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You are Here (and we know it!)

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What if you get to know that someone in Kimberly, Australia, knows where you went biking last Sunday? If this doesn’t creep you enough, what if the person also knows that you ran across the pavement in front of your house yesterday?  If you don’t come under the Unconcerned category of Westin’s  privacy segmentation , you must have guessed that we are hinting at the scary truth of the current day’s location privacy. With ubiquitous apps like Google(search), Facebook, Uber keeping records of your movements almost every second(with or without your permission), its hard to imagine how much location-based information do services like Google Maps, Foursquare, Strava posses. What our project is about? If these apps are collecting location-based data, then what do we, as students, got to do about it? Well, in this project, we didn’t do  any rocket science , we only took  publicly  available  routes  uploaded by a user and predicted a tiny detail of the
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#GeneralElections2019

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Problem Statement The course Privacy and Security on Online Social Media has taught us about the growing importance of social media in approximating to a good degree the public sentiments over any trending issues currently. Social media can even be thought as a virtual simulation of the real world itself to a certain extent. Our focus was to tackle one such challenge: General Elections 2019. We wanted to analyse the change in sentiments of people who tweeted about elections in 2014 versus today. We planned on using the tweet text features, along with the account features to build a classifier to classify new tweets into classes (political leanings), and map these results to obtain some sort of opinion polls before the elections. Dataset The dataset used for our experiments were " Twitter and Polls: Analyzing and estimating political orientation of Twitter users in India General Elections 2014 ". Preliminary Analysis After some preliminary tests on
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TNT : Troll or Not Troll

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Problem statement The problem we try to address in this project is to classify a tweet as a either a Troll or an Organic Tweet .  So let's get started by defining what we define both of them. Troll We define troll as a opinion manipulating tweet that is posted by a malicious user for the sole intention of swinging the election results in favor or against a particular party. Organic Tweet?   Any tweet that is not a troll is a genuine / generic of organic tweet.  Motivation There are many popular cases where people are paid to make a post on public forums in order to change opinions of people in favour or against a particular party. One such popular example is Russian Troll Factory . In this case many people were paid to tweet on the subject of US Presidential Election 2016 which were about to happen at that time. We wanted to see if we can classify if a particular tweet as a paid opinion manipulating tweet or not. Data Collection
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