Title: Pew Research Center – American Trends Panel Wave 14 Fieldwork Dates: January 12 – February 8, 2016 Sample Size: N = 4,654 U.S. adults Mode: Web and Mail (English and Spanish) Purpose: This wave investigates Americans’ news consumption habits, civic engagement, and loyalty to media sources. It includes detailed social media behavior, local voting patterns, and a civic activity index. The dataset supports Pew’s broader research on digital media and political participation.

Title: Pew Research Center – American Trends Panel Wave 28 Fieldwork Dates: August 8–21, 2017 Sample Size: N = 4,971 U.S. adults Mode: Web-based survey (English and Spanish) Purpose: This wave explores public attitudes on gender equality, gender traits, and social values. It supports multiple Pew reports and Fact Tank posts, including longitudinal analysis with Wave 29. The dataset includes open-ended responses, corrected survey items, and specialized weights for social media and combined-wave analysis.

Files included: • Data file (Pew_February_2012) – Data from Pew Research, raw version available from http://www.pewinternet.org/datasets/february-2012-search-social-networks-and-politics/ • Syntax file (Clean syntax) – syntax for creation of all variables and all analysis.

Title: Pew Research Center – American Trends Panel Wave 19 Fieldwork Dates: July 12 – August 8, 2016 Sample Size: N = 4,579 U.S. adults Mode: Web and Mail (English and Spanish) Purpose: This wave explores Americans’ experiences with the digital platform economy and their perceptions of political discourse on social media. It supports Pew reports on gig work, online selling, home sharing, and the political environment in digital spaces.

This study aims to understand the views of Americans concerning relevant social factors such as social media, police violence, online personal information and protection, social media company accountability, and public displays of the American flag. The American Trends Panel (ATP), created by the "https://www.pewresearch.org/" Target="_blank">Pew Research Center, is a nationally representative panel of randomly selected U.S. adults. Panelists participate via self-administered web surveys. Panelists who do not have home internet access are provided with a tablet and wireless internet connection. Interviews are conducted in both English and Spanish. The panel is being managed by "https://www.ipsos.com/en" Target="_blank">Ipsos. For the ATP Wave 127 survey, special topics include Americans and their data.

The "https://www.pewresearch.org/internet/2023/10/18/data-privacy-methodology-2/" Target="_blank">ATP Wave 127, conducted from May 15 to May 21, 2023, includes an oversample of Hispanic men, non-Hispanic Black men, and non-Hispanic Asian adults to provide more precise estimates of the opinions and experiences of these smaller demographic subgroups. These oversampled groups are weighted back to reflect their correct proportions in the population. A total of 5,101 panelists responded out of 5,841 who were sampled, for a response rate of 87 percent.

Title: Pew Research Center – Wave 69 American Trends Panel Fieldwork Dates: June 16–22, 2020 Sample Size: N = 4,708 U.S. adults Mode: Web-based survey (English and Spanish) Purpose: This wave of the ATP explores public opinion on coronavirus concerns, political traits, policing reforms, and census participation. It includes longitudinal variables from previous waves (W59 and W64) and supports multiple Pew Research reports released between June and July 2020. The dataset includes multiple weighting schemes for full sample analysis, longitudinal tracking, and census module calibration. It also features a battleground state classification variable for electoral analysis. 🏷️ Tags - Pew Research Center - American Trends Panel - COVID-19 - Public Opinion - Census Participation - Police Reform - Political Traits - Battleground States - Longitudinal Survey - Web Survey - Social Media Use - Survey Weights - Panel Study - June 2020 - U.S. Politics 📝 Notes 📊 Weighting Variables - WEIGHT_W69: Full sample weight for general analysis. - WEIGHT_W59_W69: Longitudinal weight for panelists who responded to Waves 59 and 69. - WEIGHT_W64_W69: Longitudinal weight for panelists who responded to Waves 64 and 69. - WEIGHT_W69_CENSUS: Census module weight adjusted to match estimated 2020 census response rate (targeted at 74%). 🧠 Key Variables - SNSUSE: Non-internet households coded as not using social media. - COVID_COMFORT_COMB_W69: Combines comfort levels from two COVID-related questions. - BATTLE_NARROW_W69: Classifies respondents by battleground state status using SPSS syntax. Categories include: - 1 = Solid Democrat - 2 = Lean/Likely Democrat - 3 = Toss-up/Battleground - 4 = Lean/Likely Republican - 5 = Solid Republican 🔁 Longitudinal Variables from Prior Waves - POL1DT_W59, POL1DTSTR_W59, POL1DT_W64, POL1DTSTR_W64 - DEMFIELD_W59, TRUMPDEM2020_W59, DEM1_CODE_FINAL_W59 🔐 Privacy Measures - State-level data used for classification, but actual state identifiers are excluded. - ZIP codes, counties, and phone numbers removed to protect respondent confidentiality. 📚 Related Reports - Republicans, Democrats Move Further Apart on Coronavirus - Public’s Mood Turns Grim; Trump Trails Biden - Majority Favors Power to Sue Police Officers - Census Participation and Doorstep Reluctance Let me know if you’d like help building a codebook, visualizing trends, or preparing this for statistical analysis.

