According to a survey conducted in the United States in 2025, internet users in the United States spent ** hours weekly watching TV, with streaming taking up most of this time. Over 11 hours were devoted to watching both long- and short-form online video content. This substantial amount of time spent with online media reflects a significant trend in consumption habits, which have been influenced by the need for inexpensive, faster, and more accessible entertainment. Media Consumption Trends and Future Plans The average daily time spent with digital media in the United States is expected to increase from *** minutes (***** hours and ** minutes) in 2022 to close to ***** hours by the end of 2025. This pivot is dictated by the ease of access of online entertainment and variety of content available on numerous platforms.According to a survey conducted in April 2023, the majority of consumers in the United States were not planning to make any major changes to most of their media habits in the following year. However, a notable ** percent planned to increase their time spent listening to podcasts. In general, there was an evident shift towards reducing or ending paid subscriptions to numerous media services. Free entertainment is on social A survey conducted in June 2023 highlighted that adults in the United States spent more time per day on TikTok than on any other leading social media platform, with an average of **** minutes per day. The video sharing network gained more popularity with users than YouTube and Twitter. This underlines the growing interest in short form video and fast entertainment options. Today consumers step away from traditional media and dive into online offers.

Context: This dataset offers insights into the usage patterns of social media apps for 1,000 users across seven popular platforms: Facebook, Instagram, Twitter, Snapchat, TikTok, LinkedIn, and Pinterest. It tracks various metrics such as daily time spent on the app, number of posts made, likes received, and new followers gained.

Dataset Features:

User_ID: Unique identifier for each user. App: The social media platform being used. Daily_Minutes_Spent: Total time a user spends on the app each day, ranging from 5 to 500 minutes. Posts_Per_Day: Number of posts a user creates per day, ranging from 0 to 20. Likes_Per_Day: Total number of likes a user receives on their posts each day, ranging from 0 to 200. Follows_Per_Day: The number of new followers a user gains daily, ranging from 0 to 50. Context & Use Cases: This dataset could be particularly useful for social media analysts, digital marketers, or researchers interested in understanding user engagement trends across different platforms. It provides insights into how much time users spend, how actively they post, and the level of engagement they receive (in terms of likes and followers).

Conclusion & Outcome: Analyzing this dataset could yield several outcomes:

Engagement Patterns: Identifying which platforms have higher engagement in terms of time spent or likes received. Active Users: Determining which users are the most active across various platforms based on the number of posts and followers gained. User Retention: Studying the correlation between time spent and follower growth, providing insight into user retention strategies for different platforms. Overall, the dataset allows for exploration of social media usage trends and helps drive decision-making for marketing strategies, content creation, and platform engagement.

Content

This dataset contains survey-based information about students’ social media usage habits and levels of addiction. It includes demographic details such as age, gender, and academic level, along with behavioral variables related to time spent on social media, preferred platforms, frequency of use, and purpose of engagement. The dataset also records indicators of addiction such as difficulty controlling usage, impact on sleep patterns, concentration in studies, emotional dependency, and feelings of anxiety or restlessness when not using social media. Each record represents an individual student’s response, providing a detailed view of how social media use varies across different student groups.

The data is structured in a clean tabular format, making it suitable for exploratory data analysis, visualization, and machine learning tasks. Researchers and students can use this dataset to study usage trends, classify addiction levels, predict behavioral outcomes, or analyze relationships between social media habits and academic or psychological factors.

Context

Social media has become an essential part of student life, offering opportunities for communication, entertainment, and learning. However, excessive use can lead to addiction, reduced academic performance, sleep disturbances, and mental health challenges. This dataset was collected to better understand the extent of social media dependency among students and to identify patterns that may contribute to unhealthy usage behavior. The dataset is useful for educators, psychologists, data scientists, and policymakers who are interested in studying digital behavior and its impact on young learners. It can support research on mental health awareness, digital well-being, and the development of intervention strategies to promote healthier social media habits among students.

