In April 2024, Truth Social saw a total of 3.9 million desktop and mobile web visits in the United States, down from 4.8 million in March 2024. Monthly desktop and mobile web visits of the platform peaked in August 2022, reaching 9.8 million visits. Truth Social is an American media and technology company owned by former U.S. president Donald Trump.

How high is the brand awareness of Truth Social in the United States?When it comes to social media users, brand awareness of Truth Social is at ** percent in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Truth Social in the United States?In total, * percent of U.S. social media users say they like Truth Social. However, in actuality, among the ** percent of U.S. respondents who know Truth Social, ** percent of people like the brand.What is the usage share of Truth Social in the United States?All in all, * percent of social media users in the United States use Truth Social. That means, of the ** percent who know the brand, ** percent use them.How loyal are the users of Truth Social?Around * percent of social media users in the United States say they are likely to use Truth Social again. Set in relation to the * percent usage share of the brand, this means that ** percent of their users show loyalty to the brand.What's the buzz around Truth Social in the United States?In February 2024, about * percent of U.S. social media users had heard about Truth Social in the media, on social media, or in advertising over the past four weeks. Of the ** percent who know the brand, that's ** percent, meaning at the time of the survey there's little buzz around Truth Social in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Context

This data is scrapped from the Politifact website. It contains the claims made by individuals and what does the Politifact curators think about the same. This data can be used in order to run various NLP algorithms in order to find the integrity of the data and also determining the validity of a claim.

Content

Image for associating the content:- When you land on Politifact website. You will see the page with the list of facts as shown below. I have also annotated the various column fields with the image for convenience.

https://i.imgur.com/9MH52Uf.jpg" alt="Landing page for fact check page of Politifact">

Now when you click the article you land on the main page and the annotation for the curator is on the main page. You can see it as follows:- https://i.imgur.com/c9Ht0fp.jpg" alt="Article and other info">

The content of the data is scrapped from the Politifact site and has various attributes. This list of attributes are covered below:- - sources: String representing the person who is associated with the quote. - sources_dates: Date on which the information was furnished by the source. - sources_post_location: The location/medium at which the source furnished the information. - sources_quote: The actual quote/information furnished by the source in question. - curator_name: Person who curated the information from the source. - curated_date:Date at which the curator analyzed and assessed the source's quote. - fact: Fact score that is assigned to the source's quote. - sources_url: URL of the curator's article about the source's quote - curators_article_title: Title of the article written by the curator to support/reject the source's claim - curator_complete_article: Complete blog written by the curator supporting/rejecting the source's claim - curator_tags: Tags given by curator to the blog post.

Acknowledgements

The entire acknowledgment goes to Politifact.com for curating and validating such data and facts.

This is the multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed typical knowledge work (writing reports, making presentations, reading e-mail, searching for information). We manipulated their working conditions with the stressors: email interruptions and time pressure. A varied set of data was recorded: computer logging, facial expression from camera recordings, body postures from a Kinect 3D sensor and heart rate (variability) and skin conductance from body sensors. Our dataset not only contains raw data, but also preprocessed data and extracted features. The participants' subjective experience on task load, mental effort, emotion and perceived stress was assessed with validated questionnaires as a ground truth. The resulting dataset on working behavior and affect is suitable for several research fields, such as work psychology, user modeling and context aware systems.The collection of this dataset was supported by the Dutch national program COMMIT (project P7 SWELL). SWELL is an acronym of Smart Reasoning Systems for Well-being at Work and at Home. Notes on the content of the dataset:- The uLog XML files refer to documents in the dataset. Most extensions of these files have changed due to file conversions. The original extension is now included in the file names at the end.- Due to copyrights not all original documents and images are included in the dataset.- Variable C in 'D - Physiology features (HR_HRV_SCL - final).csv' refers to the type of block, 1, 2 or 3.

Truth Detection / Lie Detection / Deception Detection / Fact Checking

Context

The following dataset was obtained by parsing statements and their veracity verdict from Politifact.com. Contains 14k affirmations up till late 2020.

The statements obtained are of 6 categories: True, Mostly True, Half-True, Mostly False, False, Pants on Fire!

This dataset can be used for multiple purposes: attempting to detect truthfulness based on statement language (or conversely, detecting lies), fact-checking integration or just EDA for political purposes.

Content

There are 4 columns in politifact.csv: statement, source, link, veracity.

statement - statement made by celebrity or politician. source - can be a person, but not necessarily. link - URL of affirmation. veracity - degree of truthfulness given by the Politifact.com team.