Title: Pew Research Center – Science and News Survey Fieldwork Dates: May 30 – June 12, 2017 Sample Size: N = 4,024 U.S. adults Languages: English and Spanish Mode: Web-based survey Panel Source: GfK KnowledgePanel (nationally representative online panel) Purpose: This survey explores how Americans consume science news, their trust in various sources, and their engagement with science-related content across media and informal learning environments. It also investigates patterns of active vs. casual consumption and the role of social media, entertainment, and citizen science.

The American Trends Panel (ATP), created by "https://www.pewresearch.org/" Target="_blank">Pew Research Center, is a nationally representative panel of randomly selected U.S. adults. Panelists participate via self-administered web surveys. Panelists who do not have internet access at home are provided with a tablet and wireless internet connection. Interviews are conducted in both English and Spanish. The panel is being managed by "https://www.ipsos.com/en" Target="_blank">Ipsos.

The "https://www.pewresearch.org/religion/dataset/american-trends-panel-wave-117/" Target="_blank">ATP Wave 117 was conducted from November 16 to 27, 2022. A total of 11,377 panelists responded out of 12,402 who were sampled, for a response rate of 92 percent. The cumulative response rate accounting for nonresponse to the recruitment surveys and attrition is 4 percent. The break-off rate among panelists who logged on to the survey and completed at least one item is 1 percent. The margin of sampling error for the full sample of 11,377 respondents is plus or minus 1.4 percentage points.

Question items in the ATP Wave 117 primarily concerned politics and the 2022 midterm election that had recently taken place, attendance of religious services online or in person, and respondents' other personal religious activities, whether online in social media use or in various other forms.

📱 Context

This dataset consists of YouTube video and comment metadata for COVID-19 related videos in January 2021.

Social Media is becoming a popular data source for analysts to observe, manipulate, and model. This is especially true for data related to COVID-19. Many Users can easily find a variety of COVID-19 related social media data on Kaggle, such as datasets for Tweets.

However, despite YouTube being one of the most used social media platforms, there aren't many datasets containing metadata for YouTube videos and comments. This is dataset aims to provide usable COVID-19 related text data regarding YouTube.

📈 Dataset Description

The videos in this dataset are between January 1st, 2021 and January 30th, 2021. The dataset contains the most-viewed videos that were related to at least one of the following search queries:

• coronavirus
• covid-19
• pandemic
• vaccine

🔧 Collection Method

For this project, I developed youcos, a simple Python package for collecting and saving YouTube video and comment data through the YouTube v3 API. Feel free to contribute to the project or use the package to collect your own data!

👍 Acknowledgements

The data was primarily acquired through the YouTube v3 API. You can read their Terms of Service here

🌱 Inspiration

Here are some possible next steps for this data: - Perform sentiment analysis for the videos and comments, - Compare sentiments for COVID related posts with Twitter, Reddit, and other social media platforms - Predict the number of comments, views, and likes/dislikes a video will have based on its Title - Predict the number of likes a comment will receive based on its text and sentiments

📔 Note

future datasets / additions will include data collected based on a specific time frame, location, and view counts

If you find this dataset useful, please UPVOTE! It motivates me to create more quality content. Thank you!

