The file anonymized_app_data.csv contains a sample of smartphone app-fingerprints from 20,000 randomly selected individuals, collected in May 2016.Each record in the table corresponds to a (user, app) pair, and reveals that a given app was used at least once by a given user during May 2016. The table contains the following field:user_id : hashed user idapp_id: hashed id the smartphone app The data accompanies the publication: "Temporal and Cultural Limits of Privacy in Smartphone App Usage"

Context

This dataset provides insights into the daily mobile usage patterns of 1,000 users, covering aspects such as screen time, app usage, and user engagement across different app categories.

It includes a diverse range of users based on age, gender, and location.

The data focuses on total app usage, time spent on social media, productivity, and gaming apps, along with overall screen time.

This information is valuable for understanding behavioral trends and app usage preferences, making it useful for app developers, marketers, and UX researchers.

This dataset is useful for analyzing mobile engagement, app usage habits, and the impact of demographic factors on mobile behavior. It can help identify trends for marketing, app development, and user experience optimization.

Outcome

This dataset enables a deeper understanding of mobile user behavior and app engagement across different demographics.

Key outcomes include insights into app usage preferences, daily screen time habits, and the impact of age, gender, and location on mobile behavior.

This analysis can help identify patterns for improving user experience, tailoring marketing strategies, and optimizing app development for different user segments.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

The four datasets 'phone', 'game', 'social', and 'video' are the processed datasets that are used as input files for the Mplus models (but then in .csv instead of .dat format). The dataset 'phone' contains all data related to the main analyses of daytime, pre-bedtime and post-bedtime smartphone use. The datasets 'game', 'social', and 'video' represent the data related to the exploratory analyses for game app, social media app, and video player app use, respectively. The dataset 'timeframes' contains information about respondents' bedtime and wake-up time, which is required to calculate the three timeframes (daytime, pre-bedtime, and post-bedtime).------------------The materials used, including the R and Mplus syntaxes (https://osf.io/tpj98/) and the preregistration of the current study (https://osf.io/kxw2h/) can be found on OSF. For more information, please contact the authors via t.siebers@uva.nl or info@project-awesome.nl.

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users 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).

About Dataset

The "Time-Wasters on Social Media" dataset provides a comprehensive insight into user interactions and engagement with various social media platforms. This dataset encompasses a wide range of attributes that facilitate a thorough analysis of how social media affects users' time management and productivity. It serves as an essential resource for researchers, marketers, and social scientists who seek to delve into the intricacies of social media consumption patterns.

Generated through advanced synthetic data techniques using tools like NumPy and pandas, this dataset mimics real-world social media usage scenarios. Despite being artificially created, it accurately reflects genuine usage trends, making it a valuable asset for conducting research and analysis in the realm of social media behavior.

Columns Description

• UserID: Unique identifier assigned to each user.
• Age: The user's age. - Gender: The user's gender (e.g., male, female, non-binary).
• Location: Geographic location of the user.
• Income: The user's income level.
• Debt: Amount of debt the user has.
• Owns Property: Indicates whether the user owns property.
• Profession: The user's occupation or job.
• Demographics: Statistical data about the user (e.g., age, gender, income).
• Platform: The platform the user is using (e.g., website, mobile app).
• Total Time Spent: The total time the user spends on the platform.
• Number of Sessions: The number of times the user has logged into the platform.
• Video ID: Unique identifier for a video.
• Video Category: The category or genre of the video.
• Video Length: Duration of the video.
• Engagement: User interaction with the video (e.g., likes, comments, shares).
• Importance Score: A score indicating how important the video is to the user.
• Time Spent On Video: The amount of time the user spends watching a 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 often the user engages with the platform.
• Productivity Loss: The impact of platform usage on the user's productivity.
• Satisfaction: The user's satisfaction level with the platform or content.
• Watch Reason: The reason why the user is watching a video (e.g., entertainment, education).
• Device Type: The type of device the user is using (e.g., smartphone, tablet, desktop).
• OS: The operating system of the user's device (e.g., iOS, Android, Windows).
• Watch Time: The time of day when the user watches videos.
• Self Control: The user's ability to control their usage of the platform.
• Addiction Level: The user's level of dependency on the platform.
• Current Activity: What the user is doing while watching the video.
• Connection Type: The type of internet connection the user has (e.g., Wi-Fi, cellular).

