the data is ethically sourced and legally compliant. The dataset's broad coverage across various regions makes it particularly valuable for AI and machine learning models that require diverse, real-world data inputs.

Data Sourcing: The data is collected through a network of in-app tasks across different mini-apps within Telegram. Users participate in these tasks voluntarily, providing explicit consent to share their geolocation and IP information. The data is collected in real-time, capturing accurate geospatial details as users interact with various Telegram mini-apps. This method of data collection ensures that the information is both relevant and up-to-date, making it highly valuable for applications that require current location data.

Primary Use-Cases: This dataset is highly versatile and can be applied across multiple categories, including:

IP to Geolocation Data: The dataset provides precise mapping of IP addresses to geographical locations, making it ideal for applications that require accurate geolocation services. Geographic Data: The geospatial information contained in the dataset supports a wide range of geographic analysis, including regional behavior studies and location-based service optimization. Social Media Data: The dataset's integration with Telegram users' activities provides insights into social media behaviors across different regions, enhancing social media analytics and targeted marketing. Large Language Model (LLM) Data: The geolocation data can be used to train LLMs to better understand and generate content that is contextually relevant to specific regions. Deep Learning (DL) Data: The dataset is ideal for training deep learning models that require accurate and diverse geospatial inputs, such as those used in autonomous systems and advanced geographic analytics. Integration with Broader Data Offering: This geolocation dataset is a valuable addition to the broader data offerings from FileMarket. It can be combined with other datasets, such as web browsing behavior or social media activity data, to create comprehensive AI models that provide deep insights into user behaviors across different contexts. Whether used independently or as part of a larger data strategy, this dataset offers unique value for developers and data scientists focused on enhancing their models with precise, consented geospatial data.

Use our G2 dataset to collect product descriptions, ratings, reviews, and pricing information from the world's largest tech marketplace. You may purchase a full or partial dataset depending on your business needs. The G2 Software Products Dataset, with a focus on top-rated products, serves as a valuable resource for software buyers, businesses, and technology enthusiasts. This use case highlights products that have received exceptional ratings and positive reviews on the G2 platform, offering insights into customer satisfaction and popularity. For software buyers, this dataset acts as a trusted guide, presenting a curated selection of G2's top-rated software products, ensuring a higher likelihood of satisfaction with purchases. Businesses and technology professionals can leverage this dataset to identify popular and well-reviewed software solutions, optimizing their decision-making process. This use case emphasizes the dataset's utility for those specifically interested in exploring and acquiring top-rated software products from G2's Product Overview The G2 software products and reviews dataset offer a detailed and thorough overview of leading software companies. The dataset includes all major data points: Product descriptions Average rating (1-5) Sellers number of reviews Key features (highest and lowest rated) Competitors Website & social media links and more.

Overview

This dataset of medical misinformation was collected and is published by Kempelen Institute of Intelligent Technologies (KInIT). It consists of approx. 317k news articles and blog posts on medical topics published between January 1, 1998 and February 1, 2022 from a total of 207 reliable and unreliable sources. The dataset contains full-texts of the articles, their original source URL and other extracted metadata. If a source has a credibility score available (e.g., from Media Bias/Fact Check), it is also included in the form of annotation. Besides the articles, the dataset contains around 3.5k fact-checks and extracted verified medical claims with their unified veracity ratings published by fact-checking organisations such as Snopes or FullFact. Lastly and most importantly, the dataset contains 573 manually and more than 51k automatically labelled mappings between previously verified claims and the articles; mappings consist of two values: claim presence (i.e., whether a claim is contained in the given article) and article stance (i.e., whether the given article supports or rejects the claim or provides both sides of the argument).

The dataset is primarily intended to be used as a training and evaluation set for machine learning methods for claim presence detection and article stance classification, but it enables a range of other misinformation related tasks, such as misinformation characterisation or analyses of misinformation spreading.

Its novelty and our main contributions lie in (1) focus on medical news article and blog posts as opposed to social media posts or political discussions; (2) providing multiple modalities (beside full-texts of the articles, there are also images and videos), thus enabling research of multimodal approaches; (3) mapping of the articles to the fact-checked claims (with manual as well as predicted labels); (4) providing source credibility labels for 95% of all articles and other potential sources of weak labels that can be mined from the articles' content and metadata.

The dataset is associated with the research paper "Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims" accepted and presented at ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22).

The accompanying Github repository provides a small static sample of the dataset and the dataset's descriptive analysis in a form of Jupyter notebooks.

Options to access the dataset

There are two ways how to get access to the dataset:

1. Static dump of the dataset available in the CSV format
2. Continuously updated dataset available via REST API

In order to obtain an access to the dataset (either to full static dump or REST API), please, request the access by following instructions provided below.

References

If you use this dataset in any publication, project, tool or in any other form, please, cite the following papers:

@inproceedings{SrbaMonantPlatform,
  author = {Srba, Ivan and Moro, Robert and Simko, Jakub and Sevcech, Jakub and Chuda, Daniela and Navrat, Pavol and Bielikova, Maria},
  booktitle = {Proceedings of Workshop on Reducing Online Misinformation Exposure (ROME 2019)},
  pages = {1--7},
  title = {Monant: Universal and Extensible Platform for Monitoring, Detection and Mitigation of Antisocial Behavior},
  year = {2019}
}

@inproceedings{SrbaMonantMedicalDataset,
  author = {Srba, Ivan and Pecher, Branislav and Tomlein Matus and Moro, Robert and Stefancova, Elena and Simko, Jakub and Bielikova, Maria},
  booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22)},
  numpages = {11},
  title = {Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims},
  year = {2022},
  doi = {10.1145/3477495.3531726},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3477495.3531726},
}

Dataset creation process

In order to create this dataset (and to continuously obtain new data), we used our research platform Monant. The Monant platform provides so called data providers to extract news articles/blogs from news/blog sites as well as fact-checking articles from fact-checking sites. General parsers (from RSS feeds, Wordpress sites, Google Fact Check Tool, etc.) as well as custom crawler and parsers were implemented (e.g., for fact checking site Snopes.com). All data is stored in the unified format in a central data storage.


Ethical considerations

The dataset was collected and is published for research purposes only. We collected only publicly available content of news/blog articles. The dataset contains identities of authors of the articles if they were stated in the original source; we left this information, since the presence of an author's name can be a strong credibility indicator. However, we anonymised the identities of the authors of discussion posts included in the dataset.

The main identified ethical issue related to the presented dataset lies in the risk of mislabelling of an article as supporting a false fact-checked claim and, to a lesser extent, in mislabelling an article as not containing a false claim or not supporting it when it actually does. To minimise these risks, we developed a labelling methodology and require an agreement of at least two independent annotators to assign a claim presence or article stance label to an article. It is also worth noting that we do not label an article as a whole as false or true. Nevertheless, we provide partial article-claim pair veracities based on the combination of claim presence and article stance labels.

As to the veracity labels of the fact-checked claims and the credibility (reliability) labels of the articles' sources, we take these from the fact-checking sites and external listings such as Media Bias/Fact Check as they are and refer to their methodologies for more details on how they were established.

Lastly, the dataset also contains automatically predicted labels of claim presence and article stance using our baselines described in the next section. These methods have their limitations and work with certain accuracy as reported in this paper. This should be taken into account when interpreting them.


Reporting mistakes in the dataset
The mean to report considerable mistakes in raw collected data or in manual annotations is by creating a new issue in the accompanying Github repository. Alternately, general enquiries or requests can be sent at info [at] kinit.sk.

Dataset structure

Raw data

At first, the dataset contains so called raw data (i.e., data extracted by the Web monitoring module of Monant platform and stored in exactly the same form as they appear at the original websites). Raw data consist of articles from news sites and blogs (e.g. naturalnews.com), discussions attached to such articles, fact-checking articles from fact-checking portals (e.g. snopes.com). In addition, the dataset contains feedback (number of likes, shares, comments) provided by user on social network Facebook which is regularly extracted for all news/blogs articles.

