Facebook is fast approaching 3 billion monthly active users. That’s about 36% of the world’s entire population that log in and use Facebook at least once a month.

Facebook is becoming an essential tool for more than just family and friends. Discover how Cheltenham Township (USA), a diverse community just outside of Philadelphia, deals with major issues such as the Bill Cosby trial, everyday traffic issues, sewer I/I problems and lost cats and dogs. And yes, theft.

Communities work when they're connected and exchanging information. What and who are the essential forces making a positive impact, and when and how do conversational threads get directed or misdirected?

Use Any Facebook Public Group

You can leverage the examples here for any public Facebook group. For an example of the source code used to collect this data, and a quick start docker image, take a look at the following project: facebook-group-scrape.

Data Sources

There are 4 csv files in the dataset, with data from the following 5 public Facebook groups:

• Unofficial Cheltenham Township
• Elkins Park Happenings!
• Free Speech Zone
• Cheltenham Lateral Solutions
• Cheltenham Township Residents

post.csv

These are the main posts you will see on the page. It might help to take a quick look at the page. Commas in the msg field have been replaced with {COMMA}, and apostrophes have been replaced with {APOST}.

• gid Group id (5 different Facebook groups)
• pid Main Post id
• id Id of the user posting
• name User's name
• timeStamp
• shares
• url
• msg Text of the message posted.
• likes Number of likes

comment.csv

These are comments to the main post. Note, Facebook postings have comments, and comments on comments.

• gid Group id
• pid Matches Main Post identifier in post.csv
• cid Comment Id.
• timeStamp
• id Id of user commenting
• name Name of user commenting
• rid Id of user responding to first comment
• msg Message

like.csv

These are likes and responses. The two keys in this file (pid,cid) will join to post and comment respectively.

• gid Group id
• pid Matches Main Post identifier in post.csv
• cid Matches Comments id.
• response Response such as LIKE, ANGRY etc.
• id The id of user responding
• name Name of the user responding

member.csv

These are all the members in the group. Some members never, or rarely, post or comment. You may find multiple entries in this table for the same person. The name of the individual never changes, but they change their profile picture. Each profile picture change is captured in this table. Facebook gives users a new id in this table when they change their profile picture.

• gid Group id
• id Id of the member
• name Name of the member
• url URL of the member

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

An anonymised dataset of public Facebook content posted during March - April 2024. We first looked for public Facebook accounts or keywords associated with Myanmar, Cambodian, and Thai authoritarian content, including pro-regime propaganda, anti-dissident disinformation, promotion of anti-dissident violence, or anti-dissident doxing content. We used CrowdTangle – Meta's own social media monitoring tool – to capture content on a daily basis based on our list of authoritarian accounts and keywords. This dataset includes random samples of 2000 Myanmar posts, 2000 Thai posts, and 2000 Cambodian posts with qualitative coding.