Table shows the number of times a particular edge of the network was detected (hit) or not (miss) by the smartphone app and the sociometric badges. Only time intervals when both the app and badge were active were considered.

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

Table shows the number of times a particular edge of the network was detected (hit) or not (miss) by the smartphone app and the sociometric badges. Only office hours (Mon-Fri 9am-5pm) during the 4-week period were considered.

This data set contains all gather information of the MyMovez project, which investigated adolescents’ health behaviors (ie., nutrition, media use, and physical activity) and their social networks for three years. The first year (2016; data collection waves 1, 2, 3) and the second year (2017; wave 4) marked the first phase of the project in which the health behaviors of adolescents were monitored without intervening. The third year (waves 5, 6, 7) marked the second phase of the project in which four different types of interventions were tested to promote either water consumption or physical activity. A fifth group did not receive an intervention and is used as a control condition.

During the measurement periods, participants received the MyMovez Wearable Lab: a smartphone with a tailor-made research application and a wrist-worn accelerometer. The accelerometer (Fitbit Flex) measured the physical activity per minute and per day, and was water-resistant. The smartphone was equipped with a custom made research application by which daily questionnaires were administered. Beginning in wave 5, the app contained a social platform in which the participants could communicate with each other. The smartphone also connected to the accompanying accelerometer and other research smartphones via Bluetooth.

Among others, the most important measures in the project are:

• Questionnaire data: e.g. Food Frequency Questionnaires, Self-reported media exposure, measures related to the theory of planned behavior
• Physical activity measured by accelerometer.
• Sociometric nominations: Peers nominated classmates on certain questions
• Proximity networks inferred from the Bluetooth connections on the research phones (beacon data)
• Online communication data derived from the social platform (Social Buzz)
• Photo data (not shared in this repository)
• BMI measured by the researchers

For more information please see the accompanying overview, or the protocol paper of the project: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-018-5353-5

Sources: Kaggle This dataset, created using NumPy and Pandas, mimics real-world social media usage patterns for research and analysis through synthetic data generation techniques.

Collection Methodology

About Dataset The "Time-Wasters on Social Media" dataset provides a comprehensive insight into user interactions and engagement with various social media platforms. This dataset encompasses a wide range of attributes that facilitate a thorough analysis of how social media affects users' time management and productivity. It serves as an essential resource for researchers, marketers, and social scientists who seek to delve into the intricacies of social media consumption patterns.

Generated through advanced synthetic data techniques using tools like NumPy and pandas, this dataset mimics real-world social media usage scenarios. Despite being artificially created, it accurately reflects genuine usage trends, making it a valuable asset for conducting research and analysis in the realm of social media behavior.

Columns Description UserID: Unique identifier assigned to each user. Age: The user's age. - Gender: The user's gender (e.g., male, female, non-binary). Location: Geographic location of the user. Income: The user's income level. Debt: Amount of debt the user has. Owns Property: Indicates whether the user owns property. Profession: The user's occupation or job. Demographics: Statistical data about the user (e.g., age, gender, income). Platform: The platform the user is using (e.g., website, mobile app). Total Time Spent: The total time the user spends on the platform. Number of Sessions: The number of times the user has logged into the platform. Video ID: Unique identifier for a video. Video Category: The category or genre of the video. Video Length: Duration of the video. Engagement: User interaction with the video (e.g., likes, comments, shares). Importance Score: A score indicating how important the video is to the user. Time Spent On Video: The amount of time the user spends watching a 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 often the user engages with the platform. Productivity Loss: The impact of platform usage on the user's productivity. Satisfaction: The user's satisfaction level with the platform or content. Watch Reason: The reason why the user is watching a video (e.g., entertainment, education). Device Type: The type of device the user is using (e.g., smartphone, tablet, desktop). OS: The operating system of the user's device (e.g., iOS, Android, Windows). Watch Time: The time of day when the user watches videos. Self Control: The user's ability to control their usage of the platform. Addiction Level: The user's level of dependency on the platform. Current Activity: What the user is doing while watching the video. Connection Type: The type of internet connection the user has (e.g., Wi-Fi, cellular).