A global survey conducted in March 2020 revealed that the coronavirus has had a direct impact on in-home media consumption around the world, with ** percent of total respondents professing to have read more books or listened to more audiobooks at home and ** percent having listened to more radio due to the COVID-19 pandemic, whilst more than ** percent of consumers spent longer on messaging services and social media. Interestingly, although at least ** percent of respondents in most countries said that they were watching more news coverage, figures for Australia and the United States were lower, amounting to just ** and ** percent respectively. Australians were also the least likely to be reading more newspapers; just **** percent of consumers said that they were doing so compared to the global total of ** percent. Whilst ** percent of Italians were spending longer on messaging services, in Japan the same was true for only ***** percent of respondents, and survey participants from China and the Philippines were by far the most likely to be spending more time on music streaming services.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

This dataset contains simulated yet realistic data of 5,000 social media users across multiple countries. It captures daily social media usage habits, preferred platforms, content types, engagement scores, and mental fatigue levels.

The dataset is suitable for:

• Exploratory Data Analysis (EDA)
• Mental health analytics
• User behavior modeling
• Machine learning classification & regression tasks
• Time-series analysis

This dataset is ideal for beginners to intermediate Kaggle users looking to practice data analysis, visualization, and predictive modeling on a modern and trending topic.

Description This dataset explores the influence of mass media on voter preferences across urban and rural communities. It includes demographic details, media consumption habits, sentiment analysis of media sources, and voting preferences. The data helps analyze how different media channels—such as television, radio, newspapers, social media, and online news—affect electoral decision-making.

Key Features Demographics: Age, Gender, Education Level, Location Type (Urban/Rural)

Media Consumption: Preferred media sources (TV, Radio, Newspaper, Social Media, etc.)

Sentiment Score: Numerical sentiment (-1 to +1) extracted from media content analysis

Voting Preference (Target Column): Indicates voter support for Candidate A or Candidate B

Potential Use Cases Sentiment analysis of political media influence

Predicting voter behavior using machine learning

Studying rural vs. urban differences in media impact

Analyzing the role of traditional vs. digital media in elections

Gen Z Statistics: These techno-savvy people from Gen Z are the ones taking technology to the next level. This is the only generation that has gained experience from Gen Y and baby boomers. People from the 90s is the common term used for Gen Z. These Gen Z Statistics are a trip to the generation of people who teach you how to live! These guys know everything and are distinct from other generations. Editor’s Choice 40% of Gen Z usually leave their current job within 2 years moreover, 35% of them resign before lining up with another one. In the United States of America as of 2022, the most favorite snack of Gen Z consumers is Chips. In the United States of America, as of 2022, 45% of consumers from Gen Z preferred locally grown products. 2 out of 5 Gen Z candidates rejected an offer from a company because the company failed to live up to the candidate’s values. 46% of Gen Z barely earn extra money and fail to cover expenses. As of 2022, 29% of Gen Z are struggling with the cost of living with bills, housing, and transport. According to Gen Z Statistics, 33% of them working remotely said, mobile work helped to save money. Zoomers have entrepreneurial skills and are worried about their future. Moreover, 15% of Gen Z agreed on remote work helped them settle down at their favorite location away from work. The influential factor before choosing the food for Gen Z is ingredients.

Social Media Buying Dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19517213%2Fe2f9c167d7297b63c931dbc8b02d34ff%2FScreenshot%202024-04-14%20154826.png?generation=1713089935415579&alt=media" alt="">

Data Description

This dataset contains information about individuals and their response to a particular advertisement campaign on social media. The dataset includes the following columns:

• Age: Age of the individual in years.
• EstimatedSalary: Estimated salary of the individual.
• Purchased: Binary variable indicating whether the individual made a purchase (1) or not (0) after seeing the advertisement.

Column Descriptions

i. Age:

Data Type: Integer Description: Represents the age of the individual in years.

ii. EstimatedSalary:

Data Type: Integer Description: Indicates the estimated salary of the individual.

iii. Purchased:

Data Type: Integer (0 or 1) Description: Indicates whether the individual made a purchase (1) or not (0) after seeing the advertisement.

Additional Information

This dataset can be used to analyze the relationship between age, estimated salary, and purchase behavior in response to the advertisement. The dataset appears to be suitable for binary classification tasks, where the goal might be to predict whether an individual will make a purchase based on age and estimated salary. Exploratory data analysis (EDA) techniques can be applied to understand patterns and correlations within the dataset before building predictive models.

The Spoken Word Content Platforms market has emerged as a dynamic segment within the broader digital content industry, fueled by the rising popularity of audio-based media consumption. As consumers continue to seek engaging and accessible ways to absorb information, spoken word platforms, which include pod...

