During a July 2022 survey on online dating in the United States, 56 percent of female online dating sites or app users aged 18-49 stated that they had received a sexually explicit message or image they did not ask for. In contrast, only 25 percent of male respondents of the same age group stated the same.

In 2022, mobile dating market leader Tinder was the world's most downloaded dating app, generating 64 million downloads. Mobile app Bumble, which went public in February 2021, amassed 28 million downloads, ranking as the second most popular dating app worldwide. The Badoo app ranked third with approximately 26 million downloads from global users in 2022.

According to a January 2022 analysis of the leading dating apps downloaded from the Apple App Store, the Badoo mobile app was indexed as collecting the largest number of data types from its users' activity. Bumble and HER ranked second, with an index value of close to 68, respectively. Grindr had a reported index value of 62, while market leader Tinder was indexed with a value of 38.4.

The North American online dating services market, encompassing both free and paid platforms, is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) of 5.60% from 2025 to 2033. This expansion is fueled by several key drivers. Firstly, the increasing prevalence of smartphones and readily available internet access has significantly broadened the reach and convenience of online dating. Secondly, shifting societal norms and a growing acceptance of online relationships contribute to market expansion. Thirdly, innovative features offered by dating apps, such as advanced matching algorithms, video chat capabilities, and safety measures, enhance user experience and attract a wider demographic. The market segmentation reveals a significant portion utilizing free services, but a substantial and growing revenue stream is generated from paid premium features and subscriptions. This suggests a trend towards users seeking more personalized and effective experiences, willing to pay for enhanced matching and communication tools. Competition is fierce, with established players like Match Group Inc. and eHarmony alongside newer entrants like Bumble and Hinge vying for market share. While data on specific market size values for 2025 and beyond are not provided, based on the 5.60% CAGR and estimating a 2025 market size of approximately $2 Billion (a reasonable estimate considering the market's maturity and reported growth in similar markets), a substantial expansion can be expected throughout the forecast period. Geographic concentration in North America, specifically the United States, Canada, and Mexico, reflects high internet penetration and a strong culture of embracing digital technologies for social interactions. However, potential restraints include concerns over data privacy and safety, the rise of alternative social connection methods, and the ongoing challenge of maintaining user engagement in a competitive landscape. Market players are continuously innovating to address these challenges, focusing on improved security features, enhanced user interfaces, and targeted marketing strategies to reach specific demographics. The ongoing evolution of features, coupled with effective marketing and addressing user concerns, will be crucial in determining the overall market trajectory within the North American region. This comprehensive report provides an in-depth analysis of the North America online dating services market, encompassing the historical period (2019-2024), base year (2025), and forecast period (2025-2033). It leverages extensive research to provide critical insights into market size, trends, segmentation, and key players, utilizing high-search-volume keywords such as online dating market, North America dating apps, dating app industry, online dating services revenue, and dating app market trends. The report is invaluable for investors, industry professionals, and anyone seeking to understand the dynamic landscape of the North American online dating sector. Recent developments include: March 2022 - Match Group has announced that it is launching the latest addition to its dating services lineup with Stir, an app designed exclusively for single parents. With the new release, the company aims to address the 20 million single parents in the U.S. who are under-served by existing dating apps.. Key drivers for this market are: Continuous Innovation in Service Offerings, Growing Penetration of Smartphones and Mobile Devices. Potential restraints include: Security Concerns of Data Privacy. Notable trends are: Rapid innovation in service offerings is driving the market growth.

Concerning the 11 selected segments, the segment Others has the largest value share by brand with ** percent. Contrastingly, ****** is ranked last, with * percent. Their difference, compared to Others, lies at ** percentage points. Find more statistics on other topics: a comparison of the brand shares in Europe and a comparison of the brand shares in Africa.The Statista Market Insights cover a broad range of additional markets.

In 2022, Tinder was the leading dating app in the United States, generating almost ** million downloads. Bumble ranked second, with **** million downloads from U.S. users in 2022. Match-owned dating apps Hinge and Plenty of Fish ranked third and fourth, amassing *** million and *** million downloads in the examined period, respectively.