This dataset from Pew Research Center includes crosstab results from surveys conducted between September 25–October 9, 2014, and February 10–March 6, 2015. The survey focuses on teens, exploring their digital habits, technology use, social media engagement, and communication trends. The crosstabs provide demographic breakdowns to better understand how teens’ behaviors vary across different groups.

In December 2006, Environics Research conducted a major national survey of Muslims and multiculturalism in Canada, as part of its ongoing syndicated FOCUS CANADA research program. The research consisted of two national telephone public opinion surveys: - National survey with a representative sample of 2,045 Canadians (18 years plus) - National survey with a representative sample of 500 Muslims living in Canada The focus of this research is on the presence and experience of Muslims in this country, and draws direct comparisons with similar research conducted in 13 other countries by the Pew Research Center (many of the same research questions were used to provide for direct country-to-country comparisons). The Pew research included Muslim over-samples in Great Britain, France, Germany and Spain. Some of the topics covered in this research: General Public: - General attitudes about immigration in Canada - Personal contact with different ethnic groups (including Muslims) - Perceived discrimination against ethnic groups - General attitudes towards Muslims - Concerns about Muslims and terrorism - Islamic identity and extremism among Muslims - Integration of Muslims and other ethnic minorities into Canadian society - Canadian foreign policy and the mission in Afghanistan Muslims - Experience of being Muslim in Canada - Concern about the future of Muslims in Canada - Self identification within the Muslim community - The role and rights of women in ethnic communities - Islamic identity and extremism among Muslims - Integration of Muslims and other ethnic minorities into Canadian society - Canadian foreign policy and the mission in Afghanistan Please note, the cases in this dataset are comprised only of Muslim respondents. Data from the other component of this survey - the survey of the general population - may be found in the dataset titled "EFC064." Environics Focus Canada - Survey of Muslims in Canada (Dec 2006) Study Overview: http://queensu.ca/cora/_files/Environics%20Muslims%20in%20Canada%20-%20Overview.pdf Environics Focus Canada - Survey of Muslims in Canada (Dec 2006) Methodology: http://queensu.ca/cora/_files/Methodology%20for%20Survey%20of%20Muslims.pdf Copyright (c) 2007 - Environics Research Group

Context

Welcome to another weekend hackathon, this weekend we are providing a great opportunity to the machinehackers to flex their NLP muscles again by building a fake content detection algorithm. Fake contents are everywhere from social media platforms, news platforms and there is a big list. Considering the advancement in NLP research institutes are putting a lot of sweat, blood, and tears to detect the fake content generated across the platforms.

Fake news, defined by the New York Times as “a made-up story with an intention to deceive”, often for a secondary gain, is arguably one of the most serious challenges facing the news industry today. In a December Pew Research poll, 64% of US adults said that “made-up news” has caused a “great deal of confusion” about the facts of current events

In this hackathon, your goal as a data scientist is to create an NLP model, to combat fake content problems. We believe that these AI technologies hold promise for significantly automating parts of the procedure human fact-checkers use today to determine if a story is real or a hoax.

Content

Train.csv - 10240 rows x 3 columns (Inlcudes Labels Columns as Target) Test.csv - 1267 rows x 2 columns Sample Submission.csv - Please check the Evaluation section for more details on how to generate a valid submission Text - Raw content from social media/ new platforms Text_Tag - Different types of content tags (9 unique products) Labels - Represents various classes of Labels Half-True - 2 False - 1 Mostly-True - 3 True - 5 Barely-True - 0 Not-Known - 4

Inspiration

https://www.machinehack.com/hackathons/fake_news_content_detection_weekend_hackathon_20