In early 2025, ***percent of consumers in the United States were watching TV for at least ***** hours per day, down from ** percent who said the same in the corresponding period of 2024. At the same time, radio consumption also decreased, with ** percent of consumers listening to radio in 2025.

According to a survey conducted in the second quarter of 2022, global internet users aged between 16 and 24 spent *** hours and ** minutes with social media per day. At the same time they also spent *** hour and ** minutes with games consoles. Social media was the most time consuming media pastime for age groups 16 to 44, while internet user older than that spent more of their time watching linear TV.

Find detailed analysis in Market Research Intellect's Mem Media Consumption Market Report, estimated at USD 45 billion in 2024 and forecasted to climb to USD 70 billion by 2033, reflecting a CAGR of 6.5%.Stay informed about adoption trends, evolving technologies, and key market participants.

Same News Different Sources

How different sources report on the same events

About this dataset

Do you ever feel like you're being inundated with news from all sides, and you can't keep up? Well, you're not alone. In today's age of social media and 24-hour news cycles, it can be difficult to know what's going on in the world. And with so many different news sources to choose from, it can be hard to know who to trust.

That's where this dataset comes in. It captures data related to individuals' Sentiment Analysis toward different news sources. The data was collected by administering a survey to individuals who use different news sources. The survey responses were then analyzed to obtain the sentiment score for each news source.

So if you're feeling overwhelmed by the news, don't worry – this dataset has you covered. With its insights on which news sources are trustworthy and which ones aren't, you'll be able to make informed decisions about what to read – and what to skip

How to use the dataset

The Twitter Sentiment Analysis dataset can be used to analyze the impact of social media on news consumption. This data can be used to study how individuals' sentiments towards different news sources vary based on the source they use. The dataset can also be used to study how different factors, such as the time of day or the topic of the news, affect an individual's sentiments

Research Ideas

• Identify which news sources are most trusted by the public.
• Understand what topics are most important to the public.
• Understand how different news sources report on the same issue

Columns

File: news.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: news_api.csv | Column name | Description | |:--------------|:------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Source | The news source the article is from. (String) |

File: politics.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: sports.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: television.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: trending.csv | Column name | Description ...

