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.

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).

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 social media 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 the social dynamics 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 conduc...

This dataset covers the use of social media to influence politics by promoting propaganda, advocating controversial viewpoints, and spreading disinformation. Influence efforts are defined as: (i) coordinated campaigns by a state, or the ruling party in an autocracy, to impact one or more specific aspects of politics at home or in another state, (ii) through media channels, including social media, by (iii) producing content designed to appear indigenous to the target state. Our data draw on more than 1000 media reports and 500 research articles/reports to identify IEs, track their progress, and classify their features. The data cover 78 foreign influence efforts (FIEs) and 25 domestic influence efforts (DIEs)—in which governments targeted their own citizens—against 51 different countries from 2011 through early-2021. The Influence Effort dataset measures covert information campaigns by state actors, facilitating research on contemporary statecraft.

Analysis of ‘Which Social Media Millennials Care About Most?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/which-social-media-millennials-care-about-moste on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This data was collected by Whatsgoodly, a millennial social polling company.

It was published by Brietbart on 3/17/17.

Link to article here: http://www.breitbart.com/tech/2017/03/17/report-snapchat-is-most-important-social-network-among-millennials/

This dataset was created by Adam Halper and contains around 500 samples along with Segment Type, Count, technical information and other features such as: - Segment Description - Answer - and more.

How to use this dataset

• Analyze Percentage in relation to Question
• Study the influence of Segment Type on Count
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Halper

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Unlock the power of ready-to-use data sourced from developer communities and repositories with Developer Community and Code Datasets.

Data Sources:

1. GitHub: Access comprehensive data about GitHub repositories, developer profiles, contributions, issues, social interactions, and more.

2. StackShare: Receive information about companies, their technology stacks, reviews, tools, services, trends, and more.

3. DockerHub: Dive into data from container images, repositories, developer profiles, contributions, usage statistics, and more.

Developer Community and Code Datasets are a treasure trove of public data points gathered from tech communities and code repositories across the web.

With our datasets, you'll receive:

• Usernames;
• Companies;
• Locations;
• Job Titles;
• Follower Counts;
• Contact Details;
• Employability Statuses;
• And More.

Choose from various output formats, storage options, and delivery frequencies:

• Get datasets in CSV, JSON, or other preferred formats.
• Opt for data delivery via SFTP or directly to your cloud storage, such as AWS S3.
• Receive datasets either once or as per your agreed-upon schedule.

Why choose our Datasets?

1. Fresh and accurate data: Access complete, clean, and structured data from scraping professionals, ensuring the highest quality.

2. Time and resource savings: Let us handle data extraction and processing cost-effectively, freeing your resources for strategic tasks.

3. Customized solutions: Share your unique data needs, and we'll tailor our data harvesting approach to fit your requirements perfectly.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is trusted by Fortune 500 companies and adheres to GDPR and CCPA standards.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Empower your data-driven decisions with Oxylabs Developer Community and Code Datasets!

As of February 2022, mobile video apps YouTube and TikTok had the largest number of trackers among the examined social media apps. However, while YouTube was reported to be able to use 10 first-party trackers and four third-party trackers, TikTok was reported to have the ability to use 13 different third-party app trackers. First-party data trackers can follow user activity and store information on the visited app, while third-party trackers send information to companies and external parties. Third-party trackers are usually used to collect information on users in order to offer relevant advertising.

Dataset ini merupakan hasil dari scraping pada media sosial twitter dengan menggunakan aplikasi twint yang ditujukan pada hashtag #IndonesiaHumanRightsSOS. Scraping data dilakukan untuk cuitan yang dibuat dari tanggal 18 Desember 2020 10:59 AM s/d 19 Desember 2020 23:18 PM.

Pada dataset mengandung 106.903 Row data dengan informasi terkait: User ID, Username, Twitter Name,Tweets, dsb.

Selain itu dilampirkan juga contoh data yang telah dianalisis berupa wordcloud,username cloud, 100 most used word & most active username.

-

This dataset is the result of scraping on social media twitter using the twint application aimed at the hashtag #IndonesiaHumanRightsSOS. Data scraping is done for tweets made from December 18 2020 10:59 AM to December 19 2020 23:18 PM.