Full edition for scientific use. PUMA Surveys consist of separate modules designed and prepared by different principle investigators. This PUMA Survey consists of three modules: MODULE 1 "Non-Health Influences on Generic Health Ratings: Comparing the Susceptibility of Self-Rated Health (SRH) and the Minimum European Health Module (MEHM) to Biases Due to Optimism, Hypochondriasis, and Social Desirability", MODULE 2 "Online completion versus face-to-face completion. Testing mixing modes of data collection for Austrian social surveys", MODULE 3 "Concerns of Smartphone Owners When Using their Device for Research". Fieldwork was conducted by Statistics Austria. MODULE 1: Non-Health Influences on Generic Health Ratings: Comparing the Susceptibility of Self-Rated Health (SRH) and the Minimum European Health Module (MEHM) to Biases Due to Optimism, Hypochondriasis, and Social Desirability (Patrick Lazarevič, Martina Brandt, Marc Luy, Caroline Berghammer) Self-rated health (SRH) is the most widely used single-indicator of health in many scientific disciplines (Jylhä 2009). Even though more comprehensive approaches to measure generic health exist, they are often too time consuming for survey interviews, especially in multi-thematic surveys, due to time limitations. Research in this regard has shown that, even when controlling for comprehensive health information, SRH is noticeably and independently influenced by non-health factors like satisfaction with life or social participation (e.g., Lazarevič 2018). While these results illustrate that health ratings are influenced by non-health factors, the personality traits that are assumed to bias SRH (e.g., optimism, social desirability, or hypochondriasis) are typically not directly measured. The Minimum European Health Module (MEHM), as proposed by Robine & Jagger (2003), complements SRH with the questions whether the respondent suffers from a chronic disease and whether and to what extent they are limited in their usual activities due to a health problem. Thus, MEHM can be seen as a compromise between using SRH as a single-indicator and a comprehensive scale while covering the two most relevant factors for health ratings, i.e., chronic diseases and the functional status (Lazarevič 2018). While MEHM is obviously less time- and cost-intensive than more comprehensive approaches to measure health and there was some research done on its components separately (e.g., Berger et al. 2015), hardly anything is known about its usefulness as a short-scale of generic health, its overall psychometric properties, and its susceptibility to non-health factors potentially biasing the health measurement. This module tested the feasibility and utility of using the Minimum European Health Module (MEHM) as a short scale for measuring generic health. We demonstrate the feasibility of extracting a factor score from MEHM utilizing confirmatory factor analyses based on polychoric correlations. Further analyses suggest that this factor score might be useful in reducing bias in generic health measurement due to optimism and social desirability. MODULE 2: Online completion versus face-to-face completion. Testing mixing modes of data collection for Austrian social surveys (Markus Hadler, Franz Höllinger, Anja Eder) Collecting data online is a promising tool, given the problems survey research faces in terms of lowering response rates and increasing costs. Yet, the results on the comparability of online and face-to-face surveys are ambiguous (see Roberts et al. 2016). Therefore, the aim of our research is to test differences in responses when completing surveys online compared to collecting the same data face-to-face. Our PUMA-module collects some of the core ISSP questions online, which were asked face-to-face (CAPI) in the same time-period. The topics of the ISSP questionnaires 2017 and 2018 are “Social Networks” and “Religion.” At face value, we expect that these two areas may attract different respondents when conducted online as compared to face-to-face. Online networking should be more prevalent and traditional religious activities less common among the online respondents. If there are no significant differences between these two samples, our study will be a strong indicator that online tools are valid instruments. Therefore, the mixed modes design aims to break new ground in understanding the advantages and limitations, the costs and benefits of combining online and face-to-face interviews in Austria on the basis of two prominent survey modules from the International Social Survey Programme. MODULE 3: Concerns of Smartphone Owners When Using their Device for Research (Florian Keusch, Martin Weichbold) Smartphone use is on the rise worldwide (Pew Research Center 2017). Survey researchers are aware that smartphone users increasingly complete online surveys on their mobile devices and have investigated the quality of survey data provided via smartphones (e.g., Couper et al. 2017; Keusch &...

BACKGROUND:

The survey stands out as one of the most significant methodological innovations in the history of the social sciences. The instrument has made possible the efficient and reliable collection of measurements on individual attitudes, beliefs, values, behaviors, traits, and states (Alwin 2009), and coupled with modern sampling techniques, surveys have allowed researchers to generalize findings to much larger populations with remarkable precision. Survey data have helped governments to obtain essential descriptive information on, for example, their citizen’s living standards, demographic attributes, health status, purchasing intentions. Such data have, in turn, been used to craft evidence-based social and economic policies, more accurately forecast labor market participation and project population distributions, and monitor poverty rates. Commercial firms, especially those in the fields of market research and consumer behavior, have also been influential in the development of methods and the production of designed data (Groves 2011). Social scientists use survey data to study a wide range of social, economic, political, demographic, and cultural topics and to collect measurements of voter preferences, personality traits, social networks, and public opinions, to name a few. Survey data are an essential component of evidence-based social policy-making and program monitoring and evaluation and vital tool for understanding the worldviews and aspirations of publics around the world.

The progression of the social survey from its origins as a community and, later, national measuring device was attended by greater consensus and less variation in survey design and administration. Over the course of more than 60 years of cross-cultural survey research, the standardized survey (Gobo and Mauceri 2014; Harkness 2010) became the de facto one, with hallmark features including an almost exclusive reliance on input harmonized questionnaires and closed-ended questions, English language parent survey back translated into local-language-administered surveys, and best efforts at nationally representative sampling techniques that allow for generalization to, in most cases, the non-institutionalized adult population. With few exceptions, survivor programs originated in rich, western countries, and among survey programs originating in non-western countries, survey design and content has often been patterned after surveys designed to measure attitudes, beliefs, behaviors, states, and institutions endemic to western, industrialized societies. This is not to say that cross-national survey programs are monolithic or lacking in variety of design, content, international membership, and purposes.

THE DATA:

To get a clearer understanding of how the social survey has spread around the world over the last 60 years, we collected information from just under 40 international survey programs and compiled it into a time-series data set that allows researchers to empirically evaluate the global social survey data infrastructure from a life-history perspective. Collectively, these 40 survey programs have fielded over 7000 national social surveys comprising nearly 8 million completed interviews on a broad range of social, economic, and political topics. Units of analysis in the data set are survey programs and national social surveys. Additional data about programs and surveys include samples sizes, data collection methods, fielding dates, and countries surveyed, to name a few.

PARTICIPATING SOCIAL SURVEY PROGRAMS:

How Nations See Each Other (HNSEO) Pattern of Human Concerns Data (PHCD) Civic Culture Study (CCS) Attitudes Toward Europe Study (ATE) Political Participation and Equality (PPE) Eurobarometer (EUROB) European Values Study (EVS) World Values Survey (WVS) International Social Survey Programme (ISSP) The Political Culture of Southern Europe (PCSE) Central and Eastern Eurobarometer (CEEB) Post-Communist Publics (PCP) New European Barometer (NEB) Comparative National Elections Project (CNEP) New Soviet Citizen Surveys (NSCS) Values and Political Change in Post-Communist Europe (VPCP) Latinobarometro (LATINB) Coping with Government in the Former Soviet Union (CGFE) Afrobarometer (AFROB) Asia Europe Survey (ASES) Voice of the People Surveys (VOTP) Asian Barometer (ASIANB) Candidate Countries Eurobarometer (CCEB) Comparative Study of Electoral Systems (CSES) European Social Survey (ESS) Pew Global Attitudes Surveys (PGAS) Worldviews 2002 (WORLD)* Asia Barometer (ASIAB) Transatlantic Trends Survey (TTS) AmericasBarometer (AMERAB) Arab Barometer (ARABB) East Asia Social Survey (EASS) The Globalization of Personal Data Project (GPD) A Quest for Citizenship in an Ever Closer Europe (INTUNE) Caucasus Barometer (CAUCAB) Transatlantic Trends: Immigration (TTI) EU Neighbourhood Barometer (EUNB)

As of June 2024, 99 percent of adults in the United States between 18 and 49 years were internet users, making it the age group with the highest level of internet penetration in the country. A further share of 97 percent of adults using the internet were between 18 and 29 years old. Mobile internet usage Mobile internet usage continues to surge in the United States, with 96.2 percent of internet users accessing the web via phones as of the third quarter of 2023. In April 2024, YouTube's mobile app led with a 74 percent audience reach, while TikTok topped weekly engagement among social apps. Mobile apps and privacy Mobile apps became an essential part of mobile users, this high usage raised new concerns about data privacy. By June 2023, three in four internet users supported data localization to protect their information. Additionally, As of September 2024, 13.5 percent of paid iOS apps stated that they collected user data, with 88 percent of this data used to enhance app functionality.