The prevalence of bias in the news media has become a critical issue, affecting public perception on a range of important topics such as political views, health, insurance, resource distributions, religion, race, age, gender, occupation, and climate change. The media has a moral responsibility to ensure accurate information dissemination and to increase awareness about important issues and the potential risks associated with them. This highlights the need for a solution that can help mitigate against the spread of false or misleading information and restore public trust in the media.Data description: This is a dataset for news media bias covering different dimensions of the biases: political, hate speech, political, toxicity, sexism, ageism, gender identity, gender discrimination, race/ethnicity, climate change, occupation, spirituality, which makes it a unique contribution. The dataset used for this project does not contain any personally identifiable information (PII).The data structure is tabulated as follows:Text: The main content.Dimension: Descriptive category of the text.Biased_Words: A compilation of words regarded as biased.Aspect: Specific sub-topic within the main content.Label: Indicates the presence (True) or absence (False) of bias. The label is ternary - highly biased, slightly biased and neutralToxicity: Indicates the presence (True) or absence (False) of bias.Identity_mention: Mention of any identity based on words match.Annotation SchemeThe labels and annotations in the dataset are generated through a system of Active Learning, cycling through:Manual LabelingSemi-Supervised LearningHuman VerificationThe scheme comprises:Bias Label: Specifies the degree of bias (e.g., no bias, mild, or strong).Words/Phrases Level Biases: Pinpoints specific biased terms or phrases.Subjective Bias (Aspect): Highlights biases pertinent to content dimensions.Due to the nuances of semantic match algorithms, certain labels such as 'identity' and 'aspect' may appear distinctively different.List of datasets used : We curated different news categories like Climate crisis news summaries , occupational, spiritual/faith/ general using RSS to capture different dimensions of the news media biases. The annotation is performed using active learning to label the sentence (either neural/ slightly biased/ highly biased) and to pick biased words from the news.We also utilize publicly available data from the following links. Our Attribution to others.MBIC (media bias): Spinde, Timo, Lada Rudnitckaia, Kanishka Sinha, Felix Hamborg, Bela Gipp, and Karsten Donnay. "MBIC--A Media Bias Annotation Dataset Including Annotator Characteristics." arXiv preprint arXiv:2105.11910 (2021). https://zenodo.org/records/4474336Hyperpartisan news: Kiesel, Johannes, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, David Corney, Benno Stein, and Martin Potthast. "Semeval-2019 task 4: Hyperpartisan news detection." In Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 829-839. 2019. https://huggingface.co/datasets/hyperpartisan_news_detectionToxic comment classification: Adams, C.J., Jeffrey Sorensen, Julia Elliott, Lucas Dixon, Mark McDonald, Nithum, and Will Cukierski. 2017. "Toxic Comment Classification Challenge." Kaggle. https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge.Jigsaw Unintended Bias: Adams, C.J., Daniel Borkan, Inversion, Jeffrey Sorensen, Lucas Dixon, Lucy Vasserman, and Nithum. 2019. "Jigsaw Unintended Bias in Toxicity Classification." Kaggle. https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification.Age Bias : Díaz, Mark, Isaac Johnson, Amanda Lazar, Anne Marie Piper, and Darren Gergle. "Addressing age-related bias in sentiment analysis." In Proceedings of the 2018 chi conference on human factors in computing systems, pp. 1-14. 2018. Age Bias Training and Testing Data - Age Bias and Sentiment Analysis Dataverse (harvard.edu)Multi-dimensional news Ukraine: Färber, Michael, Victoria Burkard, Adam Jatowt, and Sora Lim. "A multidimensional dataset based on crowdsourcing for analyzing and detecting news bias." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 3007-3014. 2020. https://zenodo.org/records/3885351#.ZF0KoxHMLtVSocial biases: Sap, Maarten, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. "Social bias frames: Reasoning about social and power implications of language." arXiv preprint arXiv:1911.03891 (2019). https://maartensap.com/social-bias-frames/Goal of this dataset :We want to offer open and free access to dataset, ensuring a wide reach to researchers and AI practitioners across the world. The dataset should be user-friendly to use and uploading and accessing data should be straightforward, to facilitate usage.If you use this dataset, please cite us.Navigating News Narratives: A Media Bias Analysis Dataset © 2023 by Shaina Raza, Vector Institute is licensed under CC BY-NC 4.0

Time-Wasters on Social Media Dataset Overview The "Time-Wasters on Social Media" dataset offers a detailed look into user behavior and engagement with social media platforms. It captures various attributes that can help analyze the impact of social media on users' time and productivity. This dataset is valuable for researchers, marketers, and social scientists aiming to understand the nuances of social media consumption.

This dataset was generated using synthetic data techniques with the help of NumPy and pandas. The data is artificially created to simulate real-world social media usage patterns for research and analysis purposes.

Columns Description UserID: A unique identifier assigned to each user. Age: The age of the user. Gender: The gender of the user. Location: The geographical location of the user. Income: The annual income of the user. Debt: Tells If the is in Debt or Not. Owns Property: Indicates whether the user owns any property (Yes/No). Profession: The profession or job title of the user. Demographics: Additional demographic information about the user (Rural or Urban Life). Platform: The social media platform used by the user (e.g., Facebook, Instagram, TikTok). Total Time Spent: The total time the user has spent on the platform. Number of Sessions: The number of sessions the user has had on the platform. Video ID: A unique identifier for each video watched. Video Category: The category of the video watched (e.g., Entertainment, Gaming, Pranks, Vlog). Video Length: The length of the video watched. Engagement: The engagement level of the user with the video (e.g., Likes, Comments). Importance Score: A score representing the perceived importance of the video to the user. Time Spent On Video: The amount of time the user spent watching the video. Number of Videos Watched: The total number of videos watched by the user. Scroll Rate: The rate at which the user scrolls through content. Frequency: How frequently the user logs into the platform. Productivity Loss: The amount of productivity lost due to time spent on social media. Satisfaction: The satisfaction level of the user with the content consumed. Watch Reason: The reason why the user watched the video (e.g., Entertainment, Information). DeviceType: The type of device used to access the platform (e.g., Mobile, Desktop). OS: The operating system of the device used. Watch Time: The specific time of day when the user watched the video. Self Control: The user's self-assessed level of self-control while using the platform. Addiction Level: The user's self-assessed level of addiction to social media. Current Activity: The activity the user was engaged in before using the platform. ConnectionType: The type of internet connection used by the user (e.g., Wi-Fi, Mobile Data).