The dataset contains 106,903 rows of data with related information: User ID, Username, Twitter Name, Tweets, etc.

Also there is an example of the data that has been analyzed in the form of wordcloud, username cloud, 100 most used words & most active username.

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

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.

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 files contain the raw version that comprises 52,868 Tweet IDs (that correspond to the same number of Tweets) and the cleaned and preprocessed version that contains 46,208 unique Tweet IDs. 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 7 .txt files. The raw version of this dataset comprises 6 .txt files (TweetIDs_Corona Virus.txt, TweetIDs_Corona.txt, TweetIDs_Coronavirus.txt, TweetIDs_Covid.txt, TweetIDs_Omicron.txt, and TweetIDs_SARS CoV2.txt) that contain Tweet IDs grouped together based on certain synonyms or terms that were used to refer to online learning and the Omicron variant of COVID-19 in the respective tweets. The cleaned and preprocessed version of this dataset is provided in the .txt file - TweetIDs_Duplicates_Removed.txt. 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 the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweetsr) may be used. The list of all the synonyms or terms that were used for the dataset development is as follows: 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 A description of the dataset files is provided below: TweetIDs_Corona Virus.txt – Contains 321 Tweet IDs correspond to tweets that comprise the keywords – "corona virus" and one or more keywords/terms that refer to online learning TweetIDs_Corona.txt – Contains 1819 Tweet IDs correspond to tweets that comprise the keyword – "corona" or "coronaoutbreak" and one or more keywords/terms that refer to online learning TweetIDs_Coronavirus.txt – Contains 1429 Tweet IDs correspond to tweets that comprise the keywords – "coronavirus" or "coronaviruspandemic" and one or more keywords/terms that refer to online learning TweetIDs_Covid.txt – Contains 41088 Tweet IDs correspond to tweets that comprise the keywords – "COVID" or "COVID19" or "COVID-19" and one or more keywords/terms that refer to online learning TweetIDs_Omicron.txt – Contains 8198 Tweet IDs correspond to tweets that comprise the keywords – "omicron" or "omicron variant" and one or more keywords/terms that refer to online learning TweetIDs_SARS CoV2.txt – Contains 13 Tweet IDs correspond to tweets that comprise the keyword – "SARS-CoV-2" and one or more keywords/terms that refer to online learning TweetIDs_Duplicates_Removed.txt - A collection of 46208 unique Tweet IDs from all the 6 .txt files mentioned above after...

Overview

This data set consists of links to social network items for 34 different forensic events that took place between August 14th, 2018 and January 06th, 2021. The majority of the text and images are from Twitter (a minor part is from Flickr, Facebook and Google+), and every video is from YouTube.

Data Collection

We used Social Tracker (https://github.com/MKLab-ITI/mmdemo-dockerized), along with the social medias' APIs, to gather most of the collections. For a minor part, we used Twint (https://github.com/twintproject/twint). In both cases, we provided keywords related to the event to receive the data.

It is important to mention that, in procedures like this one, usually only a small fraction of the collected data is in fact related to the event and useful for a further forensic analysis.

Content

We have data from 34 events, and for each of them we provide the files:

items_full.csv: It contains links to any social media post that was collected.

images.csv: Enlists the images collected. In some files there is a field called "ItemUrl", that refers to the social network post (e.g., a tweet) that mentions that media.

video.csv: Urls of YouTube videos that were gathered about the event.

video_tweet.csv: This file contains IDs of tweets and IDs of YouTube videos. A tweet whose ID is in this file has a video in its content. In turn, the link of a Youtube video whose ID is in this file was mentioned by at least one collected tweet. Only two collections have this file.

description.txt: Contains some standard information about the event, and possibly some comments about any specific issue related to it.

In fact, most of the collections do not have all the files above. Such an issue is due to changes in our collection procedure throughout the time of this work.

Events

We divided the events into six groups. They are,

1. Fire

• Devastating fire is the main issue of the event, therefore most of the informative pictures show flames or burned constructions

• 14 Events

2. Collapse

• Most of the relevant images depict collapsed buildings, bridges, etc. (not caused by fire).

• 5 Events

3. Shooting

• Likely images of guns and police officers. Few or no destruction of the environment.

• 5 Events

4. Demonstration

• Plethora of people on the streets. Possibly some problem took place on that, but in most cases the demonstration is the actual event.

• 7 Events

5. Collision

• Traffic collision. Pictures of damaged vehicles on an urban landscape. Possibly there are images with victims on the street.

• 1 Event

6. Flood

• Events that range from fierce rain to a tsunami. Many pictures depict water.

• 2 Events

We enlist the events in the file recod-ai-events-dataset-list.pdf

Media Content

Due to the terms of use from the social networks, we do not make publicly available the texts, images and videos that were collected. However, we can provide some extra piece of media content related to one (or more) events by contacting the authors.

Funding

DéjàVu thematic project, São Paulo Research Foundation (grants 2017/12646-3, 2018/18264-8 and 2020/02241-9)

This dataset comprises a set of Twitter accounts in Singapore that are used for social bot profiling research conducted by the Living Analytics Research Centre (LARC) at Singapore Management University (SMU). Here a bot is defined as a Twitter account that generates contents and/or interacts with other users automatically (at least according to human judgment). In this research, Twitter bots have been categorized into three major types:

Broadcast bot. This bot aims at disseminating information to general audience by providing, e.g., benign links to news, blogs or sites. Such bot is often managed by an organization or a group of people (e.g., bloggers). Consumption bot. The main purpose of this bot is to aggregate contents from various sources and/or provide update services (e.g., horoscope reading, weather update) for personal consumption or use. Spam bot. This type of bots posts malicious contents (e.g., to trick people by hijacking certain account or redirecting them to malicious sites), or promotes harmless but invalid/irrelevant contents aggressively.

This categorization is general enough to cater for new, emerging types of bot (e.g., chatbots can be viewed as a special type of broadcast bots). The dataset was collected from 1 January to 30 April 2014 via the Twitter REST and streaming APIs. Starting from popular seed users (i.e., users having many followers), their follow, retweet, and user mention links were crawled. The data collection proceeds by adding those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. Using this procedure, a total of 159,724 accounts have been collected. To identify bots, the first step is to check active accounts who tweeted at least 15 times within the month of April 2014. These accounts were then manually checked and labelled, of which 589 bots were found. As many more human users are expected in the Twitter population, the remaining accounts were randomly sampled and manually checked. With this, 1,024 human accounts were identified. In total, this results in 1,613 labelled accounts. Related Publication: R. J. Oentaryo, A. Murdopo, P. K. Prasetyo, and E.-P. Lim. (2016). On profiling bots in social media. Proceedings of the International Conference on Social Informatics (SocInfo’16), 92-109. Bellevue, WA. https://doi.org/10.1007/978-3-319-47880-7_6

The number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, 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.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).

Global Professional Profiles Dataset covers over 785M+ professional profile records.

We do 150M+ updates a month (most updates of any vendor), and deliver the data as JSONL flat-files, or PostgreSQL database delivery.

We track every public profile and capture publicly available info on all these records.

| Volume and Stats | - 785M+ total records (and growing). - 150M+ updates/month, and growing even more! Most updates/month than anyone else! - First-party data curation — we power the world's best sales and recruitment platforms that build on top of this data. - Delivery frequency is hourly (fastest in the industry today). - Additional datapoints and linkages available. - Delivery formats: JSONL, PostgreSQL, CSV, S3, BigQuery, Redshift

| Datapoints | - Over 150+ unique datapoints available! - Key fields like Current Title, Current Company, Work History, Educational Background, Certificates, Patents, People in the Network, and more. - Unique linkage data to other social networks or contact data available.

| Use Cases |

Sales Platforms, ABM Vendors, Intent Data Companies, AdTech and more: - Build the best end-customer experience with our people feed powering your solution! - Be the first to know when someone changes jobs, and share that with end-customers. - Industry-leading data accuracy - Connect our professional records to your existing database, as well as find new connections to other social networks, and contact data. - Hashed records also available for advertising use-cases.

Venture Capital and Private Equity: - Track every company and employee that has a publicly available profile. - Keep track of your portfolio founders, employees and ex-employees, and be the first to know when they move or startup. Also maintain the anti-portfolio list of companies, founders and key employees. - Keep an eye on the pulse by following the most influential people and human capital in industries and segments you care about. - Provide your portfolio companies with access to the best data for recruitment and talent sourcing. - Review departmental headcount growth of private companies and benchmark their strength against competitors.

HR Tech, ATS Platforms, Recruitment Solutions, as well as Executive Search Agencies: - Build products for industry-specific and industry-agnostic candidate recruiting platforms. - Track person job changes and immediately refresh profiles to avoid stale data. - Identify ideal candidates through work experience and education history. - Keep ATS systems and candidate profiles constantly updated. - Link data from this dataset into GitHub, Linktree, Behance, Dribble and other social networks.

| Delivery Options | - Flat files via S3 or GCP - PostgreSQL Shared Database - PostgreSQL Managed Database - REST API - Snowflake - Other options available at request, depending on scale required

| Other key features |
- Over 65M US Company Profiles.
- 150+ Data Fields (available upon request)
- Free data samples, and evaluation.

Tags: Professionals Data, People Data, Work Experience History, Education Data, Employee Data, Workforce Intelligence, Identity Resolution, Talent, Candidate Database, Sales Database, Contact Data, Account Based Marketing, Intent Data.

Data from a survey of 35 students in a Norwegian high school class (mostly 17-year-olds) on 1 June 2016, by Jill Walker Rettberg, Professor of Digital Culture, University of Bergen. See http://jilltxt.net/?p=4505 for details.I gathered this data for a project on Snapchat narratives. I want to understand how stories are told on Snapchat. My main method is textual analysis, and this data is simply intended to give me a better idea of whether users actually watch Live stories and other stories, and whether they make them - and to give me some ideas for where to dig deeper as I continue researching stories. I plan to visit more high schools to get more responses, but since Snapchat's interface changed in 2016, the results won't be directly comparable.Importantly, this data was collected BEFORE the update in mid-June that made Live Stories and Discover channels look the same. I assume the numbers will change with this interface change.They survey was conducted in Norwegian. One of the images in the fileset shows the survey as administered. The other image shows a translation into English. I have translated the comments as directly as possible before transcribing them into the spreadsheet. The image of a filled out survey is a translation of the Norwegian survey the students actually filled out. The original Google spreadsheet is at https://docs.google.com/spreadsheets/d/13Z4ZdeoHAeI9zYqNw6Oa7Qs64g3873dAYZTcCWH1tyo/edit#gid=1943894532.

Currently, 2.7 billion people use at least one of the Facebook-owned social media platforms – Facebook, WhatsApp, and Instagram. Previous research investigating individual differences between users and non-users of these platforms has typically focused on one platform. However, individuals typically use a combination of Facebook-owned platforms. Therefore, we aim (1) to identify the relative prevalence of different patterns of social media use, and (2) to evaluate potential between-group differences in the distributions of age, gender, education, and Big Five personality traits. Data collection was performed using a cross-sectional design. Specifically, we administered a survey assessing participants’ demographic variables, current use of Facebook-owned platforms, and Big Five personality traits. In N = 3003 participants from the general population (60.67% females; mean age = 35.53 years, SD = 13.53), WhatsApp emerged as the most widely used application in the sample, and hence, has the strongest reach. A pattern consisting of a combined use of WhatsApp and Instagram appeared to be most prevalent among the youngest participants. Further, individuals using at least one social media platform were generally younger, more often female, and more extraverted than non-users. Small differences in Conscientiousness and Neuroticism also emerged across groups reporting different combinations of social media use. Interestingly, when examined as control variables, we found demographic characteristics partially accounted for differences in broad personality factors and facets across different patterns of social media use. Our findings are relevant to researchers carrying out their studies via social media platforms, as sample characteristics appear to be different depending on the platform used.