Other variants have certain classes removed and are binarized (into truths and lies). Have a quick look over this notebook for more details: https://www.kaggle.com/thesergiu/part-1-quick-eda-on-politifact-csv

Don't forget to upvote the dataset if you find it useful!

Acknowledgements

Initial Source: www.politifact.com Creator GitHub Link: https://github.com/the-sergiu GitHub Repo Link for more context: https://github.com/the-sergiu/TruthDetection

CausaLM Datasets

When evaluating a trained classification model, we usually have access to a test-set, consisting of manually labeled examples that the model was not trained on, and can hence be used for evaluation. Estimating causal effects is often harder in comparison, as we do not have access to the ground truth.

To allow for ground-truth comparisons and to spur further research on causal inference in NLP, we propose here four cases where causal effects can be estimated. In three out of those cases (adjectives, gender and race), we have constructed datasets with counterfactual examples so that the causal estimators can be compared to the ground truth.

Context Extensive research has focused on using data and satellite imagery in precision agriculture, but its use in rangeland management remains underexplored. Rangelands, covering 70% of the world’s land and supporting over 500 million people worldwide, are key for food security and the economy in some of the harshest environments. Unfortunately, they are increasingly threatened by climate change. Nearly half of the world’s rangelands face degradation, with severe impacts on biodiversity, food security, and economies, particularly in arid regions like Namibia, where 80% of the population relies on rangelands. Lack of sustainable practices aggravates this issue, as does bush encroachment, a phenomenon characterised by the increase of shrub species at the expense of palatable grasses. Namibia suffers from bush encroachment on a massive scale, with over 80% of its land affected by this issue. Accurate data is critical for sustainable rangeland management, enabling predictions of productivity, drought planning, and optimal stocking rates. However, data collection in Namibia and the SADC region remains limited despite abundant satellite imagery. To address this gap, Farm4Trade, in collaboration with the University of Namibia, Omeva Consulting, and local experts, has created a dataset. This achievement was made possible through the sponsorship and support of the Lacuna Fund, the world’s first collaborative initiative dedicated to empowering data scientists, researchers, and social entrepreneurs in low- and middle-income regions with the resources needed to create labelled datasets that tackle pressing local challenges.

Content The dataset comprises comprehensive field data collected from 20 sites across Namibia's major livestock-producing ecoregions, with observations conducted during four rounds in February 2023, May 2023, February 2024, and April 2024. The data includes: • Digitized Excel Sheets: Ground truth data, including green coverage, biomass quantity, standing herbaceous crop and grazing patterns. • Plot Pictures: Photographs captured during data collection rounds. • Other data: plot biomass and dominant species identification • Supporting Information: o Maps: Visual representations of the 20 study sites and their distribution across Namibia’s ecoregions. o Coordinates: a table with the exact coordinates of the sites, plots ecoregions where these falls as well as dominant species in the area. o Supportive Manual: Comprehensive guidance on data collection methodologies, digitization processes, and case studies. These includes two practical examples demonstrating the application of the dataset for rangeland management. o API: A tool for integrating the dataset with satellite imagery, allowing for custom geospatial analyses.

Acknowledgements We extend our sincere appreciation to the University of Namibia for providing field sites for data collection, as well as for assigning dedicated students and researchers to support the project. We are particularly grateful to Dr. Axel Rothauge of Agriconsult Namibia and Dr. Cornelis Van der Waal of Agriecological Services for their invaluable expertise in developing the field methodology and for their guidance in training and overseeing the data collection process.

We also wish to thank Tangeni Nakapipi for his expertise in creating the project’s maps. Additionally, we extend our gratitude to the interns from ArewaLadies4Tech, whose efforts in testing the dataset have been invaluable. Most importantly, we would like to thank the field enumerators for their hard work and meticulous attention to detail, which has made this dataset possible. We are also deeply grateful to the farmers for their kindness in allowing data collection on their farms.

We would like to express our appreciation to the funders. This dataset and manual have been made possible through support from Lacuna Fund, the world’s first collaborative initiative dedicated to empowering data scientists, researchers, and social entrepreneurs in low- and middle-income regions with the resources necessary to create labelled datasets that address pressing local challenges. Lacuna Fund is a collaborative funding effort supported by The Rockefeller Foundation, Google.org, Canada’s International Development Research Centre, the German Federal Ministry for Economic Cooperation and Development (BMZ) with GIZ as the implementing agency, Wellcome Trust, the Gordon and Betty Moore Foundation, the Patrick J. McGovern Foundation, and The Robert Wood Johnson Foundation. More information about Lacuna Fund can be found at https://lacunafund.org/about/.