Usage This dataset can be utilized to:

Analyze patterns in social media usage. Understand demographic differences in platform engagement. Examine the impact of social media on productivity. Develop strategies to improve user engagement and satisfaction. Study the correlation between social media usage and various demographic factors.

Dataset Title: Impact of Social Media Algorithms on Consumer Behavior in Rondon do Pará, Brazil (2025)

General Description:

This dataset provides quantitative information for analyzing the influence of social media algorithms on consumer behavior in the municipality of Rondon do Pará, Brazil. The data were compiled from public sources and complemented by empirical online responses, encompassing variables related to social media usage, exposure to personalized advertisements, and online purchasing decisions. The dataset aims to support research in the fields of digital marketing, consumer behavior, and regional economic development.

Methodology:

The research adopts a quantitative, descriptive, and applied approach, based on the analysis of secondary data obtained from public databases such as IBGE, SEBRAE, Statista, Ebit/Nielsen, and Meta Business Suite, as well as locally collected online data. Variables are grouped into thematic blocks as follows: 1. Sociodemographic Profile – age, average income, occupation, and internet usage frequency. 2. Use of Social Media – average daily usage time, most accessed platforms, and advertisement exposure frequency. 3. Algorithmic Influence and Personalization – engagement rates, retention time, and targeted content. 4. Role of Digital Influencers – audience reach, credibility, and purchase decision impact. 5. Online Consumer Behavior – purchase frequency, motivations, and comparison between online and physical shopping. 6. Impact on Local Commerce – perception of e-commerce substitution effects and influence on local economic activity.

Data analysis was conducted using Microsoft Excel and IBM SPSS Statistics, applying descriptive statistics, Pearson’s correlation, and regional comparative analysis.

Data Format and Structure:

• File type: .csv

• Number of observations: 102 valid records

• Number of variables: 21 columns corresponding to the thematic categories above

• Encoding: UTF-8

• Delimiter: Comma (,)

Main Variables:

• age (numeric)

• gender (categorical)

• monthly_income (numeric, in BRL)

• daily_social_media_use (numeric, hours/day)

• most_used_social_media (categorical)

• ad_exposure_frequency (Likert scale 1–6)

• ad_influence_level (Likert scale 1–6)

• trust_in_influencers (Likert scale 1–6)

• online_purchase_preference (binary: 0 = physical store, 1 = online)

• impact_on_local_commerce (Likert scale 1–6)

Temporal Coverage: January – October 2025

Geographical Coverage: Rondon do Pará, State of Pará, Brazil.

Categories

Business to Business Marketing

Acknowledgement

Carlos Cesar Santos

The study series "Media Analyse" is an annual, systematic survey of media usage among the German population which is conducted by "Arbeitsgemeinschaft für Media-Analyse (AGMA)“. AGMA includes popular consumer media, advertising agencies and various advertising companies. A random sample is surveyed annually in a personal interview about their media use. The present study from 1997 focuses on the use of electronic media and particularly on television and radio consumption.

This dataset provides detailed information on how individuals allocate their time across various social media platforms, including Facebook, Twitter, Instagram, YouTube, Snapchat, TikTok, LinkedIn, WhatsApp, and Pinterest. Each entry represents the number of hours spent on each platform and includes location data to explore geographic trends in social media consumption.

The dataset is ideal for analyzing:

Time distribution across social platforms.
Location-based patterns in social media usage.
Comparative studies on platform preferences.

Perfect for social behavior analysis and data-driven marketing insights!

A line chart that shows % of U.S. adults who say they get news from ...

The coronavirus outbreak has caused media consumption to increase in countries across the globe, with book reading and audiobook listening up by ** percent, social media usage seeing an increase of ** percent, and news consumption rising by ** percent. Some consumers were more engaged with the news than others, with the share of Australians and French respondents who significantly increased their news consumption standing at around ***** to *** percent lower than the global total. Perhaps unsurprisingly, comparatively few people spent more time reading newspapers and magazines or listening to the radio (all media pastimes which have waned in popularity in recent years) but time spent on social media and video streaming services grew substantially in some countries, particularly the Philippines. Meanwhile, in nearby Singapore social media usage time grew by just ** percent.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.