The peer-reviewed paper of AWARE dataset is published in ASEW 2021, and can be accessed through: http://doi.org/10.1109/ASEW52652.2021.00049. Kindly cite this paper when using AWARE dataset.

Aspect-Based Sentiment Analysis (ABSA) aims to identify the opinion (sentiment) with respect to a specific aspect. Since there is a lack of smartphone apps reviews dataset that is annotated to support the ABSA task, we present AWARE: ABSA Warehouse of Apps REviews.

AWARE contains apps reviews from three different domains (Productivity, Social Networking, and Games), as each domain has its distinct functionalities and audience. Each sentence is annotated with three labels, as follows:

Aspect Term: a term that exists in the sentence and describes an aspect of the app that is expressed by the sentiment. A term value of “N/A” means that the term is not explicitly mentioned in the sentence.

Aspect Category: one of the pre-defined set of domain-specific categories that represent an aspect of the app (e.g., security, usability, etc.).

Sentiment: positive or negative.

Note: games domain does not contain aspect terms.

We provide a comprehensive dataset of 11323 sentences from the three domains, where each sentence is additionally annotated with a Boolean value indicating whether the sentence expresses a positive/negative opinion. In addition, we provide three separate datasets, one for each domain, containing only sentences that express opinions. The file named “AWARE_metadata.csv” contains a description of the dataset’s columns.

How AWARE can be used?

We designed AWARE such that it can be used to serve various tasks. The tasks can be, but are not limited to:

Sentiment Analysis.

Aspect Term Extraction.

Aspect Category Classification.

Aspect Sentiment Analysis.

Explicit/Implicit Aspect Term Classification.

Opinion/Not-Opinion Classification.

Furthermore, researchers can experiment with and investigate the effects of different domains on users' feedback.

The global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.

PLAYSTORE APP DOWNLOADS DATA

The smartphone revolution started less than 2 decades ago with the evolution of technologies like touch screen and advanced micro chips that could outmatch the processing capacities of even some of the Computers at that time.

Android as a handset operation system played a major part in what we call the smartphone revolution with more than 2 billion active users a month worldwide. What makes Android popular is not just its aesthetics or performance but the millions of applications or softwares that are available for the platform free of cost or paid, enterprise or consumer.

Android Application development is a demanding skill and every business has found the necessity to establish its presence in the market by having its own application made available to users.

Data Description:

Train.csv - 16516 rows x 11 columns Test.csv - 24776 rows x 10 columns Sample Submission - Acceptable submission format

Attributes Description:

Offered_By : The publisher/Organization/Company that develops the application

Category : The category/Genre of the application

Rating: The total ratings received from consumers

Reviews: The total reviews received from consumers

Size: The size of the application with unit

Price: The total price of the application or cost of the in-app purchases

Content_Rating: The content rating for the application

Last_Updated_On: The date at which the application was last updated

Release_Version: The version of the application that is currently being served

OS_Version_Required: The minimum Android OS version required to run the application

Downloads: The approximated range of downloads for the application

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

Here are a few use cases for this project:

1. Sports Analytics: Use the "Basketball Players" model to automatically track players' movements, ball possession, and referee decisions during live games or post-game analysis. This data can be used by coaches, analysts, and teams to inform and improve strategies, tactics, and player performance.

2. Real-time Game Commentary: Integrate the model into sports broadcasting platforms, providing real-time updates and statistics to commentators, allowing them to focus on in-depth analysis and storytelling while the model handles identification and stat-tracking.

3. Automated Sports Highlights: Utilize the model to automatically create highlights from basketball games by identifying key moments, such as successful shots, blocks, and referee decisions. This can streamline post-production process for sports media outlets and social media channels.

4. Training and Skill Development: Leverage the "Basketball Players" model to create feedback tools for players, identifying areas of improvement in team dynamics and individual technique during practice sessions or games.