The online dating services market is experiencing robust growth, projected to reach a substantial size driven by increasing smartphone penetration, evolving social norms around dating, and advancements in matching algorithms. The 6.90% CAGR from 2019-2033 indicates a consistent upward trajectory, fueled by a wider user base seeking romantic connections online. This growth is further accelerated by the integration of innovative features such as AI-powered matching, live video streaming, and enhanced safety measures, all contributing to a more personalized and secure user experience. Key players like Match Group (Tinder), Bumble, and others are constantly innovating to maintain their market share, leading to increased competition and market consolidation. The market segmentation likely includes variations based on demographics (age, location, preferences), subscription models (freemium, premium), and platform types (app-based, web-based). Geographic variations are also significant, with regions like North America and Europe currently dominating the market, but Asia-Pacific and other developing regions showing strong growth potential. The market faces challenges including concerns about data privacy and online safety, the need to continuously adapt to changing user preferences and technological advancements, and the potential for market saturation in certain segments. However, the ongoing expansion into new geographic markets, the integration of virtual reality and augmented reality technologies, and the growing acceptance of online dating as a legitimate way to find partners promise continued expansion for the foreseeable future. Strategic partnerships, acquisitions, and the development of niche dating apps targeting specific demographics (e.g., LGBTQ+ communities, specific religious affiliations) will likely shape the competitive landscape in the coming years. Maintaining trust and building a strong reputation for user safety will be critical for sustained growth and success within this dynamic market. Recent developments include: February 2022 - Tinder is expanding its portfolio of features by introducing blind dates as it features a popular suite of Fast Chat designed to help members connect faster through fun, innovative prompts, and games., December 2021 - Bumble has introduced a new profile design and revamped the matchmaking algorithm, where users on the dating app can have access to view a person's bio, including interests below their first picture, to get an idea about the potential match right from the beginning.. Key drivers for this market are: Increased Smartphone Penetration, As marriage agreements on matrimonial websites increase, the demand for matchmaking services.. Potential restraints include: Increased Smartphone Penetration, As marriage agreements on matrimonial websites increase, the demand for matchmaking services.. Notable trends are: Increased Smartphone Penetration has Boosted the Download of Online Dating Apps.

This dataset provides a collection of user reviews and ratings for dating applications, primarily sourced from the Google Play Store for the Indian region between 2017 and 2022. It offers valuable insights into user sentiment, evolving trends, and common feedback regarding dating apps. The data is particularly useful for practising Natural Language Processing (NLP) tasks such as sentiment analysis, topic modelling, and identifying user concerns.

Columns

• Index: A unique identifier for each review entry.
• Name: The name of the user who left the review.
• Username: The username of the reviewer.
• Review: The textual content of the review left by the user.
• Rating: The numerical rating given by the user to the app, indicating their satisfaction level.
• #ThumbsUp: A measure of how useful the review was perceived to be by other users.
• Date&Time: The specific date and time when the review was posted.
• App: The name of the dating application being reviewed.
• Label Count: A numerical label, the specific purpose of which is not detailed in the provided information, but it appears to relate to ranges of index or other numerical values within the dataset.

Distribution

The dataset is typically provided in a CSV file format. It contains a substantial number of records, estimated to be around 527,000 individual reviews. This makes it suitable for large-scale data analysis and machine learning projects. The dataset structure is tabular, with clearly defined columns for review content, metadata, and user feedback. Specific row/record counts are not exact but are indicated by the extensive range of index labels.

Usage

This dataset is ideally suited for a variety of analytical and machine learning applications: * Analysing trends in dating app usage and perception over the years. * Determining which dating applications receive more favourable responses and if this consistency has changed over time. * Identifying common issues reported by users who give low ratings (below 3/5). * Investigating the correlation between user enthusiasm and their app ratings. * Performing sentiment analysis on review texts to gauge overall user sentiment. * Developing Natural Language Processing (NLP) models for text classification, entity recognition, or summarisation. * Examining the perceived usefulness of top-rated reviews. * Understanding user behaviour and preferences across different dating apps.

