https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13364933%2F23694fae55e2e76299358693ba6f32b9%2Flv-share.jpg?generation=1684843825246772&alt=media" alt=""> ➡️ There are total 3 datasets containing valuable information. ➡️ Understand people's fame and behavior's on a dating app platform. | Column Name | Description | |---------------------|------------------------------| | Age | The age of the user. | | Number of Users | The total number of users. | | Percent Want Chats | Percentage of users who want chats. | | Percent Want Friends| Percentage of users who want friendships. | | Percent Want Dates | Percentage of users who want romantic dates. | | Mean Kisses Received| Average number of kisses received by users. | | Mean Visits Received| Average number of profile visits received by users. | | Mean Followers | Average number of followers for each user. | | Mean Languages Known| Average number of languages known by users. | | Total Want Chats | Total count of users interested in chats. | | Total Want Friends | Total count of users looking for friendships. | | Total Want Dates | Total count of users seeking romantic dates. | | Total Kisses Received| Overall count of kisses received by users. | | Total Visits Received| Overall count of profile visits received by users. | | Total Followers | Overall count of followers for all users. | | Total Languages Spoken| Total count of languages spoken by all users. |

SUMMARY

When Dating apps like Tinder were becoming viral, people wanted to have the best profile in order to get more matches and more potential encounters. Unlike other previous dating platforms, those new ones emphasized on the mutuality of attraction before allowing any two people to get in touch and chat. This made it all the more important to create the best profile in order to get the best first impression.

Parallel to that, we Humans have always been in awe before charismatic and inspiring people. The more charismatic people tend to be followed and listened to by more people. Through their metrics such as the number of friends/followers, social networks give some ways of "measuring" the potential charisma of some people.

In regard to all that, one can then think:

what makes a great user profile ? how to make the best first impression in order to get more matches (and ultimately find love, or new friendships) ? what makes a person charismatic ? how do charismatic people present themselves ? In order to try and understand those different social questions, I decided to create a dataset of user profile informations using the social network Lovoo when it came out. By using different methodologies, I was able to gather user profile data, as well as some usually unavailable metrics (such as the number of profile visits).

Content

The dataset contains user profile infos of users of the website Lovoo.

The dataset was gathered during spring 2015 (april, may). At that time, Lovoo was expanding in european countries (among others), while Tinder was trending both in America and in Europe. At that time the iOS version of the Lovoo app was in version 3.

Accessory image data The dataset references pictures (field pictureId) of user profiles. These pictures are also available for a fraction of users but have not been uploaded and should be asked separately.

The idea when gathering the profile pictures was to determine whether some correlations could be identified between a profile picture and the reputation or success of a given profile. Since first impression matters, a sound hypothesis to make is that the profile picture might have a great influence on the number of profile visits, matches and so on. Do not forget that only a fraction of a user's profile is seen when browsing through a list of users.

https://s1.dmcdn.net/v/BnWkG1M7WuJDq2PKP/x480

Details about collection methodology In order to gather the data, I developed a set of tools that would save the data while browsing through profiles and doing searches. Because of this approach (and the constraints that forced me to develop this approach) I could only gather user profiles that were recommended by Lovoo's algorithm for 2 profiles I created for this purpose occasion (male, open to friends & chats & dates). That is why there are only female users in the dataset. Another work could be done to fetch similar data for both genders or other age ranges.

Regarding the number of user profiles It turned out that the recommendation algorithm always seemed to output the same set of user profiles. This meant Lovoo's algorithm was probably heavily relying on settings like location (to recommend more people nearby than people in different places or countries) and maybe cookies. This diminished the number of different user profiles that would be pr...

This dataset consists of 8892 AI-generated profile pictures downloaded from (https://www.kaggle.com/datasets/pablobedolla/this-person-does-not-exist-data)

Context

A collection of 6.6k+ images useful for multiple use cases such image identifiers, classifier algorithms etc.

The pictures are from Human faces fataset but align, crop and segment with the algorith of minivision

https://raw.githubusercontent.com/minivision-ai/photo2cartoon/master/images/data_process.jpg" alt="operation of align/crop/segment">

Content

A mix of front face, side profile pictures which will help achieve great identifying results and improved range of classifier possibilities.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

About Dataset This dataset contains real and fake images of human faces. Real and Fake Face Detection Fake Face Photos by Photoshop Experts Introduction When using social networks, have you ever encountered a 'fake identity'? Anyone can create a fake profile image using image editing tools, or even using deep learning based generators. If you are interested in making the world wide web a better place by recognizing such fake faces, you should check this dataset.

Kindly refer to my paper for more information. Please cite my work if you use my dataset in any work : K. R. Purba, D. Asirvatham and R. K. Murugesan, "Classification of instagram fake users using supervised machine learning algorithms," International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 3, pp. 2763-2772, 2020.