Purpose For the purpose of informing tobacco intervention programs, this dataset was created and used to explore how online social networks of smokers differed from those of nonsmokers. The study was a secondary analysis of data collected as part of a randomized control trial conducted within Facebook. (See "Other References" in "Metadata" for parent study information.) Basic description of 4 anonymized data files of study participants. fbr_friends: Anonymized Facebook friends networks, basic ego demographics, basic ego social media activity fbr_family: Anonymized Facebook family networks, basic ego demographics, basic ego social media activity fbr_photos: Anonymized Facebook photo networks, basic ego demographics, basic ego social media activity fbr_groups: Anonymized Facebook group networks, basic ego demographics, basic ego social media activity Each network comprises the ego, the ego's first degree connections, and the (second degree) connections between the ego's friends. Missing data and users who did not have friend, family, photo, or group networks were cleaned from the data beforehand. Each data file contains the following columns of data, taken with participant knowledge and consent participant_id: Nonidentifying ids assigned to different study participants. is_smoker: Binary value (0,1) that takes on the value 1 if participant was a smoker and 0 otherwise. gender: One of three categories: male, female, or blank, which signified Other (different from missing data). country: One of four categories: Canada (ca), US (us), Mexico (mx), or Other (xx). likes_count: Numeric data indicating number of Facebook likes the participant had made up to the date the data was collected. wall_count: Numeric data indicating number of Facebook wall posts the participant had made up to the date the data was collected. t_count_page_views: Numeric data indicating number of pages participant had visited in the UbiQUITous app up to the date the data was collected. yearsOld: Numeric data indicating age in years of the participant; right censored at 90 years for data anonymity. vertices: Number of people in the participant's network. edges: Number of connections between people in the network. density: The portion of potential connections in a network that are actual connections; a network-level metric; calculated after removing ego and isolates. mean_betweenness_centrality: An average of the relative importance of all individuals within their own network; a network-level metric; calculated after removing ego and isolates. transitivity: The extent to which the relationship between two nodes in a network that are connected by an edge is transitive (calculated as the number of triads divided by all possible connections); a network-level metric; calculated after removing ego and isolates. mean_closeness: Average of how closely associated members are to one another; a network-level metric; calculated after removing ego and isolates. isolates2: Number of individuals with no connections other than to the ego; a network-level metric. diameter3: Maximum degree of separation between any two individuals in the network; a network-level metric; calculated after removing ego and isolates. clusters3: Number of subnetworks; a network-level metric; calculated after removing ego and isolates. communities3: Number of groups, sorted to increase dense connections within the group and decrease sparse connections outside it (i.e., to maximize modularity); a network-level metric; calculated after removing ego and isolates. modularity3: The strength of division of a network into communities (calculated as the fraction of ties between community members in excess of the expected number of ties within communities if ties were random); a network-level metric. Detailed information on network metrics in the associated manuscript: "An exploration of the Facebook social networks of smokers and non-smokers" by Fu, L, Jacobs MA, Brookover J, Valente TW, Cobb NK, and Graham AL.

This data describes the use of the social media platform Facebook (http://www.facebook.com) by five (5) Massachusetts police departments over a three (3) month period from May 1st through July 31st, 2014. The five (5) police departments represented the towns/cities of Billerica, Burlington, Peabody, Waltham, and Wellesley. In addition to portraying these local trends, they demonstrate a methodology for systematically measuring social media use by government agencies or other organizations. This data was taken directly from Facebook using API’s provided by Facebook. The data includes all “wall posts” made by the representative police departments during this time period and includes data variables such as the text of the posting, the number of “likes” and “shares” (likes/shares represent features available on the Facebook social media platform), information about who performed the “like” or “share”, and comments others made in response to the “wall post”. There are 5 data files, one for each town represented. The number of variables vary per town depending on the post with the maximum number of certain features found in the row (for example, the top number of comments for one police department could be 20 while another could be 30 – the latter dataset would contain 10 more columns per row to account for the maximum possible). The data collected included the time from May 1st, 2014 through July 31st, 2014.

How does Facebook always seems to know what the next funny video should be to sustain your attention with the platform? Facebook has not asked you whether you like videos of cats doing something funny: They just seem to know. In fact, FaceBook learns through your behavior on the platform (e.g., how long have you engaged with similar movies, what posts have you previously liked or commented on, etc.). As a result, Facebook is able to sustain the attention of their user for a long time. On the other hand, the typical mHealth apps suffer from rapidly collapsing user engagement levels. To sustain engagement levels, mHealth apps nowadays employ all sorts of intervention strategies. Of course, it would be powerful to know—like Facebook knows—what strategy should be presented to what individual to sustain their engagement. To be able to do that, the first step could be to be able to cluster similar users (and then derive intervention strategies from there). This dataset was collected through a single mHealth app over 8 different mHealth campaigns (i.e., scientific studies). Using this dataset, one could derive clusters from app user event data. One approach could be to differentiate between two phases: a process mining phase and a clustering phase. In the process mining phase one may derive from the dataset the processes (i.e., sequences of app actions) that users undertake. In the clustering phase, based on the processes different users engaged in, one may cluster similar users (i.e., users that perform similar sequences of app actions).

List of files

0-list-of-variables.pdf includes an overview of different variables within the dataset. 1-description-of-endpoints.pdf includes a description of the unique endpoints that appear in the dataset. 2-requests.csv includes the dataset with actual app user event data. 2-requests-by-session.csv includes the dataset with actual app user event data with a session variable, to differentiate between user requests that were made in the same session.

Here we document the dataset and the programming code used in our paper "Modelling and Predicting Online Vaccination Views using Bow-tie Decomposition", Royal Society Open Science (2024). In this paper, we investigate a temporal dataset that describes the Facebook vaccination views campaign in network representations, involving nearly 100 million users on Facebook from across countries, continents and languages.

It was provided by Johnson et al. (2020) and Illari et al. (2022) spanning different time periods. The former contains two snapshots in February 2019 and October 2019 (before the COVID-19), which we study in our main paper. The latter contains another two snapshots in November 2019 and December 2020 (at the initial stage of the COVID-19), which we study in Supplementary Material. Both of them are openly available and documented in two papers separately of different formats (PDF & Excel), thus requiring intensive preprocessing.

To make it easier for other researchers to use this dataset, here we reorganise both versions of this dataset in gpickle format (can be directly imported as attributed networks using Python) and in CSV format (for general use of the dataset).

README file

Data Set Title: “PERCEIVE - ENGAGING THE PEOPLE’: IS SOCIAL MEDIA COVERAGE OF EU POLICY ASSOCIATED WITH PUBLIC SUPPORT FOR EUROPEAN INTEGRATION?”

Data Set Authors:

Vitaliano Barberio (Wirtschaftsuniversität Wien), ORCID http://orcid.org/0000-0002-2615-5006;

Luca Pareschi (Università di Roma Tor Vergata), ORCID http://orcid.org/0000-0002-4402-9329;

Data Set Contributors:

Ines Kuric (Wirtschaftsuniversität Wien);

Edoardo Mollona (Università di Bologna), ORCID http://orcid.org/0000-0001-9496-8618.

Markus Höllerer (Wirtschaftsuniversität Wien); http://orcid.org/0000-0003-2509-2696

Data Set Contact Person:

Luca Pareschi (Università di Roma Tor Vergata), ORCID http://orcid.org/0000-0002-4402-9329;

luca.pareschi@uniroma2.it .

Data Set License: this data set is distributed under a Creative Commons Attribution (CC BY) 4.0 International license

Publication Year: 2021

Project Info: PERCEIVE (Perception and Evaluation of Regional and Cohesion Policies by Europeans and Identification with the Values of Europe), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 693529; https://www.perceiveproject.eu/.

Data set Contents

The data set consists of:

1 README file

6 textual qualitative file saved in .txt format

“stoplist_file_[nation].txt”

12 textual quantitative file saved in .txt format

“[source]-keys.txt”: 6 files

2 excel quantitative files saved in .xlsx format

“SentimentFB.xlsx”

“topics_prevalence_and_clustering.xlsx”

Data set Documentation

Abstract

This data set contains the underlying data of the paper “’ENGAGING THE PEOPLE’: IS SOCIAL MEDIA COVERAGE OF EU POLICY ASSOCIATED WITH PUBLIC SUPPORT FOR EUROPEAN INTEGRATION?”.

Data openly available within this dataset are a subset of the two following data sets, which contains all the relevant data of Work Package 3 and Work Package 5 of PERCEIVE project:

Data set: “PERCEIVE: WP3: Effectiveness of communication strategies of EU projects” https://doi.org/10.5281/zenodo.3371133

Data set: “PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels” https://doi.org/10.5281/zenodo.3371174

For the paper we collected Facebook posts referred to EU CP policies. We don’t have the permission to share these data (as they are protected by copyright), but all the sources are described in Deliverable 5.2, which is public (see http://doi.org/10.6092/unibo/amsacta/5726 or http://doi.org/10.5281/zenodo.1318184). We analyzed the textual content of data to construct a database of discursive topics in Task5.4. Data set includes the results of topic modeling and of a sentiment analysis performed on the Facebook homepages of Local Management Authorities (LMA) of PERCEIVE case study regions.

Content of the files:

1 sub-folder, named “A_Stopword”, which contains all the stopword lists used for performing Topic Modeling. These are 6 .txt files, one for each language: Austrian, Italian, Polish, Romanian, Spanish, Swedish (“stoplist_file_[nation].txt”).

1 sub-folder which contain the Topic Modeling results for Facebook profiles of the Local Managing Authorities for Austria, Italy, Poland, Romania, Spain, and Sweden (sub-folder “B_Facebook”, 12 .txt files). For each case, a file “[source]-keys.txt” lists the 100 most important words for each topic, while a file “[source]-composition.txt” details the topic composition of each textual source. These files were obtained through Mallet software[1].

File “SentimentFB.xlsx” contains data regarding the sentiment analysis for contents on Facebook homepages of Local Managing Authorities. The first column indicates the country, as well as row labels (see below). Columns 2-21 indicate the number id of the topics for each topic model (national level). The three rightmost columns of the file represent respectively a) the name of the lexicon used to detect sentiment orientation (i.e. “VADER”); c) the average sentiment score for positive, neutral and average words for each lexicon and each country; and c) the sentiment score across all topics in a country.

File “topics_prevalence_and_clustering.xlsx” contains data regarding the three clusters of topics analyzed in the paper. The first column represents the ID of each topic; the second column reports the cluster of each topic; the third and the fourth columns report the average prevalence of each topic (rows) in posts and comments, respectively. As these data refer to a regional case study, these columns refer the first region for each country; the sixth and the seventh columns report the average prevalence of each topic (rows) in posts and comments for the second region analyzed (only for those countries where we analyzed two regions); the eighth and ninth columns reports the average prevalence of topics and comments, respectively, for each country; and finally the tenth column reports the country to which data in the previous two columns are referred.

[1] McCallum, Andrew Kachites. "MALLET: A Machine Learning for Language Toolkit."http://mallet.cs.umass.edu. 2002.

The number of Facebook users in Vietnam was forecast to increase between 2024 and 2028 by in total 5.6 million users (+10.2 percent). This overall increase does not happen continuously, notably not in 2027 and 2028. The Facebook user base is estimated to amount to 60.54 million users in 2028. Notably, the number of Facebook users of was continuously increasing over the past years.User figures, shown here regarding the platform facebook, 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).Find more key insights for the number of Facebook users in countries like Indonesia and Malaysia.

Drawing on evidence from the 2011 Egyptian uprising, we demonstrate how the use of two social media platforms – Facebook and Twitter – contributed to a discrete mobilizational outcome: the staging of a successful first protest in a revolutionary cascade, or, what we call "first mover mobilization." Specifically, we argue that these two platforms facilitated the staging of a large, nationwide, and seemingly leaderless protest on January 25, 2011, which signaled to hesitant but sympathetic Egyptians that a revolution might be in the making. Using qualitative and quantitative evidence, including interviews, social media data, and surveys, we analyze three mechanisms that linked these platforms to the success of the January 25 protest: 1) protester recruitment, 2) protest planning and coordination, and 3) live updating about protest logistics. The paper not only contributes to debates about the role of the Internet in the Arab Spring and other recent waves of mobilization, but also demonstrates how scholarship on the Internet in politics might move toward making more discrete, empirically grounded causal claims.

Alcohol use and work status by social media use (N = 5874).

Task Oriented Parsing v2 (TOPv2) representations for intent-slot based dialog systems.

Provided under the CC-BY-SA license. Please cite the accompanying paper when using this dataset -

@inproceedings{chen-etal-2020-low-resource, title={Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing}, author={Xilun Chen and Asish Ghoshal and Yashar Mehdad and Luke Zettlemoyer and Sonal Gupta}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year={2020}, publisher = "Association for Computational Linguistics" }

CHANGELOG:
03/10/2021 (V1.1): Added the low-resource splits used in the paper.
09/18/2020 (V1.0): Initial release.

