Masters Thesis defense: Sonal Goel
- Who: Sonal Goel
- What: Masters thesis defense
- When: 1600 - 1730 hrs IST, April 25, 2016
- Where: Board room, Fifth floor, IIIT-Delhi
- Why: Precogs put in a lot of effort in their work, you don't want to miss seeing it.
- Title: Image Search for Improved Law and Order: Search, Analyze, Predict image spread on Twitter.
- Abstract: Social media is often used to spread images that can instigate anger among people, hurt their religious, political, caste, and other sentiments, this in return can create law and order situation in society. This results the need for law enforcement agencies to inspect the spread of images related to such events on social media in real time. To help the law enforcement agencies to analyse the image spread on microblogging websites, we developed an Open Source Real Time Image search system, where the user can give an image, and a supportive text related to image and the system ﬁnds the images that are similar to the input image and their count. The system proposed is robust to identify images that can be cropped, scaled (to a certain factor), images with text embedded, images stitched with other images, images with varied brightness, and some combination of all these. On the input text, the system runs a text mining algorithm to extract the keywords, retrieve images related to these keywords from Twitter, and use Image comparison methodology to extract similar images. The system can analyse the users who were propagating the content, the sentiments ﬂoating with them, and their retweet analysis. We found that Improved ORB (ORB + RANSAC) performs the best for image similarity and using it we are able to achieve an accuracy of above 85% in all the cases tested. The system developed is being used in one of the Government security agency. In addition to identifying the similar images, we also aim to predict the inﬂuence of such events on people as diﬀusion rate. In microblogging sites like Twitter, information provided by tweets diﬀuses over the users through retweets. Hence, to further enhance the understanding and controlling the diﬀusion of these kinds of images, we focus to predict the retweet count of such images by using visual cues from the images, content based information and structure-based features. For this, we build a random forest regression model that takes some tweet, image and structural features to predict the retweet count.