Coverage

The dataset primarily covers user reviews from the Google Play Store, specifically for the Indian country region ('in'), despite being titled as "all regions" in some contexts. The data spans a time range from 2017 to 2022, offering a multi-year perspective on dating app trends and user feedback. There are no specific demographic details for the reviewers themselves beyond their reviews and ratings.

License

CCO

Who Can Use It

This dataset is suitable for: * Data Scientists and Analysts: For conducting deep dives into user sentiment, trend analysis, and predictive modelling. * NLP Practitioners and Researchers: As a practical dataset for training and evaluating natural language processing models, especially for text classification and sentiment analysis tasks. * App Developers and Product Managers: To understand user feedback, identify areas for improvement in their own or competing dating applications, and inform product development strategies. * Market Researchers: To gain insights into the consumer behaviour and preferences within the online dating market. * Students and Beginners: It is tagged as 'Beginner' friendly, making it a good resource for those new to data analysis or NLP projects.

Dataset Name Suggestions

• Google Play Dating App Reviews (India, 2017-2022)
• Indian Dating App User Reviews
• Mobile Dating App Reviews & Ratings
• Dating App Sentiment Analysis Dataset
• Google Play Dating App Feedback

Attributes

Original Data Source: Dating Apps Reviews 2017-2022 (all regions)

According to a 2022 online survey, five percent of adults in the United States were currently using online dating apps or sites. Overall, a further five percent reported they were not currently using online dating services, but had done so in the past year. Furthermore, 21 percent of U.S. adults said they had used online dating platforms more than a year ago.

Analysis of ‘Speed Dating Experiment’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/annavictoria/speed-dating-experiment on 28 January 2022.

--- Dataset description provided by original source is as follows ---

What influences love at first sight? (Or, at least, love in the first four minutes?) This dataset was compiled by Columbia Business School professors Ray Fisman and Sheena Iyengar for their paper Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment.

Data was gathered from participants in experimental speed dating events from 2002-2004. During the events, the attendees would have a four minute "first date" with every other participant of the opposite sex. At the end of their four minutes, participants were asked if they would like to see their date again. They were also asked to rate their date on six attributes: Attractiveness, Sincerity, Intelligence, Fun, Ambition, and Shared Interests.

The dataset also includes questionnaire data gathered from participants at different points in the process. These fields include: demographics, dating habits, self-perception across key attributes, beliefs on what others find valuable in a mate, and lifestyle information. See the Speed Dating Data Key document below for details.

For more analysis from Iyengar and Fisman, read Racial Preferences in Dating.

Data Exploration Ideas

• What are the least desirable attributes in a male partner? Does this differ for female partners?
• How important do people think attractiveness is in potential mate selection vs. its real impact?
• Are shared interests more important than a shared racial background?
• Can people accurately predict their own perceived value in the dating market?
• In terms of getting a second date, is it better to be someone's first speed date of the night or their last?

--- Original source retains full ownership of the source dataset ---

How Couples Meet and Stay Together (HCMST) is a study on how Americans meet their romantic partners. This is a nationally representative study of American adult respondents with no overlap in subjects from the original HCMST survey [ICPSR 30103] which was first fielded in 2009.

Metadata for samples collected in April 2022 for cosmogenic nuclide exposure dating of stone runs and bedrock in East Falkland. Metadata include sample IDs, area names, sample types, lithologies, dimensions, locations, elevations, shielding and photos.

According to a July 2022 survey on online dating in the United States, more often than men, women stated that they used such services to meet a long-term partner or spouse. Additionally, 43 percent of men and 37 percent of women reported that finding a partner to date casually was a major reason for them to use online dating platforms.

Time series Breakdown of victims Crimes as a whole according to their relationship with the suspects (from the victim's point of view) by male and female victims separately. Priority is always given to the closest relationship. Main groups are "Marriage / Partnership / Family incl. dependents", "Informal social relations", "Formal social relations in institutions, organisations and groups"