TOPv2 is a multi-domain task-oriented semantic parsing dataset. It is an extension to the TOP dataset (http://fb.me/semanticparsingdialog) with 6 additional domains and 137k new samples.

In total, TOPv2 has 8 domains (alarm, event, messaging, music, navigation, reminder, timer, weather) and 180k samples randomly split into train, eval, and test sets for each domain. Please refer to the paper for more data statistics. Note: As TOPv2 data is provided on a per-domain basis, the UNSUPPORTED utterances in the original TOP dataset were removed as they could not be mapped to any domain.

The training, evaluation and test sets for each domain are provided as tab-separated value (TSV) files with file names of "domain_split.tsv". The first row of each file contains the column headers, while each following row is of the format: domain

e.g. event

The low-resource splits used in our experiments are provided in the low_resource_splits subdirectory, including training and validation sets from the reminder and weather domains under 10, 25, 50, 100, 250, 500 and 1000 SPIS.

The number of Facebook users in Indonesia was forecast to continuously decrease between 2024 and 2028 by in total 20 million users (-11.04 percent). According to this forecast, in 2028, the Facebook user base will have decreased for the fifth consecutive year to 161.16 million users. User figures, shown here regarding the platform facebook, 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).Find more key insights for the number of Facebook users in countries like Thailand and Vietnam.

The number of Facebook users in Europe was forecast to continuously increase between 2024 and 2028 by in total 15.5 million users (+3.91 percent). According to this forecast, in 2028, the Facebook user base will have increased for the sixth consecutive year to 412.26 million users. User figures, shown here regarding the platform facebook, 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).Find more key insights for the number of Facebook users in countries like South America and North America.

The Cardiff Travel Survey is a longitudinal survey that aims to (a) establish current and previous (before the coronavirus outbreak) travel habits; (b) explore how travel-related attitudes, social norms and perceptions change over time; and (c) examine the interplay between individual (perceptual) and environmental (infrastructural) factors in travel mode choice, in particular in relation to the uptake of active travel such as walking and cycling in the City of Cardiff, Wales. The Cardiff Travel Survey 2021 (Wave 1) is the first wave of data collected in 2021 (n=731) by the Centre for Climate Change and Social Transformations (CAST) and is the first of a longitudinal series of surveys to be held annually. This first wave of the Cardiff Travel Survey can be used as a baseline for follow-up surveys as well as possible future information campaigns and interventions. Data for the Cardiff Travel Survey 2021 were collected between 19 May 2021 and 9 July 2021. Participants were recruited through posts on social media, such as Facebook® and Twitter®. Invitations were posted on CAST and investigator accounts as well as local Facebook® pages (e.g., Cardiff Students Postgraduate Network and neighbourhood-specific pages). The survey was hosted on the Qualtrics online survey platform and available in both English and Welsh. Inclusion criteria were that participants had to be at least 18 years of age and live in or travel regularly to Cardiff. The English version of the survey was completed by 690 respondents and the Welsh version by 41 respondents. Incomplete responses (n=56), defined as those without any answers beyond socio-demographic information, were removed from the dataset. This left a final sample of 675 adults. Participants were asked to create a unique code that can be used match this survey to the next surveys without knowing their identity. Main topic areas of the questionnaire are: Demographics, Travel behaviours - before coronavirus outbreak, Travel behaviours - current, Physical activity, Physical health and mental wellbeing, Perceptions of infrastructure and environmental quality, Attitudes to active travel, Social norms, Support for transport policies, and Unique ID.The Centre for Climate Change Transformations (C3T) will be a global hub for understanding the profound changes required to address climate change. At its core, is a fundamental question of enormous social significance: how can we as a society live differently - and better - in ways that meet the urgent need for rapid and far-reaching emission reductions? While there is now strong international momentum on action to tackle climate change, it is clear that critical targets (such as keeping global temperature rise to well within 2 degrees Celsius relative to pre-industrial levels) will be missed without fundamental transformations across all parts of society. C3T's aim is to advance society's understanding of how to transform lifestyles, organisations and social structures in order to achieve a low-carbon future, which is genuinely sustainable over the long-term. Our Centre will focus on people as agents of transformation in four challenging areas of everyday life that impact directly on climate change but have proven stubbornly resistant to change: consumption of goods and physical products, food and diet, travel, and heating/cooling. We will work across multiple scales (individual, community, organisational, national and global) to identify and experiment with various routes to achieving lasting change in these challenging areas. In particular, we will test how far focussing on 'co-benefits' will accelerate the pace of change. Co-benefits are outcomes of value to individuals and society, over and above the benefits from reducing greenhouse gas emissions. These may include improved health and wellbeing, reduced waste, better air quality, greater social equality, security, and affordability, as well as increased ability to adapt and respond to future climate change. For example, low-carbon travel choices (such as cycling and car sharing) may bring health, social and financial benefits that are important for motivating behaviour and policy change. Likewise, aligning environmental and social with economic objectives is vital for behaviour and organisational change within businesses. Our Research Themes recognise that transformative change requires: inspiring yet workable visions of the future (Theme 1); learning lessons from past and current societal shifts (Theme 2); experimenting with different models of social change (Theme 3); together with deep and sustained engagement with communities, business and governments, and a research culture that reflects our aims and promotes action (Theme 4). Our Centre integrates academic knowledge from disciplines across the social and physical sciences with practical insights to generate widespread impact. Our team includes world-leading researchers with expertise in climate change behaviour, choices and governance. We will use a range of theories and research methods to fill key gaps in our understanding of transformation at different spatial and social scales, and show how to target interventions to impactful actions, groups and moments in time. Participants for the Cardiff Travel Survey (Wave 1) were recruited through posts on social media, such as Facebook and Twitter. Invitations were posted on CAST, Cardiff University, and investigator accounts as well as local Facebook pages (e.g., Cardiff Students Postgraduate Network and neighbourhood-specific pages). The survey was hosted on the Qualtrics online survey platform and available in both English and Welsh.