The dataset was collected using web scraping from third-party Instagram websites, to capture their metadata and up to 12 latest media posts from each user. The collection process was executed from September 1st, 2019, until September 20th, 2019. The dataset contains authentic users and fake users, which were filtered using human annotators. The authentic users were taken from followers of 24 private university pages (8 Indonesian, 8 Malaysian, 8 Australian) on Instagram. To reduce the number of users, they are picked using proportional random sampling based on their source university. All private users were removed, which is a total of 31,335 out of 63,795 users (49.11%). The final number of public users used in this research was 32,460 users.

Var name | Feature name | Description pos | Num posts | Number of total posts that the user has ever posted. flg | Num following | Number of following flr | Num followers | Number of followers bl | Biography length | Length (number of characters) of the user's biography pic | Picture availability | Value 0 if the user has no profile picture, or 1 if has lin | Link availability | Value 0 if the user has no external URL, or 1 if has cl | Average caption length | The average number of character of captions in media cz | Caption zero | Percentage (0.0 to 1.0) of captions that has almost zero (<=3) length ni | Non image percentage | Percentage (0.0 to 1.0) of non-image media. There are three types of media on an Instagram post, i.e. image, video, carousel erl | Engagement rate (Like) | Engagement rate (ER) is commonly defined as (num likes) divide by (num media) divide by (num followers) erc | Engagement rate (Comm.) | Similar to ER like, but it is for comments lt | Location tag percentage | Percentage (0.0 to 1.0) of posts tagged with location hc | Average hashtag count | Average number of hashtags used in a post pr | Promotional keywords | Average use of promotional keywords in hashtag, i.e. {regrann, contest, repost, giveaway, mention, share, give away, quiz} fo | Followers keywords | Average use of followers hunter keywords in hashtag, i.e. {follow, like, folback, follback, f4f} cs | Cosine similarity | Average cosine similarity of between all pair of two posts a user has pi | Post interval | Average interval between posts (in hours)

Output : 2-class User classes : r (real/authentic user), f (fake user / bought followers) 4-class User classes : r (authentic/real user), a (active fake user), i (inactive fake user), s (spammer fake user) Note that the 3 fake user classes (a, i, s) were judged by human annotators.

Inspiration

This dataset was generated to provide a benchmark set of images to test colorimetric algorithms against. We hope that it will be useful in the future to progress the ability of the scientific communities to develop algorithms/methodologies that can reduce the density of colour data contained in complex images like fruit in a unbias, reliable, and rapid manner.

Context

Contained within a a set of standard images that can be used in future studies to benchmark algorithms that undertake colorimetric analysis. These images consist of cross sectional fruit and tubers that contain complex colour gradients across the visible spectrum. These images were analyzed in our publication "A data driven approach to assess complex colour profiles in plant tissues"

A selection of 28 species of fruit and tubers was purchased from a local supermarket in Auckland, New Zealand. These fruit and tubers represented different families including Anacardiaceae (mango), Ebenaceae (persimmon), Actinidiaceae (kiwifruit), Lauraceae (avocado), Musaceae (banana), Rosaceae (apple, peach, pear, plum, and strawberry), Rutaceae (grapefruit, lemon, mandarin, and orange), and Solanaceae (potato, tamarillo, and tomato). Each fruit was cross sectioned along its most symmetrical side. Up to three cross sections of the same fruit type were placed face down on the scanner on a predefined 3x1 grid with defined positions to allow image capture of the individual fruit.

The images contained here were captured using a Canon LIDE 220 flatbed scanner (Scanning element sensor: CIS, Light source: 3 colour RGB LED) that was placed in a 2mm black perspex box with a retractable lid that completely blocked ambient light. Parent images with dimensions of 4960 pixels (W) and 7015 pixels (H) was acquired at a resolution of 600 dots-per-inch/pixels-per-inch (DPI/PPI) and output in a TIFF format. Each sibling image was calibrated using the white tile standard on the X-Rite mini-colour checker card that was included in each scanned parent image ​(McCamy, C. S. et al., 1976) and segmented.

Mccamy, C.S., Marcus, H., and Davidson, J.G. (1976). A Color Rendition Chart. Journal of Applied Photographic Engineering 11, 95-99.

Content

• This folder contains 81 fruit images in TIF format.
• Each TIF image contains two stacks. Stack-1 is a RGB image of the cross-section and Stack-2 contains a binary image the defines the of the fruit/tuber boundary.
• A xlsx containing the calibration of each colour channel (RGB) is also included

Acknowledgements

Peter A. McAtee1*, Simona Nardozza1, Annette Richardson2, Mark Wohlers1, Robert J. Schaffer3,4

1. The New Zealand Institute for Plant & Food Research (PFR), Private Bag 92169, Auckland 1142, New Zealand
2. The New Zealand Institute for Plant & Food Research (PFR) , 121 Keri Downs Road, Kerikeri, 0294, New Zealand
3. The New Zealand Institute for Plant & Food Research (PFR), 55 Old Mill Lane, Motueka, 7198, New Zealand
4. School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand