Skip to main content

Fixbook

INTRODUCTION
These days social media has become an integral part of our lives and consists of all our real-world experiences. It has become a norm to share everything we do to everywhere we go with the whole world using social media. But unknowingly many people sometimes share things which might reveal their personal information like phone numbers and locations. They don’t realize that what they are sharing might come back and bite them in the future. The only thing on their mind while posting a status is the idea of sharing their activities, because of which they don’t make an informed decision about the content. Our solution to this problem is to nudge the user when they are about to post something which might reveal their personal information like location or phone number or email address.  
Another problem that users face is finding posts and comments with undesirable content in their feed. Our solution to this is to hide such posts and comments.

METHODOLOGY
Nudge
We created a chrome extension to solve this problem which works for Facebook.
When the user starts typing a Facebook post the chrome extension issues a REST API server call with the status as the input, then the server processes it and a reply is sent stating whether the post contains any private information like location, phone number or email address. If it does, then the extension shows a nudge to the user telling them what kind of information they are sharing. After this, it's the user’s choice whether to ignore the nudge or edit the post.




Working of REST server
The server gets a string as input and then it uses a regex to check for phone numbers and email addresses in the string. It uses NLTK to check for a location in the string. While checking for the location it checks for the context as well for example ‘Delhi is an awesome place’ won’t fire the nudge but ‘moving to Delhi’ will.
A REST server was used because NLTK does not support Javascript and also so that all the computation is handled by the server and not the host. This would also make the whole process real-time.
 



Hiding Content
It takes a comma-separated list of words from the user that they deem undesirable. Whenever a new comment or post is loaded, using these keywords the extension checks if it contains one of the words if it does then that post or comment is hidden from that user’s timeline.



CONCLUSION
We successfully built a chrome extension with the desired functionalities. On posting a status with personal information, the extension nudged every time. However, we faced a few limitations. Due to the shortcomings of the NLTK library, the extension could not always accurately detect when the user is referring to a location.


THE TEAM



Group 3: [L-R] - Pahal Krishnia, Ishbir Walia, Aayushi Malik, Gargi Gupta, Varnit Jain


REFERENCES
1. http://www.django-rest-framework.org/
2. http://www.nltk.org/
3. https://developer.chrome.com/extensions/getstarted
4. https://docs.python.org/2/library/re.html
5. https://firebase.google.com

Comments

Popular posts from this blog

White or Blue, the Whale gets its Vengeance: A Social Media Analysis of the Blue Whale Challenge

The Blue Whale Challenge - a set of tasks that must be completed in a duration of 50 days - is an online social media rage. The tasks of the “game” cause both physical and mental harm to the players; the final task is to take his/her own life. The tasks include waking up at odd hours, listening to psychedelic music, watching scary videos, inflicting cuts and wounds on their bodies and the final task is to commit suicide. The game is supposedly administered by people called “curators” who incite others to take the challenge, brainwash them to cause self harm and ultimately commit suicide. Most conversations between curators and players are suspected to take place via direct message but, in order to find curators, the players need a public platform where they can express their desire to play the game - knowingly or unknowingly. Online social media serves as this platform as people post about not just their desire to be a part of the game but also details and pictures of the various task…

Social Bot Detection on Twitch

Twitch is the leading world live streaming video platform for the Gamer’s community. It is a very famous networking site and has close to 100 million monthly unique users. Bots are very prominent on the network due to various financial favors that the gaming platform provides to a user. The main objective of our Project is Detecting Social Bots on Twitch using various techniques such as Meta-data Analysis, Sentiment analysis from Chats on a Channel, and classification using Machine learning.
We started by collecting usernames of 510 channels for which we compared chatters and viewers on that channels live video. We got 51 channels which had chatters>viewers. On those channels, we did Temporal analysis for over a period of 4 weeks. Alongside, we collected their metadata, such as, Follower, Followings, Status, Partner, and total views. We calculated a Score using these features, from which we could conclude that higher the score, higher the probability of an account being a Bot accoun…

Privacy Concerns on Tinder

Introduction
Mobile dating apps have become a popular means to meet potential partners. Mobile dating application such as Tinder have exploded in popularity in recent years. Most users on Tinder use/have used Facebook as their primary way to sign up. By doing this, Tinder automatically takes user information directly from Facebook, thus saving the need to authenticate the user and user details.  In this project we aim to identify a Tinder profile on Facebook using the information that tinder obtains from Facebook. Below is the information that Tinder takes from a user when they log in for the first time.