The increasing popularity and use of digital platforms and social media such as WhatsApp, Facebook, YouTube and Instagram are opening up new opportunities for children, young people and adults to pursue cultural interests or to stage themselves aesthetically. If we focus on young people between the ages of 12 and 19, a number of studies on media use show that YouTube in particular has become the leading medium for this age group. Given the growth in importance of this web video platform, questions arise about the receptive and productive content of experience and the significance of cultural content and practices. Furthermore, there are hardly any findings on the extent to which YouTube stimulates young people to engage in cultural activities and self-organized learning processes. The sample is composed of n=818 adolescents aged 12-19 years. The selection of the study units was based on a quota procedure. The adolescent target subjects were recruited via the IFAK interviewer staff according to predefined quotas for age, gender, region, place size class, type of school attended (for students), and occupation (for non-students). The characteristics "age and gender" and "region and place size" were crossed or combined with each other to produce as accurate a representation of the population as possible. The characteristic "migration background" was not used as a quota characteristic. The specifications for this are based on the latest data from the Federal Statistical Office and ma Radio 2018 II. The structural composition of the sample corresponds to the data for the population according to the characteristics mentioned. The study was conducted as a face-to-face oral survey. The answers of the young people were recorded by an interviewer on a laptop via a corresponding survey program. 111 face-to-face interviewers from the in-house interviewing staff, who have experience in interviewing children and adolescents, were used. The predefined questionnaire was binding for all interviewers with regard to the wording and sequence of questions. The maximum number of interviews per interviewer was n=10. Each interviewer received a detailed written briefing on the project at the beginning of the study. Die zunehmende Verbreitung und Nutzung digitaler Plattformen und sozialer Medien wie z. B. WhatsApp, Facebook, YouTube oder Instagram eröffnen Kindern, Jugendlichen und Erwachsenen neue Möglichkeiten, kulturellen Interessen nachzugehen oder sich ästhetisch zu inszenieren. Richtet man seinen Blick auf Jugendliche im Alter von 12 bis 19 Jahren, so zeigt eine Reihe von Studien zur Mediennutzung, dass sich insbesondere YouTube zum Leitmedium dieser Altersgruppe entwickelt hat. Angesichts des Bedeutungszuwachses dieser Webvideo-Plattform stellen sich Fragen nach den rezeptiven und produktiven Erfahrungsgehalten sowie der Bedeutung kultureller Inhalte und Praktiken. Weiterhin existieren kaum Erkenntnisse darüber, inwiefern YouTube die Jugendlichen zu kulturellen Aktivitäten und selbstorganisierten Lernprozessen anregt. Die Stichprobe setzt sich aus n=818 Jugendlichen im Alter von 12-19 Jahren zusammen. Die Auswahl der Untersuchungseinheiten erfolgte auf der Grundlage eines Quotenverfahrens. Die Rekrutierung der jugendlichen Zielpersonen erfolgte über den IFAK-Interviewerstab nach vorgegeben Quoten für Alter, Geschlecht, Region, Ortsgrößenklasse, besuchter Schultyp (bei Schülern) und Berufstätigkeit (bei Nicht-Schülern). Dabei wurden die Merkmale „Alter und Geschlecht“ sowie „Region und Ortsgröße“ gekreuzt bzw. miteinander kombiniert, um ein möglichst genaues Abbild der Grundgesamtheit herzustellen.Das Merkmal „Migrationshintergrund“ wurde nicht als Quotierungsmerkmal herangezogen. Die Vorgaben hierfür basieren auf den aktuellsten Angaben des Statistischen Bundesamtes und der ma Radio 2018 II. Die strukturelle Zusammensetzung der Stichprobe entspricht nach den genannten Merkmalen den Daten für die Grundgesamtheit. Die Studie wurde als persönlich-mündliche Befragung durchgeführt. Die Antworten der Jugendlichen wurden dabei über ein entsprechendes Befragungsprogramm von einem Interviewer auf einem Laptop erfasst. Zum Einsatz kamen 111 face-to-face Interviewer aus dem hauseigenen Interviewerstab, die Erfahrungen mit der Befragung von Kindern und Jugendlichen haben. Der vorgegebene Fragebogen war im Hinblick auf Wortlaut und Reihenfolge der Fragen für alle Interviewer verbindlich. Die maximale Anzahl an Interviews pro Interviewer lag bei n=10. Jeder Interviewer erhielt zu Beginn der Studie eine detaillierte schriftliche Einweisung in das Projekt.

As of the fourth quarter of 2024, photo and video sharing app Snapchat had 453 million daily active users worldwide, up from 443 million global DAU in the third quarter of 2024. The app has seen steady increases in daily active users since the beginning of 2019. Snapchat is relevant for teenagers Originally launched in 2011, Snapchat has become one of the most popular social messaging and photo sharing apps worldwide; making its CEO and co-founder Evan Spiegel one of the world’s richest social media entrepreneurs. With almost 800 million active users as of April 2024, Snapchat easily ranks among the most popular social networks worldwide. According to U.S. teenagers in fall 2023, Snapchat is the second most important social network of their generation, ahead of photo sharing competitor Instagram and other networks such as Twitter or Facebook. Overall, 48 percent of U.S. internet users aged 15 to 25 years were reportedly using Snapchat, the highest usage reach among any age group. When it comes to user satisfaction with social media, Snapchat’s performance is fair to middling. According to recent survey data, the social app scored 72 out of 100 points on a consumer satisfaction scale, ranking ahead of Twitter and Facebook but behind Pinterest and eternal rival Instagram.

The number of Facebook users in Central & Western Europe was forecast to decrease between 2024 and 2028 by in total 29.8 million users. This overall decrease does not happen continuously, notably not in 2026 and 2027. The Facebook user base is estimated to amount to 192.47 million users in 2028. Notably, the number of Facebook users of was continuously increasing over the past years.User figures, shown here regarding the platform facebook, 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).Find more key insights for the number of Facebook users in countries like Eastern Europe and Russia.

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.