Raw data are contained in these CSV files (and corresponding REST API endpoints):

• sources.csv
• articles.csv
• article_media.csv
• article_authors.csv
• discussion_posts.csv
• discussion_post_authors.csv
• fact_checking_articles.csv
• fact_checking_article_media.csv
• claims.csv
• feedback_facebook.csv

Note: Personal information about discussion posts' authors (name, website, gravatar) are anonymised.

Annotations

Secondly, the dataset contains so called annotations. Entity annotations describe the individual raw data entities (e.g., article, source). Relation annotations describe relation between two of such entities.

Each annotation is described by the following attributes:

1. category of annotation (`annotation_category`). Possible values: label (annotation corresponds to ground truth, determined by human experts) and prediction (annotation was created by means of AI method).
2. type of annotation (`annotation_type_id`). Example values: Source reliability (binary), Claim presence. The list of possible values can be obtained from enumeration in annotation_types.csv.
3. method which created annotation (`method_id`). Example values: Expert-based source reliability evaluation, Fact-checking article to claim transformation method. The list of possible values can be obtained from enumeration methods.csv.
4. its value (`value`). The value is stored in JSON format and its structure differs according to particular annotation type.

At the same time, annotations are associated with a particular object identified by:

1. entity type (parameter entity_type in case of entity annotations, or source_entity_type and target_entity_type in case of relation annotations). Possible values: sources, articles, fact-checking-articles.
2. entity id (parameter entity_id in case of entity annotations, or source_entity_id and target_entity_id in case of relation

The number of Twitter users in Indonesia was forecast to continuously increase between 2024 and 2028 by in total 1.4 million users (+6.14 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 24.25 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 Malaysia and Singapore.

90% of people aged 18-29 use social media in some form. 15% of people aged 23-38 admit that they are addicted to social media.

The set of these datasets are made to analyze ifnormation credibility in general (rumor and disinformation for English and French documents), and occuring on the social web. Target databases about rumor, hoax and disinformation helped to collect obviously misinformation. Some topic (with keywords) helps us to made corpora from the micrroblogging platform Twitter, great provider of rumors and disinformation. 1 corpus describes Texts from the web database about rumors and disinformation. 4 corpora from Social Media Twitter about specific rumors (2 in English, 2 in French). 4 corpora from Social Media Twitter randomly built (2 in English, 2 in French). 4 corpora from Social Media Twitter about specific rumors (2 in English, 2 in French). Size of different corpora : Social Web Rumorous corpus: 1,612 French Hollande Rumorous corpus (Twitter): 371 French Lemon Rumorous corpus (Twitter): 270 English Pin Rumorous corpus (Twitter): 679 English Swine Rumorous corpus (Twitter): 1024 French 1st Random corpus (Twitter): 1000 French 2st Random corpus (Twitter): 1000 English 3st Random corpus (Twitter): 1000 English 4st Random corpus (Twitter): 1000 French Rihanna Event corpus (Twitter): 543 English Rihanna Event corpus (Twitter): 1000 French Euro2016 Event corpus (Twitter): 1000 English Euro2016 Event corpus (Twitter): 1000 A matrix links tweets with most 50 frequent words Text data : _id : message id body text : string text data Matrix data : 52 columns (first column is id, second column is rumor indicator 1 or -1, other columns are words value is 1 contain or 0 does not contain) 11,102 lines (each line is a message) Hidalgo corpus: lines range 1:75 Lemon corpus : lines range 76:467 Pin rumor : lines range 468:656 swine : lines range 657:1311 random messages : lines range 1312:11103 Sample contains : French Pin Rumorous corpus (Twitter): 679 Matrix data : 52 columns (first column is id, second column is rumor indicator 1 or -1, other columns are words value is 1 contain or 0 does not contain) 189 lines (each line is a message)