5. Fan Experience: Employ the model in smartphone apps or AR devices, providing fans with real-time information on their favorite teams and players during live games, enhancing their overall experience and engagement.

The public use microdata file (PUMF) from the Canadian Internet Use Survey (CIUS) provides data on the adoption and use of digital technologies and the online behaviors of individuals 15 years of age and older living in the ten provinces of Canada. The survey is built off the previous iteration of the CIUS, last conducted in 2012. While there is some comparability with the 2012 CIUS, the 2018 survey was redesigned in 2018 to reflect the rapid pace at which Internet technology has evolved since the previous survey iteration. The files include information on how individuals use the Internet, smartphones, and social networking websites and apps, including their intensity of use, demand for certain online activities, and interactions through these technologies. It also provides information on the use of online government services, digital skills, online work, and security, privacy and trust as it relates to the Internet.

Ziel dieser Studie ist es, die Bereitschaft zur Teilnahme an der passiven mobilen Datenerfassung unter deutschen Smartphone-Besitzern zu messen. Die Daten stammen aus einer Webumfrage unter deutschen Smartphone-Nutzern ab 18 Jahren, die aus einem deutschen Online-Access-Panels rekrutiert wurden. Im Dezember 2016 füllten 2.623 Teilnehmer den Fragebogen Welle 1 zu den Themen Smartphone-Nutzung und -Fähigkeiten, Datenschutz und Sicherheit sowie allgemeine Einstellungen gegenüber Umfragen und Forschungseinrichtungen aus. Im Januar 2017 wurden alle Befragten von Welle 1 zur Teilnahme an einer zweiten Webumfrage eingeladen. Darin enthalten waren Vignetten, die mehrerer Dimensionen einer hypothetischen Studie mit passiver mobiler Datenerfassung variierten. Die Befragten wurden gebeten, ihre Bereitschaft zur Teilnahme an einer solchen Studie zu bewerten. Insgesamt 1.957 Teilnehmer füllten den Fragebogen Welle 2 aus. Themen: Welle 1 Besitz von Smartphone, Handy, PC, Tablet und/oder E-Book-Reader; Art des Smartphones; Häufigkeit der Nutzung des Smartphones; Smartphone-Aktivitäten (Surfen, E-Mails, Fotografieren, Anzeigen/Post von Social Media-Inhalten, Einkaufen, Online-Banking, Installieren von Apps, Verwenden von GPS-fähigen Apps, Verbinden über Bluetooth, Spielen, Streamen von Musik/Videos); Selbsteinschätzung der Smartphone-Kompetenz; Einstellung zu Umfragen und Teilnahme an wissenschaftlichen Studien (persönliches Interesse, Zeitverschwendung, Verkaufsgespräch, interessante Erfahrung, nützlich); Vertrauen in Institutionen im Hinblick auf den Schutz persönlicher Daten (Marktforschungsunternehmen, Universitätsforscher, Bundesbehörden wie das Statistische Bundesamt, Mobilfunkanbieter, App-Unternehmen, Kreditkartenunternehmen, Online-Händler und soziale Netzwerke); Bedenken hinsichtlich der Verwendung personenbezogener Daten durch die vorgenannten Institute; allgemeine Datenschutzbedenken; Gefühl von verletzter Privatsphäre durch Banken/Kreditkartenunternehmen, Finanzamt, Regierungsbehörden, Marktforschungsunternehmen, soziale Netzwerke, Apps, Internetbrowser); Besorgnis über Datensicherheit betreffend Smartphone-Aktivitäten für die Forschung (Online-Umfrage, Umfrage-App, Forschungs-App, SMS-Umfrage, Kamera, Aktivitätsdaten, GPS-Standort, Bluetooth); Anzahl der Online-Umfragen, an denen der Befragte in den letzten 30 Tagen teilgenommen hat; andere Panel-Mitgliedschaften außer der von mingle; frühere Teilnahme an einer Studie mit Herunterladen einer Forschungs-App auf das Smartphone (passive mobile Datenerfassung). Welle 2 Bereitschaft zur Teilnahme an der passiven mobilen Datenerfassung (mittels acht Vignetten mit unterschiedlichen Szenarien, die die Ausprägungen mehrerer Dimensionen einer hypothetischen Studie mit passiver mobiler Datenerfassung variierten. Die Forschungs-App erhebt die folgenden Daten für Forschungszwecke: technische Merkmale des Smartphones (z.B. Telefonmarke, Bildschirmgröße), das aktuell genutzte Telefonnetz (z.B. Signalstärke), aktueller Standort (alle 5 Minuten), welche Apps verwendet werden und welche Websites besucht werden sowie die Anzahl der ein- und ausgehenden