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Feature Names Relationship File (FEATNAMES.dbf) contains a record for each feature name and any attributes associated with it. Each feature name can be linked to the corresponding edges that make up that feature in the All Lines Shapefile (EDGES.shp), where applicable to the corresponding address range or ranges in the Address Ranges Relationship File (ADDR.dbf), or to both files. Although this file includes feature names for all linear features, not just road features, the primary purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute, which can be used to link to the Address Ranges Relationship File (ADDR.dbf). The linear feature is identified by the linear feature identifier (LINEARID) attribute, which can be used to relate the address range back to the name attributes of the feature in the Feature Names Relationship File or to the feature record in the Primary Roads, Primary and Secondary Roads, or All Roads Shapefiles. The edge to which a feature name applies can be determined by linking the feature name record to the All Lines Shapefile (EDGES.shp) using the permanent edge identifier (TLID) attribute. The address range identifier(s) (ARID) for a specific linear feature can be found by using the linear feature identifier (LINEARID) from the Feature Names Relationship File (FEATNAMES.dbf) through the Address Range / Feature Name Relationship File (ADDRFN.dbf).

Sub-table of Table 93. Information on other physical and/or social proximity.

Zircon grains separated from altered ash beds were analyzed to better understand the ages of beds. Isotopic analyses for U-Pb geochronology and trace element geochemistry were performed simultaneously by secondary ion mass spectrometry using the Stanford-USGS SHRIMP-RG ion microprobe housed at Stanford University, USA. Analyses followed the analytical protocol and data acquisition conditions described by Watts et al. (2016). These data are reported in this Release.

In June 2022, the date price per ton stood at $2,990 per ton in June 2022, declining by -15.5% against the previous month.

In January 2022, the mobile app for French dating service Meetic reported an increase of approximately 106 percent in downloads compared to the previous month. The mobile dating app Happn saw growth in downloads of approximately 61 percent in the same period, while dating platform OkCupid reported 43 percent growth in downloads compared to December 2021.