Anrufe und SMS-Nachrichten auf dem Smartphone); Grund, warum der Befragte an der im ersten Szenario verwendeten Forschungsstudie (nicht) teilnehmen würde (offene Antwort); Wahrnehmen von Unterschieden zwischen den acht Szenarien; Art der wahrgenommenen Unterschiede (offene Antwort); erinnerte Daten, die von der Forschungs-App gesammelt werden (Recall); frühere Einladung zum Herunterladen einer Forschungs-App; Download dieser Forschungs-App. Demographie: Geschlecht; Alter; Bundesland; Schulbildung; berufliche Qualifikation; Zusätzlich kodiert wurden: laufende Nummer; Teilnehmer-ID; Dauer (Reaktionszeit in Sekunden); Gerätetyp, mit dem der Fragebogen ausgefüllt wurde; Vignettentext; Vignetteneinführungszeit; Vignettenzeit. The goal of this study is to measure willingness to participate in passive mobile data collection among German smartphone owners. The data come from a two-wave web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2016, 2,623 participants completed the Wave 1 questionnaire on smartphone use and skills, privacy and security concerns, and general attitudes towards survey research and research institutions. In January 2017, all respondents from Wave 1 were invited to participate in a second web survey which included vignettes that varied the levels of several dimensions of a hypothetical study using passive mobile data collection, and respondents were asked to rate their willingness to participate in such a study. A total of 1,957 respondents completed the Wave 2 questionnaire. Wave 1 Topics: Ownership of smartphone, mobile phone, PC, tablet, and/or e-book reader; type of smartphone; frequency of smartphone use; smartphone activities (browsing, e-mails, taking photos, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, play games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, statistical office, mobile service provider, app companies, credit card companies, online retailer, and social networks); concerns regarding the disclosure of personal data by the aforementioned institutions; general privacy concern; privacy violated by banks/ credit card companies, tax authorities, government agencies, market research companies, social networks, apps, internet browsers); concern regarding data security with smartphone activities for research (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth); number of online surveys in which the respondent has participated in the last 30 days; Panel memberships other than that of mingle; previous participation in a study with downloading a research app to the smartphone (passive mobile data collection). Wave 2 Topics: Willingness to participate in passive mobile data collection (using eight vignettes with different scenarios that varied the levels of several dimensions of a hypothetical study using passive mobile data collection.The research app collects the following data for research purposes: technical characteristics of the smartphone (e.g. phone brand, screen size), the currently used telephone network (e.g. signal strength), the current location (every 5 minutes), which apps are used and which websites are visited, number of incoming and outgoing calls and SMS messages on the smartphone); reason why the respondent wouldn´t (respectively would) participate in the research study used in the first scenario (open answer); recognition of differences between the eight scenarios; kind of recognized difference (open answer); remembered data the research app collects (recall); previous invitation for research app download; research app download. Demography: sex; age; federal state; highest level of school education; highest level of vocational qualification. Additionally coded was: running number; respondent ID; duration (response time in seconds); device type used to fill out the questionnaire; vignette text; vignette intro time; vignette time.

During a January 2024 global survey among marketers, nearly 60 percent reported plans to increase their organic use of YouTube for marketing purposes in the following 12 months. LinkedIn and Instagram followed, respectively mentioned by 57 and 56 percent of the respondents intending to use them more. According to the same survey, Facebook was the most important social media platform for marketers worldwide.