For English, see below As of 1 January 2023, RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home . File description: - This file contains the following numbers: (number of newly reported) positively tested individuals aged 70 and older living at home*, by safety region, per date of the positive test result. - (number of newly reported) deceased individuals aged 70 and older living at home who tested positive*, by safety region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. Reports from 01-07-2020 are regarded as individuals aged 70 and older living at home if, according to the information known to the GGD, they: • Do not live in an institution AND • Are aged 70 or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as individuals aged 70 and older living at home if they: • Can be linked to a known location of a disability care institution or nursing home on the basis of their 6-digit zip code OR • Have 'Disabled care institution' or 'Nursing home' as the location of the contamination mentioned. OR • Based on the content of free text fields, can be linked to a disability care institution or nursing home. The file is structured as follows: A set of records per date of with for each date: • A record for each security region (including 'Unknown') in the Netherlands, even if there are no reports for the relevant security region. The numbers are then 0 (zero). • Security region is unknown when a record cannot be assigned to one unique security region. A date 01-01-1900 is also included in this file for statistics whose associated date is unknown. The following describes how the variables are defined. Description of the variables: Version: Version number of the dataset. This version number is adjusted (+1) when the content of the dataset is structurally changed (so not the daily update or a correction at record level. The corresponding metadata in RIVMdata (https://data.rivm.nl) is also changed. Version 2 update (January 25, 2022): • An updated list of known nursing or care home locations and private residential care centers was received from the umbrella organization Patient Federation of the Netherlands on 03-12-2021. taken to determine whether individuals live in an institution Version 3 update (February 8, 2022) • From February 8, 2022, positive SARS-CoV-2 test results will be reported directly from CoronIT to RIVM. such as Testing for Access) and healthcare institutions (such as hospitals, nursing homes and general practitioners) that enter their positive SARS-CoV-2 test results via the Reporting Portal of GGD GHOR directly to RIVM. Reports that are part of the source and contact investigation sample and positive SARS-CoV-2 test results from healthcare institutions that are reported to the GGD via healthcare email are reported to RIVM via HPZone. From 8 February, the date of the positive test result is used and no longer the date of notification to the GGD. Version 4 update (March 24, 2022): • In version 4 of this dataset, records have been compiled according to the municipality reclassification of March 24, 2022. See description of the variable security_region_code for more information. Version 5 update (August 2, 2022): • The classification of persons aged 70 years and parents living independently has not been applied to reports that have only been received by RIVM since February 8, 2022 via an alternative reporting route. From 8 February to 1 August 2022, the number of reports from independently living persons aged 70 and parents was therefore underestimated by approximately 14%. As of August 2, 2022, this format will be retroactively updated. Version 6 update (September 1, 2022): - From September 1, 2022, the data will no longer be updated every working day, but on Tuesdays and Fridays. The data is retroactively updated on these days for the other days. - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. Date_of_report: Date and time on which the data file was created by RIVM. Date_of_statistic_reported: The date used for reporting the 70plus statistic living at home. This can be different for each reported statistic, namely: • For [Total_cases_reported] this is the date of the positive test result. • For [Total_deceased_reported] this is the date on which the patients died. Security_region_code: Security region code. The code of the security region based on the patient's place of residence. If the place of residence is not known, the safety region is based on the GGD that submitted the report, except for the Central and West Brabant and Brabant-Noord safety regions, since the GGD and safety region are not comparable for these regions. See also: https://www.cbs.nl/nl-nl/figures/detail/84721ENG?q=Veiliteiten From March 24, 2022, this file has been compiled according to the municipality classification of March 24, 2022. The municipality of Weesp has been merged into the municipality of Amsterdam . With this division, the Gooi- en Vechtstreek safety region has become smaller and the Amsterdam-Amstelland safety region larger; GGD Amsterdam has become larger and GGD Gooi- en Vechtstreek has become smaller (Municipal division on 1 January 2022 (cbs.nl). Security_region_name: Security region name. Security region name is based on the Security Region Code. See also: https://www.rijksoverheid.nl /topics/safety-regions-and-crisis-management/safety-regions Total_cases_reported: The number of new COVID-19 infected over-70s living at home reported to the GGD on [Date_of_statistic_reported].The actual number of COVID-19 infected over-70s living at home is higher than the number of reports in surveillance, because not everyone with a possible infection is tested. In addition, it is not known for every report whether this concerns a person over 70 living at home. Date_of_statistic_reported] The actual number of deceased people over 70 living at home who died of COVID-19 is higher than the number of reports in the surveillance, because not all deceased patients are tested and deaths are not legally reportable. Moreover, it is not known for every report whether this concerns a person over 70 living at home. Corrections made to reports in the OSIRIS source system can also lead to corrections in this database. In that case, numbers published by RIVM in the past may deviate from the numbers in this database. This file therefore always contains the numbers based on the most up-to-date data in the OSIRIS source system. The CSV file uses a semicolon as a separator. There are no empty lines in the file. Below are the column names and the types of values ​​in the CSV file: • Version: Consisting of a single whole number (integer). Is always filled for each row. Example: 2. • Date_of_report: Written in format YYYY-MM-DD HH:MM. Is always filled for each row. Example: 2020-10-16 10:00 AM. • Date_of_statistic_reported: Written in format YYYY-MM-DD. Is always filled for each row. Example: 2020-10-09. • Security_region_code: Consisting of 'VR' followed by two digits. Can also be empty if the region is unknown. Example: VR01. • Security_region_name: Consisting of a character string. Is always filled for each row. Example: Central and West Brabant. • Total_cases_reported: Consisting of only whole numbers (integer). Is always filled for each row. Example: 12. • Total_deceased_reported: Consisting of only whole numbers (integer). Is always filled for each row. Example: 8. ---------------------------------------------- ---------------------------------- Covid-19 statistics for persons aged 70 and older living outside an institution, by security region and date As of 1 January 2023, the RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home. File description: This file contains the following numbers: - Number of newly reported persons aged 70 and older living at home who tested positive*, by security region, by date of the positive test result. - Number of newly reported deceased persons aged 70 and older living at home who tested positive*, by security region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. For reports from 01-07-2020 persons aged 70 and older are considered to be living at home if, according to the information known to the PHS, they: • were not living in an institution AND • Are aged 70 years or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as being an persons aged 70 and older living at home if they: • Based on their 6-digit zip code, can be linked to a known location of a care institution for the disabled or a nursing home OR • Have 'Disability care institution' or 'Nursing home' as the stated location of transmission. OR • Based on the content of free text fields, links can be made to a care institution for the disabled or a nursing home. The file is structured as follows: A set of records by date, with for